AI process optimization – from bottleneck to blueprint in 6 steps

AI process optimization – from bottleneck to blueprint in 6 stepsAI is the driving force behind modern process optimization. Organizations that digitize their operations and deploy AI for process improvement build agility, efficiency and strategic advantage. Professor Dr. Sam Solaimani — Professor of Digital Technology, Innovation & Operations Management — delivers a clear message: digitization affects not only the infrastructure of organizations, but their core business processes down to the very core. In this article he outlines six essential steps by which organizations can turn bottlenecks into a future-proof blueprint — with AI as the engine of sustainable process optimization.

Five technological trends in process management

According to Solaimani, five technological trends currently dominate the domain of operations and process management:

Generative AI and autonomous agents — while automation previously focused on reducing manual work, we now see systems that learn themselves and make decisions.
Explosive growth of data — organizations increasingly combine structured and unstructured data sources to gain better insights.
Real-time information — working with retrospective reports is less effective when you can see what customers, patients or citizens are doing right now.
More complex and widely applied AI-algorithms — legislation and ethics are often still catching up; the questions are not only what technology can do, but also what it may do.
Improved technological infrastructure — cloud computing, better connectivity and more reliable security and monitoring enable unprecedented scalability, flexibility and reliability.

Six steps toward AI-driven process optimization

Using data from across multiple layers of a chain, one can build an integrated view of demand and capacity. By coupling AI and predictive models to intake trends and available resources, organizations can forecast better, utilise capacity more efficiently, and reduce vacancies (for example, in reception centres). However, geopolitical developments, political decision-making, data quality and organizational structures and agreements proved at least as influential as technology.

From this case and earlier projects, Solaimani draws one clear lesson: to deploy AI effectively for process improvement, six interrelated steps are needed:

Holistic approach: from process to value stream

Data-driven decision-making, planning and management — especially with AI — only works if you understand the entire system end-to-end, not just individual departments.

Chain-wide agreements and data sharing

Organizations in chains must agree on data standards and responsibilities. Only by bundling and harmonizing data across all parties can advanced analytics be applied effectively to make better collective decisions.

Data quality and governance

Poor data quality hinders data-driven work. Often organizations lack standardization, data governance, and integration. Without those, data remains unreliable, leading to inconsistent decisions and inefficient resource use. Effective data governance requires multidisciplinary collaboration: technology, infrastructure, finance, and compliance must work together.

Ethical frameworks and public responsibility

In the public domain, ethics and compliance go hand in hand. Organizations need a clear ethical compass for privacy, discrimination, transparency, accountability and human dignity. Because technology often develops faster than policy, it’s essential not to wait but to act proactively.

Skills and digital culture

Digital transformation requires not only new technical skills, but also a cultural shift toward experimentation, iterative learning and willing risk-taking. Frontline staff often have the richest insight into operational problems and potential solutions — bottom-up input and knowledge sharing are essential. Effective leadership must therefore create space for that.

Digital leadership

Digital leadership means leaders not only care about culture, but actively guide digital change. That involves clear decisions: who decides about data and technology? Who monitors quality, ethics and progress? In practice this means steering groups, regular audits of process performance, and clarity about accountability if something goes wrong. This is particularly important in the public sector.

Paradoxes and dilemmas of digital transformation

These ingredients form a blueprint for organizations and chains seeking to use AI properly. In his teaching at Nyenrode, Solaimani emphasizes that the six steps are not a linear path; they bring tensions and trade-offs — for example: central data policy vs. decentralized autonomy, fast optimization vs. fundamental redesign, ethical oversight vs. space for innovation.

There is no universal solution. Each organization must consciously decide where it stands, depending on its culture, risk tolerance and regulatory environment. As Solaimani says: “What is true, though, is this: organizations that ignore these tensions push innovation aside. Organizations that allow all risks, undermine trust. The art is not to find the perfect balance, but to make these dilemmas explicit, learn to deal with them, and continuously adjust.”