AI has quickly moved from emerging technology to boardroom priority. Yet many organisations are discovering that it cannot fix poor processes, incomplete data, or unclear ways of working.
Before businesses can benefit from AI, they need to understand how their organisation actually operates today. AI can produce a presentation, analyse a spreadsheet, or create a document in seconds, but improving a complex manufacturing, finance, or supply chain operation is a different challenge.
This is where our Process Pilot tool provides a practical starting point. It reveals how work moves through the business, where data or processes are creating friction, and what should be improved before automation begins. AI only knows what your systems tell it, which is why readiness should be approached as a journey, not a product.
AI reflects the business you already have
Consider manufacturing. Imagine that a particular component has always taken 15 seconds to make and every production record shows the same 15-second cycle. When AI analyses that data, it will conclude that the component takes 15 seconds to make.
The problem is, AI has no independent way of knowing that changes to the machine, production sequence, or operating parameters could reduce the cycle to 13 seconds. Historical data tells AI what has happened, but it does not necessarily tell it what should happen.
Without guidance from people who understand the operation, AI cannot reliably identify which processes are already efficient and which have simply become accepted over time. Automating the existing process may improve consistency, but it can also embed the original inefficiency. Automating a broken process does not fix it, it just enables the same problem to happen more quickly and consistently.
We have seen a similar pattern across many forms of digital transformation. Technology is selected before the organisation has defined the problem, understood how work currently flows, or established what a successful outcome should look like. AI can accelerate a process, but speed only creates value when the process is taking the business in the right direction.
Your data estate is more complicated than it looks
Most modern organisations have enormous amounts of data. The difficulty lies in identifying which information matters, how it connects, and whether it accurately reflects what is happening across the business.
Large ERP platforms can contain thousands of interconnected data structures, making it difficult to identify which information is relevant to a particular business challenge. For example, a standard Microsoft Dynamics 365 Finance and Supply Chain Management environment contains between 15,000 and 18,000 data tables, although only 20 or 30 may matter for a specific operational question. While Process Pilot is system-agnostic, the same challenge applies across ERP environments: without specialist knowledge, internal teams should not be expected to navigate that complexity unaided.
Information also becomes less reliable when it is not maintained. Delivery times are a common example. Businesses may stop updating them consistently because the task takes time or employees have developed informal workarounds. When a delivery arrives late, everyone internally may understand why, but the system simply records another delay. AI does not know the surrounding story and treats the information as an accurate description of events.
Analysis built on incomplete, stale, or inconsistent data will be unreliable. Preparing for AI therefore involves more than cleaning a dataset before implementation. Information needs to remain accurate as operations, customers, suppliers, and market conditions change.
Governance and security must come first
AI readiness should not sit entirely within IT, although several areas require clear technical ownership. Licensing, security, access, and cost controls must be established before people are given greater freedom to interact with business systems.
AI usage can generate transaction costs through tokens, credits, and Azure consumption. Without appropriate governance, organisations can face unexpected bills as adoption grows. Access controls are even more important. A finance employee should not be able to request six months of everybody’s payroll data simply because an AI interface makes it technically possible.
Security roles and permissions must continue to apply when people interact with tools such as Microsoft Copilot. The same principle extends to Model Context Protocol servers and AI agents that can connect to systems or perform actions on a user’s behalf.
Their ability to move beyond generating information and begin acting upon it creates new opportunities, but also new risks. The necessary guardrails must be established before access is widened.
Start with the pain point, not the product
The first question for any senior leader considering AI should be: what do you actually want to achieve? “We need AI” is not a useful business objective. A better starting point is a specific operational problem that would make a measurable difference if solved.
For a finance team, that might be a month-end close that takes several weeks. For a manufacturer, it could be poor Overall Equipment Effectiveness (OEE), excessive changeover times, or inconsistent performance between production lines. Retailers may want to understand why products are being returned, while supply chain leaders may need to reduce delays or improve capacity.
Once the problem has been defined, the organisation can determine whether the answer involves AI, automation, process redesign, better use of its existing ERP system, or a combination of approaches. It also creates a baseline for measuring real business value through changes in user behaviour, productivity, process reliability, operational efficiency, and revenue impact. This ensures improvement initiatives deliver outcomes that matter, rather than simply generating activity.
External experience can be valuable at this stage. Specialists working across industries may recognise a recurring problem and understand how other organisations have addressed it. Internal teams know their own business, but they may not have access to the wider patterns, comparisons, and tools needed to identify the fastest route forward.
Moving beyond process mining’s blank slate
Most advice about AI readiness reaches the same conclusion: businesses must improve their data and processes first. Far less is said about how they should do it.
Traditional process-mining projects often begin with a blank platform. The organisation must locate and map the data, engage specialist engineers, define its processes, interpret the event logs, and build a useful operational model from scratch. It can take 12 to 18 months before the business receives meaningful insight.
That approach makes little sense when many core ERP processes are broadly repeatable. Organisations still need to purchase, order, invoice, manufacture, deliver, and close their accounts, even if the details vary.
Process Pilot removes much of this initial complexity. Its system-agnostic approach uses pre-built standard processes and established data relationships, allowing the business to begin with a mapped view rather than an empty environment.
As we explain in our article on process mining without the heavy lift, the purpose is not to present a supposedly perfect model of the business. The first valuable moment often comes when someone looks at the output and says, “That doesn’t look right.”
That response may expose inaccurate data, an unexpected process variation, or a genuine operational bottleneck. It gives the business a practical starting point for understanding what is happening, why it is happening, and what should change.
What meaningful improvement looks like
In one manufacturing environment we worked with, four production lines were operating at approximately 60 to 65% OEE. Analysis showed that certain builds performed significantly better on particular machines. By reorganising production, three lines could operate at approximately 85 to 90% OEE, while the most difficult builds were consolidated onto the remaining line.
This allowed the business to focus its most skilled resources and continuous-improvement work where they were needed most, effectively increasing productive capacity without investing in additional equipment. The result came from understanding the relationship between the builds, machines, and production plan, rather than automating the existing arrangement.
We have seen the same principle in finance. In one multinational organisation, month-end close was taking between 14 and 20 days, although the business needed to close within seven. Analysis revealed approximately ten contributing issues, including long-running scripts, server problems, and information spread across different sources. Once these were addressed, the close was reduced to five or six days.
As our article on improving finance process efficiency explores, slow close cycles are rarely caused by one dramatic failure. They are usually the accumulated result of delays, workarounds, incomplete information, and dependencies across finance and operations. AI can help identify patterns and direct attention, but improvement still depends on resolving the underlying issues.
AI readiness requires continuous improvement
Improving a process once is not the end of the journey. Any change must continue to be monitored because a faster manufacturing cycle may affect quality, place additional strain on equipment, or perform differently across products, shifts, and facilities.
Process Pilot continues working in the background, allowing organisations to measure what happens after an improvement is introduced. This supports the kind of continuous improvement that turns an initial saving into an ongoing operational capability.
As AI agents and Model Context Protocol (MCP) connections mature, they could extend this capability by monitoring performance, identifying anomalies, and highlighting further opportunities. They will still need clear objectives, reliable information, appropriate security, and people with the expertise to interpret their findings.
Engineers, finance professionals, operators, and other subject-matter experts remain essential. AI can process more information and surface patterns more quickly, but people must decide whether an apparent improvement is safe, practical, and commercially valuable.
The value of agentic AI lies in giving people better visibility and greater capacity to act while retaining human judgement and control. It should support expertise rather than attempting to replace it.
The greater cost may be doing nothing
Businesses often delay this work because they assume that preparing their data and processes will cost too much or take too long. Trying to solve everything internally can also be expensive, particularly when teams must spend months learning where information sits, how processes connect, and which tools are needed.
Specialists who already understand the systems, processes, and cross-industry patterns can reach useful insight much faster. A 5% reduction in operating costs may be enough to alter a company’s competitive position, creating room to reduce prices, increase capacity, protect margins, or invest in growth.
AI will continue to evolve, with new tools, agents, and capabilities appearing regularly. Readiness will not come from purchasing every new product, but from building the operational discipline needed to use the right capabilities well.
The cost of acting deserves careful consideration, but so does the cost of standing still while competitors learn to operate more efficiently. Organisations do not need to become AI experts overnight. They need visibility, understanding, and a commitment to continuous improvement. The businesses that build those foundations today will be best positioned to benefit from whatever AI delivers tomorrow.
Want to understand whether your data and processes are ready for AI? Talk to SNCL to learn how Process Pilot can reveal bottlenecks, data problems, and improvement opportunities across your ERP environment.
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