Modern businesses are all racing to transform their business processes with AI—nearly every organization we meet at iGrafx has it on their roadmap. This should come as no surprise: the opportunity is enormous, not just to make processes faster or cheaper, but to make them smarter, more adaptive, and ultimately more valuable.
That said, AI without an accompanying strategy is a recipe for wasted time and money. Before jumping in, leaders must ask: What outcomes are we trying to achieve? How will we measure success? What should our processes look like after AI is applied?
The reality is that most transformation projects fail for one of two reasons (or both). Either:
- The wrong goals were chosen, or
- The results weren’t measured effectively
This is where process intelligence becomes essential, providing the clarity, context, and governance to ensure AI delivers real, measurable value.
Why Process Mining Alone Isn’t Enough
Many companies have turned to process mining to provide a baseline for how their operations are running. They uncover how as-is processes are being executed across the organization by examining system logs from enterprise applications. This offers visibility into processes and can also help reveal inefficiencies: processes that take too long, include a lot of rework, or are impacting customer satisfaction.
But in practice, this is not enough to ensure successful transformation. What is missing from process mining is context. These are some key questions that simply can’t be answered through process mining:
- How were your processes intended to work?
- What are the interdependencies between processes?
- Who is responsible for the process?
- What regulatory requirements and controls need to be met?
- What business strategy does this process support?
The Shift: From Mining-Led to Model-Aware Intelligence
What industry analysts are indicating, and what we at iGrafx are seeing, is a massive shift from mining-led to model-aware process intelligence.
Process mining looks at system logs to tell you what happened at a specific point in time – you shipped something, created an invoice, delivered goods, etc. While it can, for example, help you see there was a major delay between charging the customer and checking the inventory, it doesn’t tell you that the intended process was designed to check inventory before charging the customer. With the help of model-aware process intelligence, you can fundamentally change the way the process is executed to follow the intended steps, as opposed to removing the delay between two steps that will only result in unhappy customers.
A model contains metadata about the process – roles and responsibilities, policies and risks, relationships, strategies, requirements, etc. Starting with a model and then augmenting it with process mining offers the deep context needed to understand and improve your processes – including the ability to quantify and monitor the impact of your changes.
What is Model-Aware Process Intelligence?
Model-aware process intelligence integrates process mining, modeling, simulation, and predictive analytics, all connected by a ‘single source of truth’ process repository. Process mining provides the execution insights, process modeling provides the context and the governance, and simulation and predictive analytics provide the foresight to make informed decisions and uncover issues before they occur.
Simulation also allows teams to perform what-if scenarios that tell you the positive and negative impact of a change, without any risk or upfront investment. For example, how will my process perform differently if I deploy AI, add more resources, or implement a new piece of machinery? This takes the guesswork out of process change, ensuring your strategy will deliver the expected results before deploying it into production.
At iGrafx, we rely on a proven 3-phase approach: Discover, Design, Optimize. Discover your existing processes through process mining or with Pia (our GenAI Process Intelligence Assistant for SOP upload), design ideal future processes that comply with regulations and internal policies, and then continuously optimize to maximize operational performance aligned with strategy.
Where many companies misstep is when they skip the design phase and go straight from discovery to optimization. Model-aware process intelligence supports a better, safer transformation, embeds compliance within processes, and aligns strategy to execution with the ability to measure performance against your models. It’s not realistic to do a process mining project and then expect to save $10M by eliminating inefficiencies (through ad hoc fixes, automation, or AI). Model-aware process intelligence is about closing the loop: moving to measurable, outcome-based use cases and demonstrating real business value.
The Process Repository Advantage
A process repository provides a powerful foundation for organizations by serving as the single source of truth for all process components. It ensures that models enriched through process mining are captured with their full business context, creating a complete and accurate view of how work gets done. Beyond documentation, the repository connects ownership, risks, compliance requirements, and strategic objectives, aligning processes with business goals.
This centralized structure also enables safe change and thorough impact analysis, giving teams confidence when improving or transforming operations. Most importantly, a process repository is essential for unlocking AI-driven initiatives and ensuring sustainable transformation, as it delivers the clarity and governance needed to scale innovation responsibly.
You can learn more about the iGrafx process repository here.
Why AI Demands Model-Aware Process Intelligence
AI must be grounded in governed process intelligence
AI delivers the most value when it operates on a foundation of accurate, governed process intelligence. Without visibility and governance, AI risks amplifying inefficiencies or creating compliance gaps instead of driving transformation.
Repository ensures compliance, ownership, and context
A centralized process repository provides the single source of truth that AI needs to function effectively. By linking compliance requirements, ownership, and business context, it ensures that AI outcomes align with organizational priorities and regulatory standards.
Pia auto-builds models from SOPs or conversations
With Pia (iGrafx’s Process Intelligence Assistant), organizations can accelerate their journey by automatically generating process models from standard operating procedures or even simple chat conversations.
Model-aware AI = orchestration, automation, agentic processes
When AI is model-aware, it doesn’t just analyze data, it can orchestrate workflows, automate tasks, and enable truly agentic processes. This level of intelligence turns AI into a driver of efficiency and adaptability, rather than just another tool.
Enables safe, practical AI adoption today
By grounding AI in process intelligence, companies can adopt it safely and pragmatically without fear of unintended consequences. This approach builds confidence, mitigates risk, and ensures AI adoption delivers real business value from day one.
Key Takeaways
Let’s summarize what we’ve covered regarding the role model-aware process intelligence can have in your organization:
- Transformation is more than simply making slight improvements to what you do today; it’s about fundamentally changing how you run your business—reimagining and rethinking what’s possible given the technology landscape available today.
- A process repository provides the context, governance, data, and models needed to improve process performance in ways that can be measured and continuously optimized.
- Model-aware process intelligence is the proven path to AI-readiness.Without this foundation, AI projects risk costing a lot and delivering little.
If your organization is focused on reducing risk, boosting efficiency, cutting costs, and ensuring compliance, connect with iGrafx to discover how we can help.