Ewelina Magrel – Blog – Future Processing https://www.future-processing.com/blog Thu, 12 Feb 2026 11:13:44 +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 Ewelina Magrel – Blog – Future Processing https://www.future-processing.com/blog 32 32 AI process optimisation guide: where to use it and what to expect? https://www.future-processing.com/blog/ai-process-optimisation/ https://www.future-processing.com/blog/ai-process-optimisation/#respond Thu, 12 Feb 2026 11:08:51 +0000 https://stage-fp.webenv.pl/blog/?p=35628
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AI process optimisation guide: where to use it and what to expect?

AI can quietly transform how organisations plan, make decisions, and get work done – if you know where to apply it. This guide outlines where AI process optimisation delivers the greatest impact, what benefits you can expect, and what risks to watch out for as it becomes part of everyday operations.
Last update date: 12 February 2026
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What is AI process optimisation?

Business process optimisation is about understanding how work flows through a business, spotting friction, and refining tasks to make operations faster, smoother, and more accurate. Adding AI accelerates that cycle: machine learning algorithms, natural language processing, and generative models can analyse operational data at scale, propose improvements, and automate repetitive or judgment-light steps.

A common approach pairs process mining with AI. Process mining reconstructs how work actually happens (not how it’s drawn on flowcharts), exposing bottlenecks and rework. AI then tackles those pain points – automating tasks like data extraction, routing, validation, or summarisation, and supporting decisions that used to require human time and attention.

The real draw isn’t just operational efficiency – it’s adaptability. AI can surface insights within minutes in mature data environments, monitor processes in near real time, and help teams reallocate capacity toward higher-value work. To get those benefits, however, organisations need clean data, solid architecture, and human oversight; without them, even advanced models may under-deliver.

Done well, AI process optimisation means reduced errors, better decision making, boosted productivity, and alignment of operations and business processes with strategic goals.

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Which types of business processes are good candidates for AI optimisation?

Artificial intelligence tends to shine in processes that are high-volume, repetitive, and fuelled by structured or semi-structured data.

Think customer support queues, claims and collections workflows, onboarding journeys, compliance checks like KYC/AML, supply chain operations including order and inventory management, and logistics coordination, pricing and forecasting cycles, or back-office case handling. These areas involve clear rules, predictable patterns, and large transaction counts – conditions that make process optimisation and automation both viable and valuable.

Work that relies heavily on emails, documents, tickets, or chat conversations is also ripe for improvement because AI is unusually good at making sense of unstructured information. It can analyse purchase data, customer behavior, extract data from PDFs, route messages, summarise cases or customer feedback, or draft responses without requiring manual intervention. The result is faster turnaround, fewer errors, and more time for humans to focus on exceptions, creative decisions, and complex customer needs.

How can AI process optimisation help businesses?

AI systems improve the way work is seen, measured, and executed. By analysing operational and historical data, they reveal how processes run in reality – highlighting bottlenecks, handoffs, and repetitive tasks that slow everything down.

They can also:

  • accelerate research and development cycles
  • optimise resource allocation
  • cut rework and wait times
  • improve service interactions and case resolution

On the predictive side, AI models can forecast incoming volumes (e.g., orders, claims, or support calls) so teams can plan capacity, avoid firefighting, and meet SLAs more reliably.

The result isn’t just efficiency – it’s better customer experience, better operational stability, and more room for humans to handle creative, judgement-rich, and relationship-driven work.

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What business benefits can we expect from AI process optimisation?

Beyond the technical novelty, AI process optimisation delivers value because it improves how work flows, how decisions are made, and how both customers and employees experience the organisation. When AI is embedded into operational processes rather than treated as an isolated experiment, benefits become tangible, measurable, and scalable.

Typical business outcomes include:

Lower handling time and operating cost

Automated data extraction, routing, and decision support reduce manual effort and rework.

Fewer errors and better compliance

AI reduces human slip-ups, enforces rules consistently, and helps organisations adhere to regulatory requirements.

Faster case resolution and higher throughput

Operational bottlenecks shrink and queues move faster, improving SLAs and conversion rates.

Improved customer experience

Shorter waits, more accurate responses, and proactive service boosts satisfaction and retention.

Better resource allocation

Forecasting models predict incoming volumes (e.g., orders, claims, calls), enabling smarter capacity planning and less firefighting.

Higher employee productivity and engagement

Repetitive tasks shift to machines, freeing people to focus on exceptions, sales, advisory work, and relationship-building.

Additional advantages of a process-driven approach to AI include:

  • Easier implementation & adoption: Integrating AI into existing workflows shortens deployment cycles and avoids costly standalone projects.
  • Built-in structure and oversight: Processes give AI clear goals, escalation paths, and governance, ensuring it complements rather than replaces human judgement.
  • Better data quality and access: Operational workflows feed AI cleaner, real-time data while maintaining privacy and regulatory constraints.
  • Enhanced safety and risk management: Approval steps, audit trails, and human-in-the-loop controls reduce systemic risk and keep the technology accountable.
  • Measurable ROI and continuous improvement: Performance can be tracked across each activity, allowing organisations to refine models and optimise business processes over time.
  • Scalable enterprise adoption: Once proven in one workflow, the same patterns, tooling, and controls can expand to others, turning pilots into portfolio-level gains.
Benefits of AI in digital transformation

What are the main risks and challenges we should be aware of?

AI process optimisation is powerful, but it isn’t frictionless. The biggest challenges tend to show up where data, trust, regulation, or change management intersect. Tackling them early makes adoption far smoother.

Key risks and how to mitigate them:

Poor or inconsistent data quality

AI technology struggles when the data feeding it is incomplete, outdated, or misaligned.

To remedy, invest in data cleaning, metadata standards, and monitoring. Also, establish ownership for critical datasets and validate inputs continuously.

Opaque or non-explainable models in regulated decisions

Black-box predictions can be unacceptable in areas like credit, healthcare, or compliance.

To remedy, use explainable AI techniques, set transparency requirements upfront, and apply human-in-the-loop approval for high-stakes decisions.

Over-automation of sensitive or judgment-heavy steps

Automating too aggressively can damage customer trust or create ethical and legal exposure.

To remedy, define clear boundaries for automation vs. assisted decision-making, escalate edge cases to humans and regularly test for unintended consequences.

User resistance and low trust in artificial intelligence outputs

If frontline staff don’t believe the AI, they won’t use it, killing adoption before benefits materialise.

To remedy, co-design solutions with users, provide clarity on “how it works,” surface confidence scores, and show evidence of performance improvements.

Governance and accountability gaps

Without rules, roles, and logs, AI can drift, degrade, or make ungoverned decisions.

To remedy, establish model governance, audit trails, performance reviews, and escalation paths. Align with internal risk and compliance functions.

Security and privacy concerns, especially in the cloud

Handling personal or confidential data introduces regulatory, contractual, and cyber risk.

To remedy, apply robust security controls, data minimisation, encryption, and privacy-by-design principles. Also, ensure vendors meet regulatory and industry standards.

Addressing these challenges early creates a stronger foundation for adoption and helps ensure AI becomes a trusted, high-performance part of everyday operations rather than a technical curiosity.

How do we ensure AI optimisation doesn’t just create "shadow processes"?

One of the most common pitfalls in AI process optimisation is simply layering AI onto legacy workflows without rethinking how work should flow from start to finish.

When this happens, organisations often end up with shadow processes: informal workarounds, parallel spreadsheets, and manual checks that quietly undermine the very efficiencies AI was supposed to deliver. These hidden processes not only erode productivity but also make compliance, auditing, and performance measurement far more difficult.

To prevent shadow processes and make AI process optimisation truly effective, organisations should take a deliberate, process-first approach. To do so, take into account the following steps:

Redesign the process intentionally

Don’t just “bolt on” AI. Map the workflow end-to-end, identify friction points, and redesign steps to ensure AI complements the work rather than simply digitising old habits.

Update SOPs, training, KPIs, and controls

Employees need clear guidance on the new way of working. Align standard operating procedures, performance metrics, and internal controls with AI-driven processes to ensure consistency, accountability, and measurable outcomes.

Retire redundant steps and manual workarounds

If AI has automated or simplified a task, remove any leftover manual checks or duplicate processes. Otherwise, employees may revert to old behaviours, negating potential gains.

Clarify handoffs and ownership between humans and AI models

Define exactly where AI ends and human responsibility begins. Clear ownership reduces confusion, prevents duplicated effort, and ensures exceptions or complex cases are handled correctly.

Communicate changes widely and clearly

Keep teams informed throughout the transition. Explain the reason behind changes, demonstrate how AI will support their work, and provide ongoing support and training. Visibility and transparency build trust and adoption.

When these steps are taken thoughtfully, the "new way of working" becomes the standard, embedding AI into operations without creating hidden, unmonitored processes. Teams can then fully realise efficiency, accuracy, and strategic benefits, rather than fighting shadow systems that quietly eat into productivity.

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FAQ

How is AI-driven optimisation different from traditional process improvement?

Traditional improvement relies heavily on workshops, manual analysis, and static rules. AI, combined with predictive analytics, can continuously learn from real data – transactions, logs, and conversations – to detect bottlenecks, anticipate potential issues, and recommend or even take actions in real time. This transforms optimisation from a one-off project into an ongoing, data-driven capability that evolves as the business does.

You need access to process data (events, timestamps, case IDs), transactional data (orders, claims, tickets), and – where relevant – unstructured data (emails, documents, chat logs, voice transcripts). Clean, well-linked data across systems makes it easier to map the real process, train models and measure impact.

You should define clear policies on where AI can and cannot make decisions, use human-in-the-loop for high-impact cases, document models and data sources, and log key AI-driven decisions. Involve risk, legal and compliance early, and create review processes for monitoring bias, errors and model drift.

You typically need a cross-functional team: process owner, business SMEs, data scientists/machine learning engineers, automation engineers (RPA/workflow), and change management specialists. A central AI or automation team can provide platforms and standards, but business domains must own the outcomes.

For well-chosen, focused use cases, organisations often see early benefits within a few months – especially in areas like routing, triage, classification, and assistant-style tasks. Integrating AI into business process management allows these gains to be tracked, measured, and optimised quickly. Deeper transformation of complex, cross-functional processes takes longer, but when rolled out incrementally, it can deliver step-change improvements across the organisation.

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]]> https://www.future-processing.com/blog/ai-process-optimisation/feed/ 0 GitHub Copilot speeding up developers work by 30% – a case study https://www.future-processing.com/blog/github-copilot-speeding-up-developers-work/ https://www.future-processing.com/blog/github-copilot-speeding-up-developers-work/#respond Tue, 16 Apr 2024 09:12:18 +0000 https://stage-fp.webenv.pl/blog/?p=29007 In the contemporary business landscape, enterprises continually seek avenues to optimise value delivery while effectively harnessing all available resources.

What is GitHub Copilot?

Before we go any deeper, let’s check what GitHub Copilot – a powerful AI coding support tool developed through a partnership between GitHub and OpenAI – really is.

GitHub Copilot functions as an AI companion programmer, providing autocomplete-style suggestions for writing code.

The suggestions and code snippets can be accessed by either initiating the code or by crafting a natural language comment outlining the intended functionality.

GitHub Copilot’s primary function is to interpret users’ intentions expressed in natural language and translate them into code, making it comprehensible for computers. Additionally, it possesses the capability to translate code across various programming languages.

Exploring market trends related to Generative AI

When it comes to Generative AI, at Future Processing we focus on exploring the possibilities of using it in various tasks and internal processes, while continuing to gather experience in commercial projects.

We constantly test our solutions and their effectiveness, we also draw conclusions and learn how to best use the potential of artificial intelligence for us and our clients.

Summary of a Future Processing project of implementing GitHub Copilot for commercial projects

Last fall, we started implementing GitHub Copilot for commercial projects across the company.

It was introduced only to the design teams and clients that were willing to use the tool. After some time, we asked what our developers thought about the tool and how it impacted their work. Here’s what we found out!

  • 43% of people from project teams use Copilot on a daily basis.
  • 30% of them have already passed the so-called verification after 3 months of use, and
  • after the trial period 80% of people decided they still wanted to use it.

Our findings are presented in the graphs below.

When writing new code Copilot increases the speed of work by 34%, while when writing unit tests, it does so by 38%. As much as 96% of developers said Copilot speeded up their everyday work.

the percentage increase in speed while using GitHub Copilot
GitHub Copilot speeding up developers work

It’s good to remember that Copilot will not save the same amount of time in every task, and it will not work in the same way for every job you do, but with properly selected tasks it can help saving as much time as our developers indicated in the survey.

When asked detailed questions, our developers agreed Copilot made them:

  • more productive,
  • allowed them to complete repetitive tasks faster,
  • made them feel less frustrated during their coding sessions.

It looks like Copilot helped them do the tasks which are most tiresome and repetitive, unlocking their time for real creativity and troubleshooting.

how helpful is GitHub Copilot
GitHub Copliot developers survey

Migrating from Angular to React: 40% time savings with AI – a case study

One of our programming teams faced the challenge of migrating a small application from the Angular framework to React.

Looking for ways to optimise and speed up this process, they turned to GitHub Copilot.

When initiating the migration process, the team began with a detailed analysis of the requirements and structure of the current application code based on the Angular framework.

The analysis showed that Copilot is an excellent tool for porting components between technologies, especially due to the large amount of boilerplate code to write.

The team of experts developed a set of standards that the code generated by Copilot had to meet. This important information was used to create a prompt, easy to run in a chat window inside the IDE, opened in the context of the selected component. The code generated in this way could be easily copied to the appropriate component in a React-based application.

Thanks to these activities, the component migration process became more effective, thus accelerating the development of the project.

The entire migration was a success, confirmed by the positive results of a satisfaction survey conducted among the team.

Its results relating to the experience of using GitHub Copilot and the effectiveness of its support in the migration process can be summarised as follows:

GitHub Copilot is a revolutionary tool that speeds up the process of moving applications between libraries. As the task of transferring an application from Angular to React shows, Copilot can automatically rewrite the entire boilerplate of code, allowing the team to focus on business logic and styling.

Thanks to this, teams are able to save up to 40% of time when switching from one technology to another.

Although it may happen that the tool suggests a solution requiring additional work, it is usually helpful and speeds up the process.

Thanks to Copilot, even people who have no knowledge of React can easily understand what is happening in the code. The satisfaction of teams using the tool is at the level of 4-5 out of 5 points.

Copilot for Microsoft 365 – internal tests

Some time ago we had the opportunity to test a new service from Microsoft, intended to make our work easier and more efficient on various platforms and devices.

Copilot for Microsoft 365 is an intelligent assistant that utilises artificial intelligence and our data from the Microsoft 365 ecosystem to assist with daily tasks, including organising meetings, writing documents, creating presentations, and facilitating team collaboration.

Copilot integrates with applications such as Teams, Outlook, Word, PowerPoint, Excel and SharePoint and provides helpful suggestions, tips and features based on our needs and preferences.

In our experience, Copilot in its current state has many advantages, which we listed below:

  • It can analyse the content of messages and documents and provide us with relevant information, e.g. meeting dates, related documents and email threads. This is very useful when we want to quickly find something related to our topic.
  • It allows to create automatic meeting summaries that include the most important points, tasks and decisions. This is a great way to increase the efficiency and transparency of conversations. Additionally, you can “talk” to the recording of such a meeting asking additional questions or asking it, for example, to write user stories based on what was discussed. However, it isn’t flawless, and some effort is still required to refine it to the desired level.
  • In mobile Teams, it can summarise what has happened in the thread since the last message we read. This is very helpful when we want to quickly understand the situation and join the discussion.
  • It helps create documents in Word.
  • Similarly, it is available in Outlook (only web-based for now).

We came to the conclusion that Copilot for Microsoft 365 is a promising tool that can significantly help organise work and collaboration within a team, but to become a truly useful and effective solution it still requires improvements and extensions.

The implementation of the Polish language is planned for March/April. We will then see if it does a better job of recognising and generating the text we work with every day.


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