{"id":50872,"date":"2026-03-24T15:59:26","date_gmt":"2026-03-24T14:59:26","guid":{"rendered":"https:\/\/alistar.net\/?p=50872"},"modified":"2026-03-27T14:51:13","modified_gmt":"2026-03-27T13:51:13","slug":"from-ai-experiment-to-impact-how-to-make-process-driven-ai-truly-successful","status":"publish","type":"post","link":"https:\/\/alistar.net\/en\/blog\/van-ai-experiment-naar-impact-zo-maak-je-procesmatige-ai-echt-succesvol\/","title":{"rendered":"From AI Experiment to Impact: How to Make Process-Driven AI Truly Successful"},"content":{"rendered":"

Almost every company is experimenting with AI these days. Yet only a small number of them succeed in creating tangible business value with it. Why is that? And more importantly: what sets organizations that are truly making progress with AI apart from those that get stuck in isolated pilot projects?

At Alistar, we see every day where the difference is made. Not in the technology itself, but in strategy, processes, and people.<\/p>\n\n\n\n

Beyond the AI Hype: Why Success Hasn't Materialized<\/h2>\n\n\n\n

According to research (including studies by Gartner), only 10 to 20% of companies are successful in implementing AI. That seems surprising, given the rapid adoption of tools such as Copilot<\/a>, ChatGPT, and Claude. On an individual level, these tools often save time quickly. But as soon as organizations want to use AI to optimize business processes, the complexity increases exponentially.

A major stumbling block? The lack of a clear definition of success. KPIs for AI aren\u2019t self-evident, especially when technology disrupts existing processes and roles. Successful AI therefore requires a well-thought-out business case as well as change at the human and organizational levels.<\/p>\n\n\n\n

\"\"<\/a><\/figure>\n\n\n\n

From Experiment to \u201cFrontier Firm\u201d<\/h2>\n\n\n\n

Organizations that do get it right work step by step based on a clear vision. They evolve toward what Microsoft calls a \u2018frontier firm<\/a>\u2019refers to: companies where AI is no longer an experiment, but an integral part of daily processes and decision-making.\".<\/p>\n\n\n\n

This evolution typically occurs in three stages:<\/p>\n\n\n\n

    \n
  1. Accelerating Progress with Existing AI Tools
    <\/strong>Employees use ready-made AI solutions to perform their daily tasks more efficiently.
    <\/li>\n\n\n\n
  2. AI agents as co-pilots
    <\/strong>AI agents or \u201c    Copilot <\/a>\u201dCoworkers\" support employees by taking over or preparing work within existing processes.
    <\/li>\n\n\n\n
  3. Data-Driven Optimization and Strategy
    <\/strong>AI is used to systematically improve processes and support strategic decisions.<\/li>\n<\/ol>\n\n\n\n

    Technology follows the process\u2014not the other way around<\/h2>\n\n\n\n

    A common misconception is that new technology automatically leads to improvement. In practice, we see that the design of the process is more important than the technology itself<\/strong>. This applies not only to AI, but also to ERP<\/a>-, CRM<\/a>\u2013 and cloud solutions<\/a>.<\/p>\n\n\n\n

    True digital growth only occurs when people, data, and technology<\/strong> come together. That means: guidance, change management, and a focus on adoption. Because not every employee feels the same way about AI\u2014ranging from hesitant to overly enthusiastic.<\/p>\n\n\n\n

    The Triple E Model: Enable, Extend, Envision<\/h2>\n\n\n\n

    To provide targeted support to organizations in their AI journey, Alistar follows a three-step plan:<\/p>\n\n\n\n