Anna Mleczko – Blog – Future Processing https://www.future-processing.com/blog Tue, 31 Mar 2026 11:33:08 +0000 en-US hourly 1 https://wordpress.org/?v=6.8.3 https://www.future-processing.com/blog/wp-content/uploads/2020/02/cropped-cropped-fp-sygnet-nobg-32x32.png Anna Mleczko – Blog – Future Processing https://www.future-processing.com/blog 32 32 The true cost of doing nothing: what media organisations stand to lose without cyber resilience https://www.future-processing.com/blog/what-media-organisations-stand-to-lose-without-cyber-resilience/ https://www.future-processing.com/blog/what-media-organisations-stand-to-lose-without-cyber-resilience/#respond Tue, 31 Mar 2026 10:57:36 +0000 https://stage-fp.webenv.pl/blog/?p=35904
Home Blog The true cost of doing nothing: what media organisations stand to lose without cyber resilience
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The true cost of doing nothing: what media organisations stand to lose without cyber resilience

The cost of a cyber attack rarely ends with fines and ransom payments. Without cyber resilience, downtime, reputational damage, and lost contracts multiply the real impact.
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According to the Cost of a Data Breach report for 2025, the average global cost of a data breach stands at $4.44 million. Additionally, unplanned downtime can cost far more than broad cross-industry averages suggest.

According to New Relic’s State of Observability for Media and Entertainment 2025, the median cost of a high-business-impact outage in the media sector is $2 million per hour, or roughly $33,000 per minute, underlining how quickly even short disruptions can translate into major financial losses. Yet even these figures capture only a fraction of the true financial impact organisations face after a cyber incident.

Cyber attacks are often described in terms of visible losses: ransom payments, regulatory fines, legal settlements. These are the numbers that appear in headlines and board reports, but the truth is that they represent only the tip of the iceberg.

Below the surface sits a much larger body of costs. Without cyber resilience, these hidden impacts compound over time, turning a single cyber incident into a prolonged business crisis.

Key takeaways

  • Cyber resilience reduces the total cost of a cyber attack, not just the likelihood of one.
  • In the media sector, cyber incidents can immediately disrupt broadcasting, streaming, or publishing schedules, leading to direct revenue loss.
  • The visible cost of a data breach represents only a fraction of the total financial impact, with downtime and reputational damage often driving the largest losses.
  • The average breach lifecycle of 241 days allows cyber threats to expand before detection, increasing operational disruption and recovery costs.
  • For media organisations, protecting audience trust and maintaining uninterrupted content delivery is central to limiting the long-term impact of a cyber incident.

Visible and hidden costs: the full financial impact

When organisations assess the cost of a cyber attack, they typically focus on direct and measurable expenses such as ransom payments, forensic investigations, legal services, regulatory fines, customer notifications, and the cost of rebuilding compromised systems. These visible costs are tangible, relatively easy to quantify, and usually reported to boards and insurers.

In the media sector, however, the financial impact rarely stops there. Disruptions to broadcasting, streaming platforms, or production workflows translate directly into lost advertising revenue and missed distribution commitments. Because media organisations operate on strict publishing and broadcast schedules, even short periods of downtime can lead to cancelled campaigns, contractual penalties, and revenue loss.

The longer-term consequences often prove even more costly. Advertisers may pause campaigns, distribution partners reconsider agreements, and audiences migrate to alternative platforms when services become unavailable. This loss of confidence can weaken viewer and subscriber loyalty and reduce long-term audience value. At the same time, reputational damage, rising cyber insurance premiums, and increased scrutiny from investors or regulators add further financial pressure.

Taken together, these visible and hidden effects illustrate a broader reality: for media organisations, the true cost of a cyber incident extends far beyond the initial technical recovery.

cyber resilience definition future processing

The compounding timeline of a breach

Another critical factor influencing the cost of a cyber incident is time. Organisations lacking cyber resilience measures typically discover incidents later and require longer recovery cycles.

The average breach lifecycle, from initial intrusion to containment, now stands at 241 days. This means attackers can remain inside an organisation’s systems for months before detection.

During this dwell time, attackers move laterally across networks, escalate privileges, and extract increasing volumes of data. By the time the incident becomes visible, the scope of compromise is significantly larger.

In the media sector, the consequences of this prolonged dwell time can be particularly severe. Attackers may gain early access to production, content management, or broadcasting systems. When the breach eventually surfaces, media organisations may face halted broadcasts, delayed publishing schedules, and the potential exposure of unreleased or sensitive content, amplifying both financial and reputational damage.

Head to a post about Cyber Resilience Act and learn about its aims, key components, reasons why it is crucial for every software development company to plan the actions regarding CRA, and more.

The cost gap: resilience vs no resilience

Traditional cybersecurity focuses primarily on preventing attacks, but while prevention remains essential, it is no longer sufficient on its own.

Evidence shows that organisations investing in cyber resilience are better prepared to limit the financial and operational impact of cyber incidents, yet adoption remains low.

According to PwC’s Global Digital Trust Insights 2025, only 2% of organisations have implemented cyber resilience across their entire organisation, despite rising threat levels. At the same time, 77% of companies expect their cybersecurity budgets to increase, and 67% of security leaders report that generative AI has already expanded their attack surface. In broadcasting, AI-driven automation means attackers can map vulnerabilities in your CDN and CMS faster than ever, turning targeted attacks into mass-scale automated threats.

For companies in the technology, media and telecommunications (TMT) sector, this gap between risk and preparedness is particularly significant. KPMG’s Cybersecurity Considerations 2025: Technology, Media & Telecommunications highlights that as media companies increasingly rely on digital distribution platforms, connected devices, and AI-driven services, cybersecurity failures can directly threaten revenue, reputation, and audience trust. The report also notes that TMT organisations face increasingly sophisticated threats such as ransomware and AI-powered attacks and complex supply chains, making real-time threat detection and resilient infrastructure essential to maintaining secure and uninterrupted digital services.

These findings point to a broader conclusion: cyber resilience should be treated as a financial risk management decision rather than purely a technical upgrade. Organisations that strengthen their ability to detect threats early and respond effectively are better prepared to contain incidents and limit the scale of disruption, which translates directly into financial outcomes.

The Benefits of Cyber Resilience Future Processing

Preparing for the inevitable: building a cyber resilience strategy

When organisations lack a clearly defined cyber resilience strategy and a cyber incident response plan, the first hours after an attack often become disorganised. Decision-making slows, communication between technical teams and leadership becomes inconsistent, and critical actions such as containment or system isolation are delayed, extending downtime and increasing financial impact.

Because cyber incidents are a matter of when rather than if, preparation is essential. Building true resilience requires more than a compliance security audit. You need an engineering partner who understands your code, your cloud dependencies and can stress-test your response through executive tabletop exercises. By defining responsibilities, strengthening detection capabilities, and preparing recovery procedures in advance, organisations can respond faster and reduce disruption to critical business operations.

What to do to start building cyber resilience in the media sector:

  • Identify critical media assets – map the systems that keep content on air, such as broadcast platforms, CMS, and streaming infrastructure, and understand the business impact of their disruption.
  • Design secure and segmented architecture – separate production environments from corporate systems to prevent attacks from spreading across the organisation.
  • Implement continuous monitoring – detect anomalies early through targeted monitoring of media workflows and audience-facing platforms.
  • Prepare structured incident response – establish clear runbooks and test them with your Board in simulated tabletop exercises, so teams can respond quickly under pressure.
  • Ensure resilient recovery capabilities – use redundant environments and secure backups to restore services quickly and maintain uninterrupted content delivery.
  • Maintain resilience continuously – strengthen defences through ongoing vulnerability management, patching, and oversight of third-party risks.

The most expensive strategy is inaction

Treating cyber risk as a distant possibility may appear harmless in the short term. In reality, without strong resilience measures, content pipelines, production environments, and distribution infrastructure remain exposed to disruption at exactly the points where media businesses generate value.

The real question for media companies is not whether cyber resilience is necessary, but how prepared they are to maintain uninterrupted content delivery when an incident occurs.

At Future Processing, we work with media organisations to strengthen that resilience. Through our work with broadcasters, streaming providers, and digital media platforms, we often see how cyber resilience challenges play out in real production and distribution environments.

If you would like to explore how these risks might affect your organisation, we are always open to a conversation. The goal is simple: ensure that when cyber threats emerge, organisations can respond quickly, protect critical services, and keep content flowing to audiences.

Stop guessing. Test it under real broadcast pressure.

Through our Cyber Resilience Accelerator, we are offering a limited "Client Zero" program for UK media organisations. Get a hands-on Media Crash Test, including a boardroom tabletop exercise and live remediation of your critical vulnerabilities.

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Cyber resilience in media: from strategy to execution https://www.future-processing.com/blog/cyber-resilience-media-strategy-execution/ https://www.future-processing.com/blog/cyber-resilience-media-strategy-execution/#respond Tue, 03 Mar 2026 10:16:27 +0000 https://stage-fp.webenv.pl/blog/?p=35717
Home Blog Cyber resilience in media: from strategy to execution
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Cyber resilience in media: from strategy to execution

The cost of a cyber attack rarely ends with fines and ransom payments. Without cyber resilience, downtime, reputational damage, and lost contracts multiply the real impact.
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A single ransomware attack on a major US TV network caused $74 million in total damage. Even after the cyber insurance payout, the company was left with $24 million in losses it couldn’t recover.

Would your organisation be able to absorb a hit like that?

Cyber resilience: what it is and why it matters

cyber resilience definition future processing

Cyber resilience encompasses a set of proactive cybersecurity strategies, practices, and technologies aimed at minimising the impact of adverse cyber events and ensuring business continuity in the face of disruptions.

Today, security incidents are a major concern for organisations of all sizes and across all industries. This is true for the media sector as well.

These threats come in various forms, such as data breaches, ransomware attacks, network outages, or even natural disasters that affect digital infrastructure. The consequences of these incidents can be severe, leading to financial losses, reputational damage, legal liabilities, and compromised customer data.

In the media sector, the risk landscape goes further. Content platforms themselves are targets. Attackers may attempt to publish false news, manipulate headlines or alter content on homepages and social channels. A fabricated story appearing on the front page of a major broadcaster would not only trigger an immediate PR crisis, but could also be used as a powerful tool for political or market manipulation.

There is also the issue of protecting sensitive information related to sources, investigative journalists, correspondents, and employees operating in high-risk environments. For media organisations, cyber resilience is therefore closely tied to safeguarding not just systems and revenue, but also editorial integrity and the personal safety of their people.

These are the reasons why cybersecurity services and cyber resilience really matter. They ensure business continuity allowing organisations to continue their operations and deliver critical services even in the face of a cyber attack or disruption. They ensure protection of sensitive data and critical infrastructure, enhance the organisation’s reputation and trust and help meeting regulatory requirements. They also give a great advantage when it comes to cost savings and adaption to changing threat landscape.

Cyber Resilience Act – an EU legal framework

Adopted in 2024, the Cyber Resilience Act (CRA) introduces mandatory cybersecurity requirements for digital products sold within the EU. It sets out clear obligations for manufacturers and software providers to design, develop, and maintain products that are secure by default and by design.

Crucially, the regulation covers the entire product lifecycle, from initial development and conformity assessment through to vulnerability handling and post-market monitoring, with the aim of improving the overall security and reliability of IT solutions across the European market.

For UK media organisations, this is particularly relevant when working with EU partners, distributors or technology providers. Digital platforms, content management systems, and broadcast technologies often operate across borders, meaning security and compliance expectations do not stop at the UK boundary.

Stronger product-level security reduces the risk of IP theft, service disruption and data breaches that could affect editorial credibility, audience trust and commercial relationships with EU-based businesses.

Head to a post about Cyber Resilience Act and learn about its aims, key components, reasons why it is crucial for every software development company to plan the actions regarding CRA, and more.

Cyber threats and the key components of a cyber resilience strategy

Cyber threats are malicious activities or attacks that exploit vulnerabilities in computer systems, network or digital infrastructure. They can have a wide range of objectives, including unauthorised access to sensitive information, disruption of services and business operations, financial gain, or sabotage.

To counter these threats effectively, media organisations need a cyber resilience strategy that links technology decisions directly to business impact.

The core components should include:

Business impact and threat mapping

Resilience starts with understanding what is truly at stake. This means identifying critical assets such as live broadcast systems, content management platforms, and streaming infrastructure, calculating the cost of a single minute of “dead air”, and mapping Single Points of Failure that could trigger disruption.

Media-grade architecture and segmentation

Network design must prevent lateral movement. A ransomware attack affecting finance or HR systems should not compromise production environments or content delivery networks. Proper segmentation and secure architecture limit blast radius and protect on-air operations.

Early warning systems

Continuous, targeted monitoring enables rapid detection of anomalies before they escalate into operational crises. A tailored, SOC-lite approach focused on media workflows helps identify unusual activity in real time, reducing the risk of public-facing failures.

Executive war room and playbooks

When incidents occur, response must be structured. Pre-tested runbooks, clear decision-making paths, and defined communication protocols ensure that both Board and IT teams act quickly and consistently, particularly during high-pressure live scenarios.

Zero-downtime recovery

Business continuity and disaster recovery plans should guarantee that if primary systems are compromised, secure fallback infrastructure such as immutable backups and redundant environments keeps content flowing to audiences.

Continuous cyber resilience

Resilience is not a one-off audit. It requires ongoing vulnerability management, patching, third-party risk oversight and adaptation to emerging attack vectors targeting the broadcasting and media sector. Governance and leadership support remain critical to sustaining this long-term approach.

What may happen if you are not cyber resilient enough?

In the media sector, a cyber incident is rarely confined to the IT department. It quickly becomes a public, operational, and financial crisis.

In 2021, a ransomware attack on Sinclair Broadcast Group disrupted live transmissions across multiple US stations. News programmes could not air, advertising slots were lost and operations were severely affected. The incident demonstrated how quickly a technical breach can translate into sustained revenue loss and reputational impact. The attack cost Sinclair $63 million in lost advertising revenue, with a further $11 million spent on mitigation and recovery, resulting in $24 million in net losses beyond its insurance coverage.

That same year, Australia’s Channel 9 was forced off air following a cyber attack that paralysed its systems. Live news bulletins from Sydney were cancelled, production workflows were interrupted, and broadcasting capability was significantly reduced. A single breach was enough to halt a national broadcaster’s core operations.

The risks extend beyond traditional broadcasters. The Guardian experienced a ransomware attack in 2022 that disrupted internal systems and affected staff access to critical tools. Even when publishing continues, the operational strain, recovery costs and reputational scrutiny are considerable.

Outside media, incidents at organisations such as Marks & Spencer and Jaguar Land Rover illustrate the broader pattern: cyber attacks lead to operational shutdowns, supply chain disruption and prolonged recovery efforts. For media companies, the equivalent impact may include leaked pre-release content, compromised subscriber data, missed publishing windows, or cancelled live events.

Without cyber resilience, the consequences are not limited to data loss. They include dead air during prime time, breached editorial systems, public loss of trust and escalating financial damage. In a sector where visibility is high and credibility is core to the business model, the absence of resilience can quickly become front-page news.

The benefits of cyber resilience for the media industry

The Benefits of Cyber Resilience Future Processing

Cyber resilience delivers measurable business value, particularly in sectors where digital assets are core to operations.

Minimised financial losses linked to attacks

Cyber attacks generate costs that extend well beyond the initial breach. Incident response, forensic investigations, legal advice, regulatory fines, and operational downtime can significantly affect revenue.

A mature cyber resilience approach reduces the scale and duration of disruption, helping organisations limit financial exposure.

In the media sector, where outages can interrupt live broadcasts or streaming services, every hour of downtime directly translates into lost advertising revenue and contractual penalties.

Enhanced business continuity

Cyber resilience enables organisations to maintain essential operations even during an incident. With tested disaster recovery plans and clearly defined escalation paths, critical services can continue while threats are contained.

For media companies, this may mean keeping publishing platforms, broadcast infrastructure, or subscription services operational despite ongoing security challenges.

Protection of reputation and trust

A cyber attack can severely damage an organisation’s reputation and erode customer confidence. In media, breaches often become headline news themselves, amplifying public scrutiny.

Protecting subscriber data, internal communications, and editorial systems is therefore not only a technical priority but a business imperative tied directly to audience trust and brand credibility.

Compliance with regulations

An increasing number of industries are subject to strict data protection and cybersecurity regulations, including the Cyber Resilience Act and data privacy frameworks.

Implementing a cyber resilience strategy supports compliance by embedding security controls throughout systems and processes. For media organisations operating across borders, this structured approach helps manage regulatory complexity while protecting user data.

Safeguarding intellectual property

Cyber attacks targeting intellectual property can have serious commercial consequences. In the media sector, stolen scripts, leaked footage, or compromised investigative materials can undermine exclusivity and competitive advantage.

Cyber resilience measures reduce the risk of unauthorised access or manipulation, ensuring that valuable content assets remain protected.

Improved incident response and recovery

Well-defined processes, clear roles and regular testing of response plans allow organisations to react quickly and effectively to cyber incidents. Faster containment limits operational disruption and accelerates system restoration.

In time-sensitive media environments, this responsiveness can prevent missed publication deadlines or cancelled live events.

Proactive risk management system

By identifying and assessing vulnerabilities across systems and supply chains, organisations can mitigate risks before they are exploited.

This proactive stance is particularly important in media, where content passes through multiple production, post-production and distribution partners, increasing exposure to third-party risks.

Stronger suppliers’ and customers’ relationships

Organisations that prioritise cybersecurity demonstrate responsibility in handling shared data and digital assets.

In media ecosystems that rely on collaboration between studios, agencies, technology providers and distributors, strong cyber resilience builds trust and supports long-term partnerships.

Competitive advantage

A strong cyber resilience posture enhances credibility with customers, partners and investors. In media markets where brand perception and reliability influence subscriber growth and advertising deals, demonstrable security maturity can become a differentiating factor.

Long-term savings

Although investing in cyber resilience requires upfront resources, it reduces the likelihood and severity of future incidents.

Avoiding repeated crises, extended downtime and reputational recovery costs leads to more stable financial performance over time, particularly in a sector where visibility and public trust are central to success.

Is my organisation cyber resilient?

For media organisations, cyber resilience goes beyond protecting IT systems. It directly affects content delivery, audience trust, and commercial stability.

Use the questions below as a short self-assessment checklist:

  • Do we have a clear inventory of our most critical assets, including editorial systems, content archives, broadcast infrastructure, streaming platforms and subscriber databases?
  • Can we quantify the financial and reputational impact of a 24-hour disruption to live broadcasts, publishing platforms or on-demand services?
  • Do we regularly test incident response scenarios that reflect media-specific threats, such as content leaks, ransomware during live production or compromise of internal communications?
  • Are our production and post-production partners subject to defined cybersecurity requirements and third-party risk assessments?
  • Do we have a documented, board-approved cyber resilience strategy aligned with regulatory obligations, including the Cyber Resilience Act where applicable?
  • Can we restore critical systems and content repositories within defined RTO and RPO targets, and have these targets been validated through testing?

Any “no” or uncertain answer should be treated as a strategic risk. In the media sector, cyber incidents rarely remain internal issues, but they quickly become public events with operational, financial, and reputational consequences.

Identify potential risks and vulnerabilities in your systems to protect your organisation from all angles.

FAQ

Is cyber resilience the same as cybersecurity?

No. Cybersecurity focuses primarily on prevention and protection. Cyber resilience goes further, covering detection, response, recovery, and the ability to maintain operations, for example keeping broadcasts or publishing platforms running during an incident.

The Act directly applies to manufacturers, importers and distributors of products with digital elements placed on the EU market. However, UK media organisations that develop in-house tools, customise digital products or rely on EU-based technology providers must ensure their systems and processes align with CRA requirements, particularly around vulnerability management and incident reporting, if they operate or collaborate within the EU market.

By tracking metrics such as mean time to detect (MTTD), mean time to recover (MTTR), validated RTO and RPO targets, frequency of incident simulations, patching timelines, and the resilience of live production and content delivery systems under stress testing.

No. Smaller publishers, regional stations and digital media platforms are often more exposed due to limited internal resources and complex supplier networks. At the same time, regulatory expectations and audience scrutiny apply across the entire media value chain.

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5 key AI challenges facing the UK media industry https://www.future-processing.com/blog/key-ai-challenges-in-media-industry-in-uk/ https://www.future-processing.com/blog/key-ai-challenges-in-media-industry-in-uk/#respond Thu, 26 Feb 2026 10:38:45 +0000 https://stage-fp.webenv.pl/blog/?p=35696
Home Blog 5 key AI challenges facing the UK media industry
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5 key AI challenges facing the UK media industry

The UK media industry is moving away from "attention-chasing" toward what many experts call "Intentional Media" and "AI-adaptive" strategies. Read on to find out more.

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In 2026, the UK media industry has moved past the "AI experimentation" phase and is now in a "structural rebuild." For IT companies and consultancies, the sales landscape has shifted from selling tools to selling operational resilience and revenue recovery.

The timing adds complexity. The European Union adopted its AI Act in March 2024, creating extra-territorial compliance requirements for UK media distributing content into EU markets.

Meanwhile, the UK government pursues a “pro-innovation” approach through sector-led regulation involving Ofcom, the ICO, and the CMA. And since late 2022, generative AI has moved from research curiosity to operational reality faster than most organisations could adapt.

As Future Processing - a technology consultant and software delivery partner for media & advertising - we examined what companies in this sector aim to achieve with AI. And while their needs differ, AI remains a flexible tool that, when used well, helps organisations support their broader business goals.

Challenge #1: The data dilemma – turning analytics into decisions

Many British media firms possess decades of archives and audience data, but these are often trapped in fragmented legacy systems. The challenge is making that data useful for AI.

Breaking down data silos in legacy media houses

Consider a typical UK broadcaster. Audience viewing data sits in one system. Archive metadata lives in another. Social engagement metrics come from third-party platforms. Content rights information exists in spreadsheets and contracts.

For AI systems to deliver value, they need access to clean, connected data. But cleaning and connecting data from decades of different formats, vendors, and standards requires significant investment before any AI benefit materialises.

The organisations that struggle most are those with the richest archives – exactly the content that could generate the most value if properly accessible to AI tools.

Predictive vs. descriptive analytics

UK media organisations have historically excelled at descriptive analytics: understanding what happened, who watched, which content performed.

The shift to predictive analytics – forecasting churn, anticipating content preferences, enabling real-time personalisation – requires fundamentally different data infrastructure and skills.

GDPR and audience trust

UK GDPR creates both barriers and opportunities for AI analytics. The rigorous approach to data privacy limits certain personalisation techniques, but it also provides a framework for building public trust.

Consent:

  • Challenge: Obtaining valid consent for AI-driven personalisation
  • Practical response: Clear, granular consent mechanisms

Legitimate interests:

  • Challenge: Balancing business needs with user expectations
  • Practical response: Documented assessments and opt-out options

Data retention:

  • Challenge: Keeping audience data only as long as necessary
  • Practical response: Automated deletion policies tied to AI training cycles

Transparency:

  • Challenge: Explaining how AI uses personal data
  • Practical response: Plain-language notices about recommendation engines

Media leaders need cross-functional AI governance spanning editorial, legal, technology, and commercial teams.

Consultancy partners like Future Processing help design operating models that connect data strategy with compliance requirements from the start.

Get recommendations on how AI can be applied within your organisation.

Explore data-based opportunities to gain a competitive advantage.

Challenge #2: The efficiency imperative vs. implementation reality

Whilst the promise of AI is high, the practical reality of implementing it in a 24/7 media environment presents significant operational risks. Industry research shows that current AI models have significant practical limitations that constrain real-world application.

Automating the ‘boring’ to save the creative

The clearest efficiency gains come from automating repetitive processes: metadata tagging, transcription, logging, format conversion. These tasks consume significant production time without requiring creative judgment.

However, implementation faces concerns among employees about potential job changes amid ongoing industry restructuring. Workflows built over years resist change, adding to the uncertainty.

For example, in December 2025, Omnicom announced the departure of 4,000 employees, reflecting the scale of workforce adjustments in the sector. Additionally, January 2026 saw three major mergers among media companies in UK, further contributing to the changing situation.

The key findings from successful implementations show that positioning AI as augmentation rather than replacement reduces resistance – but this requires genuine commitment, not just messaging.

Areas where automation delivers measurable ROI:

  • Metadata enrichment for archive content
  • Quality control checks in post-production
  • Audience report generation
  • Automated transcription and captioning across multiple languages
  • Rights expiry monitoring and alerts

Reliability in 24/7 broadcast environments

AI in a “live” environment cannot be wrong. A hallucination in a news ticker, an incorrect sports score, a misquoted politician – these errors directly damage credibility.

Major news organisations like CNN are responding by investing heavily in AI-driven fact-checking and misinformation detection tools, specifically to identify manipulated images, deepfakes, and misleading content before publication. This represents a defensive approach where AI combats AI-generated misinformation rather than creating content.

For UK broadcasters, the risk of subtle misquoting of UK politicians, court judgments, or NHS guidance is particularly acute. The legal and reputational consequences of getting these wrong far outweigh any efficiency gains.

Cost of compute vs. ROI

When AI services transition from pilot programs to production deployment, the true cost structures become apparent. Service providers relying on data, recommendation, and personalisation – all AI-intensive activities – face unexpected cost pressures that aren’t apparent during experimental phases.

Many UK media organisations currently conducting AI pilots may discover that scaling these solutions to production environments is substantially more expensive than anticipated.

Practical safeguards for AI content workflows:

  • Human-in-the-loop editing for all externally published content
  • Red-teaming prompts to identify failure modes before launch
  • Editorial AI style guides defining acceptable use cases
  • Escalation protocols when AI confidence scores fall below thresholds
  • Regular audits comparing AI outputs against human-verified samples

Challenge #3: The integration headache

Most AI technologies arrive as external “black boxes.” Connecting them to existing – often outdated – production systems creates the integration headache that derails many AI initiatives.

The challenge is maintaining version consistency using AI. When AI tools modify content – adding captions, adjusting formats, generating thumbnails – tracking which version went where becomes critical for rights management and quality control.

Infrastructure issues compound these challenges: latency and scale for recommendation engines and real-time personalisation on high-traffic UK streaming and news sites require architecture that many organisations don’t yet have.

Integration with legacy CMS, MAM, and broadcast systems demands specialist expertise.

Challenge #4: The technical foundations (the hidden iceberg)

Moving from experiments to production AI in media brings non-trivial technical, financial, and environmental challenges. What organisations see above the surface – the AI features – represents a fraction of the investment required.

Cloud cost and FinOps pressures

Generative models and large-scale inference can significantly increase spending for media groups already under tight margins. The surge in mainstream AI usage is prompting media organisations to fundamentally rethink their infrastructure strategies.

As AI adoption accelerates across the industry, demand for advanced computing power intensifies, creating tighter availability and efficiency pressures that must be carefully managed.

Learn more about FinOps:

Effective Cloud Cost Governance strategy includes
Cloud Cost Governance - a guide to smarter cloud spending

Security and IP protection in the age of GenAI

Cybersecurity and safety risks multiply with AI deployment. Model endpoints become new attack surfaces. Data can leak through prompts submitted to external AI services. Secure MLOps practices aligned with NCSC guidance become essential.

Copyright protection and content security against leakage to public LLM models present particular concerns for rights holders. The BFI’s recent report on AI in the UK screen sector highlighted concerns over AI training on copyrighted material from film, TV, and journalism without proper licensing.

Real-world concerns raised by UK and global artists – including high-profile musicians like Sir Elton John and Sir Paul McCartney – about unauthorised use of their work and likeness have brought these issues into mainstream awareness.

Maintaining code quality in AI-assisted development

A less-discussed challenge: is AI generated code (from tools like GitHub Copilot) secure and maintainable in the long term?

As development teams adopt powerful tools for faster coding, they create potential technical debt and security vulnerabilities that may only surface months or years later.

Key opportunities of AI in software development
Key opportunities of AI in software development

Sustainability considerations

High energy consumption and carbon emissions of large models echo concerns raised in BFI and CoSTAR work. Greener architectures and regional data centre choices matter increasingly for UK media organisations with sustainability commitments.

Future Processing approaches these problems through discovery and architecture review, cloud cost optimisation, secure MLOps pipelines, and ongoing managed services tailored to media sector requirements.

£1B+ in bookings for the UK’s largest independent broadcaster with a new ad management platform

Challenge #5: Ethical and regulatory compliance in the UK

UK media players must balance innovation with ethics and regulation. Ad-hoc experimentation is no longer enough – structured governance has become essential.

The EU AI Act creates extra-territorial requirements affecting UK media companies distributing content into EU markets. Transparency and deepfake-labelling rules apply regardless of whether you’re headquartered in London or Manchester.

AI readiness audits across editorial, technical, and legal functions should map where AI is already in use – transcription, colour grading, recommendations – and assess risks for each application.

Governance area

Key components

  • AI policies

Acceptable use definitions, prohibited applications, approval workflows

  • Editorial guidelines

When and how to disclose AI assistance, quality thresholds

  • Risk registers

Documented risks with owners, mitigations, and review schedules

  • Incident response

Escalation paths for AI failures, public communication protocols

  • Labelling rules

Clear standards for marking AI generated content for UK audiences

The road ahead for UK media

The main AI challenges facing UK media – data fragmentation, implementation risks, integration complexity, technical foundations, and regulatory compliance – aren’t going away. But doing nothing is riskier than careful action.

Competitors, including global platforms with deep resources, are already using AI aggressively. The choice isn’t between AI and no AI, but between strategic AI adoption and being disrupted by those who move faster.

With responsible, well-governed deployment and the right partners, Artificial Intelligence can strengthen UK media’s public-interest role, creativity, and commercial resilience. The benefits will flow to organisations that focus on solving specific business problems – analytics, operations, distribution – rather than chasing generative content creation headlines.

Over the next 2–5 years, the gap between AI-enabled and AI-hesitant media organisations will widen. The technology and practice will mature, regulation will crystallise, and audience expectations will rise.

The organisations that start making concrete, low-risk moves now – building data foundations, establishing governance, running controlled pilots with proper human control – will be positioned to scale when the tools, costs, and confidence align.

Get recommendations on how AI can be applied within your organisation.

Explore data-based opportunities to gain a competitive advantage.

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Practical generative AI for media: how to use AI without the hype https://www.future-processing.com/blog/practical-generative-ai-for-media/ https://www.future-processing.com/blog/practical-generative-ai-for-media/#respond Wed, 07 Jan 2026 09:14:04 +0000 https://stage2-fp.webenv.pl/blog/?p=35336
Home Blog Practical generative AI for media: how to use AI without the hype
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Practical generative AI for media: how to use AI without the hype

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Generative AI has become a familiar topic across almost every industry, and the media sector is no exception. Many teams feel an expectation to explore new tools or test emerging capabilities so they do not appear to be falling behind.

This sentiment is often expressed through questions such as “We need to be using it” or “Where can we use it?”. When activity starts from that position, AI can quickly become a solution in search of a problem rather than a purposeful way to improve how teams work or make decisions.

Recently, Anna Mleczko, Head of Media & Advertising at Future Processing, sat down with Emma Wicks, Director of Customer Analytics & Data Science at The Telegraph.

During their discussion, Emma’s shared insight into her approach, which begins with identifying the real issue at hand and only then considering whether generative AI is the right tool to address it.

For media leaders, the question becomes less about how to use generative AI and more about where it can reliably help. The most effective applications tend to emerge in areas that are already well understood but have long been difficult to optimise, where the right intervention can strengthen decision making and reduce the day-to-day friction that slows teams down.

Creativity under constraint

Creativity in media organisations is often expressed through how teams approach everyday problem solving. With evolving audience behaviours, shifting platforms and a wide mix of legacy processes, progress frequently relies on people who can connect ideas across different areas of the business and rethink how established workflows operate. This type of creativity is not about producing something novel, but about recognising patterns and spotting opportunities to find practical ways of making improvements.

It is also an essential part of how AI is used effectively. Generative AI introduces new possibilities, although its value depends on the ability to frame the right questions and understand where it can meaningfully support existing work.

Emma Wicks explains:

We have not always got the biggest budgets or the best tools, but there will be a way to answer that problem with what we have got.
Emma Wicks
Director of Customer Analytics & Data Science at The Telegraph

This attitude reflects a broader reality across the industry, where resourcefulness shapes what innovation looks like in practice.

When teams approach challenges with this mindset, AI becomes a means to strengthen processes that have long been difficult to optimise, rather than a piece of technology to deploy simply because it is available.

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The problem-first approach to using AI

AI adoption in many organisations begins with questions such as “Where can we use it?” or “What can we automate?”. These questions often come from a genuine desire to innovate, but they can push teams towards technology-led initiatives that are only loosely connected to real operational needs.

Most AI failures in media come from starting with the technology rather than the problem. The result is familiar across the industry: isolated proofs of concept, tools that do not integrate into daily workflows and projects that struggle to demonstrate lasting value.

I always start with the problem first. If generative AI is the way to solve it, great. If it is not, we will not use it.

Emma Wicks
Director of Customer Analytics & Data Science at The Telegraph

A more sustainable approach starts with understanding the underlying problem. Instead of looking for opportunities to apply AI, teams focus on identifying the specific challenges that hinder progress or limit insight.

This perspective reduces the pressure to adopt AI for the sake of visibility and instead encourages teams to concentrate on areas where new capabilities can genuinely help. For media organisations, it also provides a safeguard for investment by ensuring that resources are directed towards improvements that support editorial, commercial or operational goals rather than experimental work that may not lead anywhere.

By making problem definition the first step, AI becomes part of a broader toolkit, applied selectively and with clear purpose rather than a technology that drives strategy on its own.

Use case 1: improving image metadata with AI

Problem: No structured way to analyse image characteristics at scale.
AI Capability: Generative AI auto-tags attributes such as people, faces, layout and composition.
Outcome: Clearer insight into which images perform best across platforms.

Image choice has a noticeable influence on how articles perform across platforms, yet many media organisations still lack the detailed metadata needed to understand why certain images work better than others. Most publishers rely on simple counts of images or basic engagement metrics, which offer little insight into the visual features that shape performance. In the absence of this structure, teams often depend on intuition rather than evidence.

This leads to a familiar pattern. When editors ask which image styles perform best, the task often becomes a manual review of thumbnail collections and an attempt to spot informal trends. As Emma noted:

We were literally just eyeballing thumbnails... now we can see whether a person looking at the camera works better.

Emma Wicks
Director of Customer Analytics & Data Science at The Telegraph

Manual review can produce broad impressions, but it cannot scale or reveal the subtleties that influence behaviour across platforms.

Generative AI provides a practical alternative by automatically tagging images with attributes such as the presence of people, facial expression, colour balance and layout. When combined with performance data and analysed through machine learning models, these tags make it possible to identify which visual elements contribute to stronger results on some platforms but not on others.

For media organisations, strengthening metadata offers immediate and transferable value. It supports clearer insight and more consistent testing, which results in more informed decisions about image strategy. By improving data foundations, teams can achieve reliable gains without relying on high-visibility AI features.

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Use case 2: AI for more accurate article classification

Problem: Outdated article classification taxonomies with inconsistent tagging and heavy manual rules.
AI Capability: Generative AI assigns mid-level subjects more accurately than rigid code.
Outcome: Clearer insight, improved reporting and stronger personalisation.

Article classification remains a long-standing challenge for many publishers. Taxonomies often evolve gradually, becoming brittle and inconsistent over time, leaving teams with a mix of broad categories that are too general and granular tags that are too detailed to support reliable analysis. The result is a classification system held together by complex rules, extensive conditional logic, and continuous editorial effort simply to keep it functioning.

Instead of relying on fixed rules or thousands of inconsistent tags, AI can help create mid-level taxonomies that better reflect how content is actually produced and consumed. These categories offer enough nuance to reveal meaningful patterns in audience behaviour while remaining structured enough to maintain consistency. At The Telegraph, this shift replaced a system built on lengthy logic statements. Emma gladly explained:

We used to have 13 pages of code... now AI categorises articles in a more consistent way.

Emma Wicks
Director of Customer Analytics & Data Science at The Telegraph

For the wider industry, the benefits are clear. More reliable categorisation strengthens reporting and insight generation, improves topic planning and reduces friction between desks by offering a shared understanding of how content is organised. It also enhances personalisation and recommendation systems by providing more accurate signals about article topics.

The broader lesson is that valuable AI use cases often begin with strengthening core foundations. By modernising taxonomies, media organisations can reduce editorial overhead, unlock clearer insight and create a more effective base for future AI applications.

Building a culture of safe AI exploration

The ability to use AI effectively is shaped as much by organisational culture as by the technology itself. Tools evolve quickly, and their value depends on how confident teams feel when experimenting with them and how willing they are to share what they discover. A low-pressure environment encourages this kind of behaviour, allowing people to learn through practical use rather than formal instruction alone.

One useful model is to create regular opportunities for teams to exchange ideas and demonstrate what they have found. At The Telegraph, this takes the form of monthly sessions that range from simple discoveries, such as a feature that improves the tone of an email, to early prototypes built through experimentation. As Emma observed, “It is not all on me to find everything”, a reminder that reflects the importance of distributing curiosity and responsibility across the whole team.

For media organisations, several practices help create the right conditions:

  • Encourage experimentation by giving teams permission to test tools without expectation
  • Support better prompt craft through simple guidance and shared examples
  • Offer safe spaces where people can try ideas without concern for mistakes
  • Highlight useful discoveries so others can learn from them

Together, these practices help AI adoption grow from genuine relevance rather than pressure, allowing new ideas to take hold naturally across the organisation.

Benefits of AI in digital transformation
Benefits of AI in digital transformation

Where AI creates real value in media

Much of the public discussion around AI in the media still focuses on automated content creation, particularly the idea of AI writing articles. Although this attracts attention, it overlooks areas where AI can make a more consistent and measurable contribution. The stronger opportunities lie in improving the infrastructure that supports journalism rather than attempting to replace the work itself.

Enhancing metadata, strengthening insight generation and streamlining workflows offer clearer value. These improvements help teams understand how content performs, identify bottlenecks and create space for journalists to focus on reporting and analysis. Selectivity is essential when using AI.

The real risk is not that AI will replace newsrooms, but that organisations may concentrate on visible experiments while overlooking quieter improvements that raise quality every day. By prioritising the foundations of content production, media companies can develop a more stable and effective approach to AI.

Summary

Generative AI delivers the most value when it is used to solve well-defined challenges rather than adopted by default. A problem-first approach helps teams understand where AI can make a meaningful contribution and where established methods remain more effective.

Strengthening metadata, improving insight generation and giving teams low-pressure opportunities to explore new tools all support this approach, even if these areas attract less attention than content-focused experiments.

Emma emphasised the importance of knowing “where we want to use it and where we do not”. Generative AI is not inevitable, yet it can be transformative when applied with purpose. The organisations that benefit most will be those that invest in foundations rather than shortcuts and prioritise improvements that genuinely support their goals.

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What is green coding? A contribution to save the environment https://www.future-processing.com/blog/what-is-green-coding-a-contribution-to-save-the-environment/ https://www.future-processing.com/blog/what-is-green-coding-a-contribution-to-save-the-environment/#respond Tue, 22 Nov 2022 10:34:02 +0000 https://stage-fp.webenv.pl/blog/?p=23310 What is green coding (sustainability programming)?

Green coding is an important development in the tech world that has gained traction over recent years. It refers to programming sustainable code written with the specific purpose of minimising the energy consumption of software developed. Less energy consumption means less waste, minimising harmful effects and benefiting our environment.

Digital evolution & impacts
Digital evolution & impacts

A research paper titled ‘How Green Are Java Best Coding Practices?‘ found there to be two main considerations to understanding green coding and how best to implement it – structural and behavioural.

Structural considerations pertain to energy measures that need to be taken into account with regard to code blocks (also referred to as units of code).

Behavioural considerations relate to how energy consumption varies depending on which user scenarios are being run at that particular time. For example, sending an email or checking social media.  

It can be tricky to highlight particular examples of green coding as their use is subtle and tends to have low footprints.

However, in order to increase an application’s performance through reducing energy consumption, programmers often use a tool called Big O Notation. This tool helps them calculate the efficiency of their coding algorithms, making them more ‘green’.  

Why Green Coding matters: its impact on sustainability

In 2018 alone, online video streaming resulted in the same amount of harmful greenhouse gas emissions as the country of Spain. Data centres use vast quantities of energy just to stay afloat. According to IEA, in 2020 alone, they drew between 260-340 TWh, which equates to approximately 1.1%-1.4 of the total global electricity use. With 7.2 million data centres worldwide, it should come as no surprise that the tech industry is now focusing on more ways to become green as part of its digital transformation strategy

At this time of increased digital interactions (thanks to the pandemic), we must search for greener, more energy-efficient solutions to our energy consumption habits.

Tech and software development companies have an important role to play in this move to a sustainable future, not least to reduce their impact on the environment.  

Germany has been a pioneer in the field of green tech in recent years, with its 2030 plan to reduce emissions by 65% and its 2050 plan to be entirely carbon neutral. Other countries and organisations are following suit, with big players such as AWS pledging to run 100% of their operations with renewable energy by 2025. 

How much CO2 can be reduced in 2030 in total?
How much CO2 can be reduced in 2030 in total?

The benefits of green coding

The importance of green coding and its environmental impact
The importance of green coding and its environmental impact

Today, businesses face considerable challenges, of which we can mention the excessive use of energy, global warming or the increasing awareness of customers’ purchasing decisions.

Green computing is an essential method of developing sustainable software development and practices, which in turn reduce a company’s carbon emissions. Fewer emissions mean lessening any negative, harmful environmental impact that the software may have.

By refining systems with eco-designed alternatives, we can save huge amounts of unnecessary energy wastage, perhaps as high as 30%. There are many advantages of green coding.

Have a look at some common benefits below.

Reduced energy consumption

Optimising software to use less energy is the fundamental goal of green coding. Given the growing global usage of data centres and their substantial electricity consumption, this is especially crucial as the digital world develops. When software is designed to use less processing power, it can run using less energy and this helps to reduce technology’s negative impact on the environment.

Lower operating costs and enhanced performance

In the realm of green coding, the principle of “using less and spending less” makes perfect sense. It’s crucial to minimise the power consumption of software to better manage operational costs amidst fluctuating and often high energy prices.

This approach, which is central to energy efficiency in software development, provides significant benefits for businesses. By optimising code to consume less power, companies can reduce their energy bills while also improving the functionality of their software systems.

Consequently, green coding has emerged as an attractive option for businesses, offering cost savings and increased productivity through more efficient software performance.

Positive brand image and social responsibility

By implementing green coding and energy efficiency standards, businesses can show they care about the environment and win over an increasingly green-conscious clientele and stakeholder base. Having a code of ethics in business is often consistent with the larger set of CSR objectives, especially for companies working toward zero emissions or lowering their carbon footprint.

Faster page speed

Green coding refers to a technique used in applications to reduce the number of server requests, which in turn leads to an increase in page speed. As a result, users can access content more quickly and are less likely to experience frustration, ultimately leading to higher satisfaction and engagement.

Additionally, the reduction in server requests translates into less energy being consumed, resulting in lower energy usage and less wastage.

Server load is decreased

It’s important to note that implementing green coding practices can potentially decrease server load. This is because optimising applications can reduce the number of server requests, resulting in an overall lower server load.

Furthermore, green coding practices can reduce the amount of bandwidth needed to run web pages and applications, thus increasing efficiency and decreasing waste.

Improved SEO

When applied correctly, green coding helps to improve SEO and increases a website’s ranking. Although not an environmental advantage, it is undeniably an important business benefit.

Read more about ESG and Digital Transformation:

How do I make my code greener? Adopting green coding practices

Don’t get us wrong, energy efficient hardware alone will not solve all the issues we have that are related to carbon emissions through energy waste. However, it is an important tool to add to your metaphorical toolbox when it takes positive steps towards a more sustainable world and overall energy consumption.

Clever coding can help utilise more renewable technologies through workload orchestration.

It can enhance the use of silicon-aware coding to reduce tech debt, energy use and it can minimise the amount of data which is transferred over a particular network by optimising its applications. All of these are very worthwhile pursuits.

For companies interested in adopting green coding principles into their operations, here are some key starting points to follow.

Training

Train your staff to make them aware of green coding and its benefits so that they understand what it is and what benefits it offers. This is the first important stage to adopting it in your company culture.

Provide ongoing training and awareness-raising sessions so that everyone works with the same philosophy on their daily tasks. With good training, development teams will be motivated to scale back on superfluous lines of code, optimise their systems and achive sustainability goals.

Provide incentives

It is important to incentivise software developers who leverage green coding practices in their daily tasks. Reward good work and encourage innovation.

Help them to see a valuable reason for pouring their efforts into adopting greed coding and show them real, tangible compensation when they adopt this strategy, reduce energy consumption and hit important milestones.

Encourage a company-wide adoption and accountability strategy

Build the ‘green software’ philosophy into every aspect of the business and digital transformation. After all, it’s not only up to the software engineers to drive their company forward in the fight to become sustainable; every single employee has a shared responsibility.

Build it into the very fabric of your company in a multifaceted manner, as this will be a constant reminder that you want and need this to work for the betterment of our world.

The future of sustainable programming

It’s becoming clear that sustainable programming practices will play a crucial role in influencing the future of the software development sector. As more people become aware of the importance of reducing the adverse effects of coding on the environment, sustainable programming is quickly gaining acceptance.

To ensure environmental sustainability, software product lifecycle management should prioritise eco-friendly choices throughout the development cycle, from conception to disposal. The future of sustainable software engineering also lies in developing user-centred software that has minimal impact on the natural world.

When it comes to developing new solutions, cloud-based alternatives are more energy-efficient than conventional data centres. By improving resource utilisation and reducing energy usage, cloud services are increasingly popular among businesses. This transition not only provides scalability and cost-effectiveness but also contributes to sustainability.

How does Green Coding influence software lifecycle and maintenance?

The environmental problems we are currently facing in the world are not going to be solved overnight with coding optimisation. However, it is a key piece of the puzzle and has an important part to play in reducing emissions and lessening the damage we are unfortunately doing to the planet.

Bit by bit, piece by piece, we are finding ways to work better, faster, more economically and with less energy usage. Sustainable software engineering is an important aspect that, if adopted by the millions of tech organisations and programmers out there, will certainly make a significant impact.

Although it’s a relatively new approach, green coding will be a part of a bigger solution to propel us into a more energy-efficient environment.

For organisations looking to contribute to this vital effort, Future Processing offers expertise and guidance in sustainable programming practices. Contact us to learn how your organisation can be part of this vital change towards a greener future in technology!

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Building a tech team – what are the biggest challenges? https://www.future-processing.com/blog/building-a-tech-team-what-are-the-biggest-challenges/ https://www.future-processing.com/blog/building-a-tech-team-what-are-the-biggest-challenges/#respond Tue, 25 Oct 2022 23:56:25 +0000 https://stage-fp.webenv.pl/blog/?p=22855
What does building a team mean?

One of the main principles to take into consideration when creating a team is that building a team always means building a team. No matter the scale of the organisation you are doing it for, no matter the sector you are in, or the budget you have, the challenges around creating a team of people who are to work together are always the same, or at least very similar. After all, it’s all about dealing with people, about supporting and managing them in the best possible way.


Managing teams across different locations  

In terms of logistics, managing teams based in different locations got a lot easier after the pandemic. Today everyone is working remotely – it became a standard. Beforehand, it was a pretty unusual thing, but the only one that worked if one was to manage teams based on different continents.  

What has not changed at all is the approach. When creating a team based on a certain culture, it’s critical to understand the culture of people you are going to work with. A team based in Hungary will be different from a team based in Poland, from a team based in the UK or from a team based in India. Their problems and motivations will be different, too. If the team building process is to be successful, one needs to get to know the culture and background by doing proper research.

A great way to understand people you are going to work with is to ask them about their challenges. Not only work-related challenges, but general ones.

You may be surprised by what you hear: someone will be worried about inflation, someone else about travel (like for example people working from India who often collaborate with companies based in different time zones and need to leave office late at night when there is no public transport).  

The most effective way of learning about those challenges is to have meetings with as many people as possible, to make yourself available to everyone who wants to talk, to interact with people within the organisation you work for. Only that way a manger is able to understand what motivates the team and what impacts the way it works. 


The biggest challenge when building a team 

Building a team always means challenges, and the main one is to make people understand the culture you want to build. To do so, the manager must be clear about the values the team needs to represent and must be able to communicate them so that everyone understands them. And must stick to them at all times, no matter what.  

How to convey such a message? It’s important to take time to speak about it to everyone within the team, to walk around the office (personally if possible, or virtually, by arranging different meetings), or to organise a big team meeting if speaking to everyone in person is physically impossible. All those conversations and meetings will help pass on the message on what’s most important for you and how you want to work.   

After the phase of introduction, it is crucial to be able to explain to people your operating model, to justify why you made certain decisions and why you stick to them even when you are being challenged. This doesn’t mean that you are not listening to other views just that sometimes leadership requires you to make decisions and stick to those decisions.

Seeking continuous feedback ensures that you use data to validate any decisions made and have the ability to improve.

When building a team, you need to be ready to have difficult conversations and in those moments it’s worth remembering about your KPIs. Human-centered approach is extremely important and should always be at everyone’s heart, but when you are to have a difficult conversation, if someone is undermining your credibility, you need to have facts and KPIs that will speak for you.  


Things to avoid when creating a team  

When you are in a fast-moving environment, it can be tricky to keep the balance between being too rigid and accepting too much criticism. The main advice here would be to never change your mind too quickly, without thinking it through first. There may be some tough moments when people don’t believe in you when they challenge you, but you need to stay firm behind the decisions you made and behind the team you support.  

If you are to change your decision, always take into consideration the impact your change will have on the team and people you work with.

Too many changes cause destabilisation and people may stop seeing your clear direction. 


Setting up a team from scratch: the shortage of IT professionals  

The first thing you need when you set a team from scratch is the knowledge of who exactly you need: suppliers, managers, service management, data, architecture, security, engineering team, dev ops, and all other different roles.  

Top things to know when building a tech team:  

  • choose people with a strong can-do attitude, who can knock down hurdles when they arise;  
  • always be positive; 
  • always look for people with the right mindset and cultural fit.  

When setting up a tech team you also need to take into consideration the situation in the market. We all know it’s difficult to find IT professionals nowadays, and you may need a lot of them. This is why when creating a team, you should decide on the model you want to go for: either a permanent team of people employed by you or a mix of full-time employees and an outsourcing partner.

If it’s the former solution, decide on the ratio: is it 30 – 70 (30% of your own employees, 70% of outsourced staff) or 70 – 30, or 50 – 50?

Once you know your blend, you need to decide what exactly you are going to outsource and how are you going to do that, which specialists will be from the outsourcing agency, and which will be employed by you. It’s important that you build the model yourself, that you decide what’s the right balance, which parts do you want to do by yourself and which you want others to do.

And remember that managing an outsourcer is very different from managing your own people.


Cultural differences when outsourcing  

Once you decide about the outsourcing model you want to stick to, it is important to remember that your outsourcing partner should be the right fit for your company. After all, if you don’t get on with people, if the teams are not working together, then even the best contract you sign is totally useless. Getting to know the company you are about to collaborate with – their experience, flexibility, quality – is therefore crucial if your collaboration is to be successful.    


Top competencies in the financial industry from a fintech scale-up point of view  

And lastly, a few words about the competencies needed in the fintech world. While knowledge of the industry is always very important and welcomed, the lack of it should never preclude anyone from hiring someone with a different background. Such an approach may even prove beneficial, as different background usually means different perspective.  

There are some industries that are more closed than others, like health tech, but fintech is a much more open environment, ready to accept people from a variety of other sectors.  

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The power of Artificial Intelligence in healthcare – best examples https://www.future-processing.com/blog/the-power-of-artificial-intelligence-in-healthcare-best-examples/ https://www.future-processing.com/blog/the-power-of-artificial-intelligence-in-healthcare-best-examples/#respond Thu, 29 Sep 2022 12:09:25 +0000 https://stage-fp.webenv.pl/blog/?p=22705
AI transforming healthcare  

AI has been transforming healthcare for some time, but the recent pandemic accelerated the process, making things like access to e-health systems not just a commodity but a must-have.

Today patients are getting increasingly used to online meetings with doctors, e-recipes, apps that help monitor their health.

More and more technology is being used for treatments of cancer or heart diseases while elderly people use programs that allow practitioners to monitor them at home, without the need of prolonged hospitalization.   

Source: https://research.aimultiple.com/healthcare-ai-use-cases/ 

The industry is changing rapidly, and its main drivers, as listed by the recent Retail Health & Wellness Tech report by PitchBook, are: 

  • COVID-19 pandemic crisis and the need for a robust pandemic preparedness and response infrastructure based on technology, 

  • Doctors focusing more on preventive care and healthy lifestyles, 

  • Increased consumer health awareness and healthy eating index, 

  • Consumer wellness startups partnering with corporations, 

  • Growing elderly population and increased life expectancy,  

  • Digital economy opening door to telehealth, personalized solutions, and fitness applications, 

  • Population increase coupled with lack of medical providers, 

  • Growth of social media, useful in promoting healthy lifestyle, 

  • Consumer and provider demand for healthcare flexibility, 

  • Expanding cost of traditional healthcare, 

  • Advancements in AI and Big Data.  


AI in healthcare – best examples  

In the last couple of years, we’ve seen some magnificent examples of how ML and AI are being used in healthcare. Here are some of the most impressive ones:  

  • COVID-19 Clinical Severity Predictive Analytics Tool, COVID-19 Early-alerts Signals and Vaccine Hesitance Analytics Tool 

Since COVID-19 outbreak, predictive analytics and AI technology are being used to fight the pandemic by predicting the pandemic curve, tracking misinformation related to vaccines and extracting themes and topics on vaccine hesitancy. They have been of great help to governments and various health systems. 

  • Manufacturing and distribution of COVID-19 vaccines 

AI and ML technologies have been used to extract and analyze data needed to reliably scale the vaccine production. Advanced systems have been implemented to help understand when and where to distribute doses of the vaccine so that they get delivered where they should, hugely influencing supply chain and pharma sectors.  

  • Live predictive analytics for urgent care system 

Live predictive analytics is used to determine the likelihood of patients developing medical conditions such as cardiac problems, diabetes, or strokes. Business intelligence technologies and predictive analytics are also used to proactively direct ambulance flows to support each other when needed, helping healthcare providers delivering optimal care.  

  • Drug development 

In pharma sector, AI based systems are being used to support researchers in their drug development processes. Such an approach saves time and money and allows for a through analysis of all data related to the drugs. New technologies also help with data monitoring and improving patient recruitment into clinical trials. 

  • Radiomics

Radiomics is a field of science which aims at development and analysis of biomarkers extracted from medical images. With the potential of uncovering patterns not visible to the naked eye, it is successfully used for disease diagnosis, survival prediction and assessment of response to a treatment.  

  • Automated DCE-MRI analysis  

DCE–MRI plays a vital role in brain tumors diagnosis by providing information on tumor prognosis. When done manually, it is prone to mistakes and is very time consuming. A fully automated solution can accelerate diagnosis, ensure reproducibility, and reduce mistakes. 

  • Robotics surgery 

Robotics surgery enables doctors to perform complex medical procedures with the highest precision and control. It is usually associated with minimal invasive surgery, although it is now being used also in more traditional surgical procedures. Thanks to its precision (precise cutting and stitching not available through traditional techniques), it minimizes blood loss and risks of infection for patients.  

  • Chatbots and administrative assistance 

By offering immediate response and communication with patients, chatbots are a great way to improve primary healthcare and accelerate the process of getting access to the right information. Digital human platforms allow patients to get in touch 24/7 and find answers to questions they may have. Administrative assistance tools on the other hand are the most important and widely used AI applications in healthcare. They help healthcare professionals in their everyday duties, in time management and assessments, saving a huge amount of time and money in the whole industry.   


Platforms for medical AI professionals 

But there is still a lot to be done, which is why it is extremely important to educate and support healthcare professionals keen to introduce innovation and AI to their organizations.

Good places to start looking for more information about the subject and for advice are:  

  • SoPE (Society of Physician Entrepreneurs)

    A global community of changemakers and entrepreneurs transforming the future of healthcare. Its main purpose is to provide a global community-based platform for biomedical and healthcare entrepreneurs to connect and collaborate. 
  • MI10 (Medical Intelligence 10)

    Trusted AI education and adoption advisors, who teach and train students, trainees, clinicians, patients and stakeholders about artificial intelligence and its adoption. Its highly qualified team of experts has a wide experience in different fields of healthcare and artificial intelligence, including augmented & virtual reality, cloud computing, deep learning, and robotic process automation. MI10 is a place to go for advice, education, and evaluation of your organization’s readiness to implement AI strategy.  
  •  RYTE

    The first all-in-one solution that gathers and analyzes billions of data points on millions of healthcare providers and clinics globally.  


The impact of introducing AI in healthcare 

Given its ability to optimize the use of resources, it is predicted that in the nearest future AI will have a huge economic impact on healthcare, making it one of the most innovative sectors and ensuring high-quality medical help.

Source: https://www.medtecheurope.org/resource-library/the-socio-economic-impact-of-ai-in-healthcare-addressing-barriers-to-adoption-for-new-healthcare-technologies-in-europe

 According to a report by Deloitte and MedTech Europe, AI can potentially help save as many as 400 000 lives a year, while freeing up to 1.8 billion working hours annually, which is equivalent of having 500 000 additional full time healthcare professionals.  

These are huge numbers which should not be ignored. The time of AI and ML in healthcare has come and it’s only wise to follow this wave of innovation within all medical related businesses.

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Finding the right business partners: Clutch.co https://www.future-processing.com/blog/finding-the-right-business-partners-clutch-co/ https://www.future-processing.com/blog/finding-the-right-business-partners-clutch-co/#respond Wed, 21 Sep 2022 10:10:11 +0000 https://stage-fp.webenv.pl/blog/?p=22633
A global shift

Research by NTT Services confirms that as much as 45% of global corporations plan to increase outsourcing in the recent years.

Source: https://findstack.com/outsourcing-statistics/

The main reasons why companies look for trusted partners and consultants to work on important projects are the cost-effectiveness of such a solution and easy access to the pool of talented and experienced candidates with the new technology it guarantees. The deal is clear, but only if the partners you decide to work with are trustworthy. If not, outsourcing may become a nightmare and you may not get all the benefits. This is why finding the right company to work with is of key importance if you want your projects to be successful.


How to find the best partner for your company?

When thinking about finding the best partner or consultant to work with, the first thing that comes to mind is looking on Google. Sounds easy but google “IT services outsourcing” and you will get so many results there is no way you can go through all of them. You can try to find some articles about the best options available, but many of them are sponsored, so getting credible information is difficult.

Another challenge arises when you look for clients’ reviews: many websites publish just positive ones, and some companies use sponsored publications and paid endorsements, so finding impartial opinions is nearly impossible.

Opportunities to get proper market insights allowing businesses to make the right decisions when it comes to choosing their collaborators are therefore limited and presented in a pretty chaotic way.

There is also an independent platform that focuses on presenting solution providers and on publishing as much information about them as possible, including reviews. It’s called Clutch.co.


What is Clutch.co and how does it work?

Clutch.co is the leading marketplace for B2B service providers.

Their team collects genuine clients’ feedback and analyses global industry data, giving businesses access to insights they need to find reliable partners. Clutch.co conducts in-depth interviews through phone calls and online form submissions with clients of each company they list, speaking about the quality of their services and many important aspects of collaboration. All reviews come with a rating displayed next to each company’s profile.

At the moment, Clutch.co’s database contains as much as 150 000 companies from all over the world, categorised by their location, expertise and skills.

To find the right company for the project you want to undertake, you can search Clutch.co’s website by the region where you want your partner to be based, by the price of their services or skills they possess. Thanks to Clutch.co’s marketplace, the whole process of finding the partner is smooth, cost effective and there is not much room for mistake. Given the great service they offer, Clutch.co’s website traffic and customer base are increasing each year.

Clutch.co’s starting page, source: https://clutch.co


What do clients say about Clutch.co?

From the clients’ point of view, Clutch.co is a service that saves their time, effort, and money. They use it to find reliable partners, to compare them and check their scorings. From the point of view of companies listed on Clutch.co, it is one of the best platforms to be on.

It drives traffic to their websites and, what’s more important, the quality of the visits it generates is very high.

“We’ve been collaborating with Clutch.co for over two years now. During that time, we saw 650% increase of traffic from clutch.co and services such as themanisfest.com and Visual Objects. This is how Clutch.co became one of our top five web traffic sources. It’s also worth mentioning the quality of the visits we get from Clutch.co: it constantly improves, as proved by an average length of a session on our website (increase to almost two minutes). What’s more, Clutch.co provided us with a wider picture when it comes to analysing the market. Thanks to data we got from Clutch.co (such as statistics for each category and the number of visits to our website), we can accurately select groups of services and technologies with the best potential.”
Marketing Data Analyst
Future Processing


Why is working with reliable companies important for Clutch.co?

Future Processing on Clutch.co, source: https://clutch.co

Clutch.co’s updated mission as of July 2022 is to empower better business decisions as the leading marketplace of B2B service providers. To achieve that, they need a lot of trustworthy companies their clients can browse through.

We are among the companies listed on Clutch.co, and the portal that allows us to share information on our experience and expertise with more potential clients every year. A good place to be at.

There is no doubt Clutch.co is a powerful platform that improves visibly, builds trust, provides transparent and honest feedback, and creates marketing opportunities, thanks to their reports and social media presence. It’s a great tool for companies around the world. But it works both ways.

For Clutch.co, listing reliable and trustworthy partners is a key to survival. Their vision is to ensure that every business leader starts at Clutch to find the right B2B services.
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Top 10 leading FinTech companies you must know https://www.future-processing.com/blog/top-10-leading-fintech-companies-you-must-know/ https://www.future-processing.com/blog/top-10-leading-fintech-companies-you-must-know/#respond Tue, 20 Sep 2022 06:42:00 +0000 https://stage-fp.webenv.pl/blog/?p=22612 Although in 2022, those “unicorns” are no longer as rare as the mythical creatures, it’s still an impressive milestone to reach in the investors’ world.

Considering an enormously successful wave of seed funding in 2021 and the rate at which innovative digital products and tools are developed these days, industry sources state there are approximately 1,100 start-ups with that status in mid-2022. Roughly 20% of them are in the fintech game.

Here are ten of the American ones we’re keeping a close eye on: from powerful giants to wave-making leprechauns.


Stripe – Payments infrastructure for the internet

Valuation: $95 billion (March 2021)

Practically every fintech ranking includes Stripe. Because ignoring this groundbreaking start-up would mean not acknowledging the historic example it set for the “unicorn” club. The company was founded in Silicon Valley in 2010. 12 years later, it is the most valuable American private fintech and the fourth one globally.

At Stripe, everything is about effective payment processing online.

Nasdaq chose it because of its robust security and reliability. Urban mobility company Lime, on the other hand, was looking for improved accuracy and speed of financial reconciliation. In January 2022, Spotify partnered with the start-up “to help creators easily monetize subscription content.” And those are only three examples from Stripe’s impressive client list.

Unicorn fact: The company’s founders are Irish. Hence, the HQs are in Dublin and San Francisco. This duality emphasizes the start-up’s presence in two big financial markets on both sides of the Atlantic.


Chime – The mobile banking everybody loves

Valuation: $25 billion (August 2021)

What’s the most annoying thing when keeping your money in the bank? We bet it’s the associated fees (speaking from experience).

Chime‘s founders thought so, too. So they decided to offer free checking (current) accounts instead. They chucked in zero overdraft fees with cash advances in the bundle, too. And you don’t even need a minimum balance to open a free mobile bank account.

Today, it might not sound so unusual anymore. However, when the company was founded ten years ago, it was still very much a novelty.

Unicorn fact: Chime boasts 13 million active accounts in 2022, compared to eight million in 2020. That’s more than New Zealand’s and Hong Kong’s populations combined. Not all customers have free checking accounts, but that number tells you a lot about the bank’s rising popularity.


OpenSea – How many NFTs have you already minted?

Valuation: $13.3 billion (January 2022)

If you’re still not well-versed in the NFT subject, can you truly call yourself a 21st-century entrepreneur? The market for this digital asset is growing as quickly as the temperatures are rising in the UK in summer 2022.

OpenSea brags about being the original peer-to-peer platform providing a marketplace for NFT buyers. Its history is fairly recent (it was established in 2017), but it already processes “about $3 billion in NFT transactions monthly.”

While not without its challenges, OpenSea has made a free minting tool available to virtually any user. So if you’ve ever wanted to give it a shot, it’s your once-in-a-lifetime opportunity before everyone is an NFT owner.

Unicorn fact: The platform’s founders will go down in history books as the first NFT billionaires, too. They reached that status in January 2021 already.


GoodLeap – A sustainability marketplace

Valuation: $12 billion (October 2021)

Renewable energies, and especially solar power, are hot topics these days.

As more businesses and individuals embrace environmentally-friendly initiatives, switching from traditional fossil-fuel sources to more sustainable methods of energy production is a necessity if we want our children to have a future on this planet.

GoodLeap (fka Paramount Equity from 2003-2017 and Loanpal until 2021) is well aware of that. The start-up’s execs bring the combined forces of solar energy generation systems and mortgage broker experience. The company makes it easier for homeowners to secure financing for greening their households. Some eco-friendly solutions they back are battery storage, smart home devices, energy-efficient windows, or water-saving turf.

Unicorn fact: In January 2021, the company was the top solar lender in the US. If this statistic doesn’t impress you, we don’t know what else can.


Deel – International payroll made easy

Valuation: $12 billion (May 2022)

Before the pandemic, remote work was a privilege. In 2022, it is normality. Businesses have learned that geographically-dispersed teams and hiring experts abroad are no longer obstacles to getting the job done well. Chances are your staff are also partially based elsewhere.

Until Deel came along in 2018, the only challenge remaining was hiring and remunerating overseas employees. The start-up’s comprehensive platform provided a practical solution for that, “built for today’s world of work.

Deel offers a range of other useful services: ensuring compliance with local laws, non-traditional payment options, including Revolut, PayPal, or cryptocurrencies, facilitating access to health insurance, or helping with visas for foreign workers.

Unicorn fact: Due to the nature of its business, Deel is, obviously, a remote-first start-up as well.


Kraken – A crypto exchange for everyone

Valuation: $10 billion (projected – June 2021)

If you’re an investor, you know what’s crackin’ anyway. So this leading platform would be your go-to place for 120+ cryptocurrencies, amongst them Bitcoin and Ethereum, that you can exchange for fiat (regular) money.

Not only is it one of the most trusted online marketplaces in the world, but it’s also one of the oldest. Investopedia called it “a good choice for new and experienced crypto investors.” And Forbes Advisor gave it a 4.6-star rating, citing sophisticated features, good customer service options, and relatively low Bitcoin withdrawal fees as its main advantages.

For a company entering its teenage years (11 in July 2022), Kraken has already proved its “wisdom” beyond its age. The start-up doubles down on cybersecurity and is particularly uncompromising about protecting its users from potential hacks.

Unicorn fact: Kraken reports its pricing to the Bloomberg Terminal, which makes it that much more credible in the trading world.


Cedar – It pays to care

Valuation: $3.2 billion (March 2021)

When you recover from being unwell, the last thing you need is another headache related to payments for your treatments.

Enter Cedar, a start-up that strives to alleviate some more pain from that traditionally cumbersome process.

The New York company, started in 2016, is in the business of removing obstacles in “today’s fragmented healthcare system [that] frustrates everyone.” It’s a payment and engagement solution used by hospitals and other medical organizations to interact with their clients.

Cedar’s consumer-friendly platform, labeled “The Cedar Advantage”, takes the clients through the whole process: from pre-service (appointment booking) to post-visit (billing breakdown). Like many other innovative products, it was birthed after the co-founder’s “nightmarish personal journey through the healthcare system led to confusion, frustration, and disappointment.”

Unicorn fact: In the sector brutally tested in the pandemic times, we hope that investment in digital health solutions will continue to improve patients’ journey. Cedar is a perfect example of necessity turned opportunity.


Caribou – Car payments under control

Valuation: $1.1 billion (May 2022)

Americans love their cars. The country is so big that an automobile is often the best, if not the only, means of transport. Hence, insurance and other car-related payments are probably a common worry for the US population.

Caribou wants to change that. It claims that if “cars offer flexibility and freedom, payments shouldn’t hold you back.” As a result, it aims to make car owners aware that their best deal is not always the best one. Secondly, it wants to educate them on how to make savings next time.

Although the company has only recently joined the “unicorn club”, its innovative angle leverages the value of local lenders. Thanks to that partnership, the start-up can offer its clients a more competitive rate portfolio.

Unicorn fact: Goldman Sachs is one of the investors behind Caribou. We’ll just leave it at that.


Human Interest – Helping employees save for retirement

Valuation: $1 billion (August 2021)

In a world driven by purchasing power and hedonism, hardly anybody thinks of retirement. We’re used to spending our earnings here and now. That avocado on toast story, “blaming” the millennials for not knowing how to invest money, might be slightly exaggerated. But the truth is that, as a global society, we’re not very good at saving up for the future.

Human Interest noticed that worrying trend already in 2015. This 401(k) (pension-oriented) has developed a digital retirement benefits platform that allows the employees of small and medium-sized companies “to launch a retirement plan in minutes and put it on autopilot.”

And it seems to have gained the traction it deserves. Since the pandemic has taught people how important it is to have a financial buffer, Human Interest’s client base and revenue have significantly grown in recent years.

Unicorn fact: The start-up is currently preparing for an IPO in 2023. So this is probably the final call to get on the bandwagon.

You are expecting number ten now, correct? But instead, there’s an honorable mention:


Klarna – Buy now, pay later

Valuation: $46 billion (with an estimated drop to $30 billion – May 2022)

While not exactly a US company, we can’t omit this post-purchase payment start-up in this ranking, either.

Established in 2005 in Stockholm, it began operations in the States ten years later. Along with enormous success on the other side of the Atlantic, it is also “Europe’s most valuable private tech company” to date.

The Swedish start-up speaks to every consumer’s dream, regardless of their country of origin. Klarna’s core business ponders the question: Wouldn’t it be nice to buy something but not have to pay for it right away? And the answer is “yes”.

Unicorn fact: The start-up doesn’t make money from those postponed payments. Instead, it charges “retail partners for affiliate marketing and payments services.”


Conclusion

Although they are now public companies, Airbnb, PayPal, and Google are sometimes considered 21st-century “unicorns” as well.

Their story is almost like a fairy tale. It’s a little about fighting dangerous dragons, seeking the advice of wise wizards, and gradually gathering the riches to become “kings” of their respective “territories”.

And if that “fantastic” journey is any indication of where being a “unicorn” can ultimately take a start-up in the future, smart investors should pay even closer attention to progressive fintech companies in 2022. Like the ones we presented above.

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How to build green software development? https://www.future-processing.com/blog/how-to-build-green-software-development/ https://www.future-processing.com/blog/how-to-build-green-software-development/#respond Thu, 08 Sep 2022 09:39:26 +0000 https://stage-fp.webenv.pl/blog/?p=22568
What is green software development?

Also known as sustainable software development, green software development is a new and important discipline that applies to software practices and architecture, climate science, data centre and hardware design, and electricity markets.

Green software development refers to software that has been developed with the goal of limiting its negative effects on the environment, particularly in terms of energy consumption. It aims to reduce its subsequent carbon footprint by generating fewer greenhouse gases, making it kinder to our environment and positively impact the world around us by operating as efficiently as possible.


The importance of green software development

The concept of ‘green technology’ has been floating around for the past two decades, but it is only recently that it has begun to build meaningful traction. The green tech and sustainability market was valued globally at $11.2 billion back in 2020, but is expected to climb to $36.6 billion by 2025 with an annual growth rate of 26.6%.

Data centres use huge amounts of energy to run, sadly contributing to the global climate issues that we are experiencing today. Between 2010 and 2020, global data centre electricity use was around 200-250 TWh – 1% of the global energy demand.

In 2020, global internet traffic surged by more than 40% with the total usage rising to 260-340 TWh in just a single year – 1.1-1.4% globally.

It is therefore clear how important it is to curb global energy use in digital technologies given its monumental saturation of the total demand on our planet. Only through continued and sustained efforts within the IT industry will this happen, making green software development and practices more crucial now than it has ever been before in our past.


The principles of green software development

The principles of green software are an established set of fundamental guidelines that engineers must follow when developing and implementing software applications.

The principles of green software development
Source: https://www.future-processing.com/blog/everything-you-need-to-know-about-green-and-sustainable-software/


What are the benefits of building sustainable software?

The environmental benefits of green software developments are clear. Greener tech means less energy usage and better efficiency. In turn, this reduces carbon emissions which positively contributes to reducing the damage to our environment. This is a widely accepted concept that everyone is on board with. Everyone in this world has a personal and collective responsibility in fighting climate change within their own personal sphere.

As well as these aforementioned environmental benefits, there are further sustainable software benefits that help to make an excellent case for green software development.

  • Less complex architecture

    Green software typically runs with less interdependencies, meaning that they are inherently less complex, and therefore, they use less energy to function.

  • Cost effective

    Fewer interdependencies means few resources are used, which in turn means that they are cheaper and more cost-effective to run.

  • Faster computing speeds

    Similarly, lower complexity in a system means that it can often run much faster.

  • Positive brand recognition

    Being seen as an ‘environmentally friendly’ company is hugely positive in today’s global social climate. Making your brand visible as a champion of green tech, pioneering the way in your industry for positive change does wonders for your brand image and will gain you a loyal following of similarly eco-focused customers. Of course, it’s important to ‘walk the walk’ and not only ‘talk the talk’, but that should go without saying.


How to build a sustainable software development Team

Green team building is much the same as building any successful tech team, but with a central and continuous focus on ‘sustainable’ practices.

When compiling any crack team of professionals, it is important to set out a hiring plan so as to onboard members who not only have the relevant skills and experience, but the right mindset. This is particularly important in sustainable team building as you need every single member of the team to have a deep understanding and passion for green development, and for them to have this concept at the forefront of all their operations. When interviewing potential candidates, go through their own personal thoughts and experiences of green and sustainable tech, discuss examples they have worked towards in their career to date and get to understand their own philosophy on the matter.

Going through these areas early on will help to cement not only your trust and confirmation in the individuals themselves in terms of how they can successfully impact your green projects, but the employees’ own understanding of your company philosophy and what will be expected of them moving forward.

Once you have assembled your team, positively promote the sustainable culture into all of your operations. Meetings, budgets, product proposals and so on should always be viewed with the underlying thought process of ‘how green is this?’ and ‘what steps can we take to become more energy efficient, to run more effectively, and to reduce emissions?’. Regularly sharing your sustainable values, in a non-preachy or patronising manner, will help to build this ideology in your company effectively, motivating your team and shaping all of your operations effectively.

Lastly, it is really important to encourage the flow of ideas on how to better approach your green software development. Operate with an ‘open door’ policy and build transparency and autonomy into everything you do. If staff members feel that no stage of the tech development is hidden or ‘off the table’, they will feel empowered to bring their thoughts and opinions on how to better improve efficiency and build more sustainability into the project.


How to build a sustainable product roadmap

Building a green product roadmap is the final piece in the puzzle to becoming a truly sustainable-focused tech team. In order to create a successful environmentally friendly roadmap, it’s important to consider the following 4 key aspects:


Environmental impact

First and foremost, the product roadmap should detail where and how it will positively impact the environment. Some key questions to ask yourself at this stage include:

  • How will it reduce emissions compared to another approach?

  • How will you build in energy efficient strategies?

  • What materials will you use? Are there other ‘greener’ materials you can use?

  • What supply chains will you rely on? What green strategies do they have in place?

  • Will this strategy be ‘green’ right away or will it take time to develop?

  • Defining the answers to these types of questions early on will help you develop and refine your sustainable product roadmap and act as the framework to which all decisions can be based throughout its lifecycle.


Stakeholders

Once you have defined the areas that you intend to build in sustainability and green processes, consider who your stakeholders are.

All your stakeholders will need to share your ‘green’ focus and sustainability efforts to as to maximise the positive impact of your project. This is applicable at every level, from your investors who will provide you with funds to develop your product, to your team members (as discussed previously), right through to the end users. Personnel at each stage will wield social and financial influence on your product so making sure they are all on board and share the same environmentally friendly vision is key.


Sustainability strategies

After defining the scope, reach and philosophy of a green software development project, it’s then necessary to focus in on the fine details.

How will you ensure that sustainability will be built in to all corners of the project? Perhaps you will hold regular meetings with your team to review the sustainability of each aspect of the project, or maybe even regular sustainability reports to help stay on task and keep the end goal in mind. This could also extend to working more closely with suppliers and other third parties to review their own strategies and practices. Whatever form it takes, the devil is in the details.


Measuring success

Last but not least, your green product roadmap needs to include measurables which allow you to define what sustainability ‘success’ looks like.

It’s no good making such a big effort to onboard your team, build a company philosophy of ‘green tech development’ and work tirelessly to reduce your carbon footprint without understanding what that success looks like. Set out clear and measurable goals in your sustainability roadmap, along with achievable time deadlines, and check off those successes one step at a time. Breaking it down developmentally will also allow you reset, readjust and reevaluate in an agile manner, further adding to your chances of success.


Conclusion: How to build green software development and stay competitive in the market

Working in a green-focused mindset with all team members building efficiency into every area of the software product is the key to reducing the global energy expenditure that software development inevitably contributes. On the other hand, it’s crucial that we don’t just jump into these green initiatives with both feet without thinking first.

Green product development needs to be sustainable both in terms of the environment and the viability of the product itself. It needs to be cost efficient so that you can stay competitive in your pricing when compared to the competition.

It needs to provide the same level of functionality to your main competitors (or more), as well as a high level of user satisfaction. It’s no good having a wonderfully sustainable product and company model, only for it to be expensive and perform poorly, as no one will use it. Everything needs to come together symbiotically, and this takes a group of highly focused individuals who share the same passion for sustainable software design and love for the state of the Earth. It can be done, it will be done, let’s do it.

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