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Why AI Programmes Stall — and How Process Mining Helps Them Scale

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Guest article by Gary Chitan, Founder & Principal Consultant at TOCH Consulting

This article examines how addressing process-level challenges early helps organisations keep AI initiatives moving and scale them into sustained business value.

Artificial Intelligence initiatives often start with momentum and ambition. Yet many organisations struggle to maintain pace beyond early pilots. The challenge is rarely a lack of technology or ideas. More often, AI programmes slow down because complexity accumulates faster than it can be resolved.

Why AI programmes lose momentum

Common causes are familiar to many organisations. Data landscapes are fragmented, with transactional data spread across systems and teams. Process definitions are unclear or based on assumptions rather than how work actually happens. At the same time, competing priorities across IT, data, and operations make it difficult to address foundational issues without disrupting ongoing delivery.

As these issues build, AI programmes become cautious. Use cases remain stuck in pilot phases, confidence in model outputs erodes, and leadership hesitates to scale investment.

The challenge across industries

This pattern appears across industries.

In manufacturing, AI use cases such as predictive maintenance or quality analytics struggle when operational data varies significantly between sites. In financial services, AI initiatives slow when transactional data from multiple platforms cannot be aligned into a single, end-to-end process view. In supply chain and planning functions, forecasting and optimisation models underperform when regional process variation introduces noise into training data.

Despite different contexts, the underlying problem is the same: AI depends on stable, well-understood processes, yet those processes are often neither visible nor controlled.

A practical way to keep AI programmes moving

One effective way to address this challenge is to embed a specialist support unit alongside the core AI team.

This unit operates with a narrow mandate: to resolve complex, high-risk issues that would otherwise delay progress. It works in small, experienced teams, integrates closely with AI, data, and operational functions, and focuses on precision rather than scale. Crucially, it allows the main AI programme to continue at pace while complexity is addressed in parallel.

Why process mining is central to this approach

A defining feature of this specialist unit is the inclusion of dedicated process mining expertise.

Process mining specialists combine a deep understanding of transactional data, business process behaviour, and analytical modelling. Their capabilities span process discovery, conformance analysis, root cause analysis, KPI definition, and the interpretation of process variation in the context of AI model performance. Just as importantly, they act as a bridge between data science teams and the business, translating complex process behaviour into actionable insights that AI teams can use immediately.

Turning data into factual process insight

Process mining plays a central role in enabling this capability.

UsingQPR ProcessAnalyzer, QPR Software’s process mining platform, the specialist unit reconstructs real business processes directly from transactional data, providing a factual view of how work actually flows through the organisation. This replaces assumptions and workshop-based models with evidence, making it possible to pinpoint where variation, bottlenecks, rework, and non-standard behaviours undermine AI outcomes.

The benefits are immediate. Process models are generated automatically from existing data, allowing AI use cases to be validated in weeks rather than months. Flexible deployment options, including Snowflake-native and on-premise environments, ensure analysis can take place within governed data landscapes without unnecessary replication. Most importantly, organisations can isolate the specific process behaviours that drive cost, delay, and model error, focusing AI investment where it delivers measurable return.

From ambition to sustained value

Keeping an AI programme moving is not about accelerating everything at once. It is about removing uncertainty, resolving complexity early, and enabling confident decisions.

Specialist support units, grounded in process mining expertise and enabled by tools like QPR ProcessAnalyzer, provide the clarity, focus, and speed needed to move AI programmes from ambition to sustained business value.

Struggling to scale your AI programmes?
Let’s explore what your processes really look like – and where AI value is being lost.
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About the author: Gary Chitan
Founder & Principal Consultant, TOCH Consulting

Gary Chitan is a business and digital transformation leader with 20+ years’ experience scaling technology ventures and driving commercial growth across the UK, EMEA, and the US. Having led senior roles in data management, AI, and process intelligence, he founded TOCH Consulting to help organisations turn process and data transparency into measurable operational excellence.

Read more: www.tochconsulting.co.uk

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