The real challenge in AI is not running a pilot, but putting it into practice
For pilot projects to translate into business value, AI needs to be considered together with processes, decision-making mechanisms, and human involvement.
As AI investments continue to grow rapidly, many organizations are struggling to turn these investments into measurable business outcomes. Companies are developing numerous pilot projects and experimenting with new models and technologies. Yet a significant proportion of technically successful projects fail to become embedded in real business processes and generate the expected economic value.
Gartner research shows that only 28% of AI use cases achieve the expected return on investment, while 20% fail completely. In addition to data quality and skills gaps, a lack of organizational transformation is emerging as a key factor behind these failures. The fact that only 19% of organizations consider themselves AI-ready in terms of both their workforce and organizational structure demonstrates that technology investments alone are not enough.
AI has now moved beyond the technology agenda to become a fundamental component of organizations’ growth, productivity, and competitive strategies. Yet increasing investment does not necessarily translate into proportional business value. Many organizations are taking their AI projects to the pilot stage but struggle to integrate them into real business processes. In the AI world, this situation is often referred to as “Pilot Purgatory”—the inability of projects to move beyond the pilot stage and achieve operational-scale adoption.
Recent research points to a common pattern in the reasons why AI projects fail. Even when projects work technically, moving from pilot to scaled deployment becomes difficult when there is no clear connection to business objectives, success criteria and KPIs are not defined from the outset, executive sponsorship is lacking, or alignment between business units and IT is not established. The increasingly used concepts of “context-learning gap” and“workflow misalignment” also describe the challenges of AI systems failing to adequately understand an organization’s business context and solutions failing to fit into real-world workflows.
One of the fundamental challenges in AI projects is that technology is often considered before business transformation. Companies frequently start by asking what they can do with the data they have or which AI tool they should use. The right starting point, however, should be to first determine which business outcome we want to change, then identify which decisions need to be improved to achieve that outcome, and finally determine which processes need to be transformed. Data and technology selection should come afterwards.
The Pilot Trap Starts with Disconnecting AI from Business Processes
One of the key reasons AI projects get stuck in the pilot stage is that technology is approached separately from real business processes and decision-making mechanisms. A technically successful pilot, by itself, does not constitute a successful AI investment.
Automating an existing workflow with AI, without changing the workflow itself, does not necessarily create transformation. If there is an inefficiency in the existing process, automating it with AI without addressing the underlying issue often means simply running the inefficiency faster.
The real value emerges when we redesign workflows and decision points around the new capabilities enabled by AI.
We Need to Talk About Decisions First, and Institutional Analytical Knowledge Next
AI requires companies to move from a data-centric perspective toward a decision-centric approach.
When a company’s business rules, processes, constraints, and cause-and-effect relationships are not incorporated into the environment in which AI operates, even powerful models can struggle to understand the organization’s actual business context.
This is where decision intelligence, agentic AI, and AI governance need to be considered together.
Decision intelligence establishes the discipline of moving from data to decisions. Agentic AI enables those decisions to be translated into action and orchestrated across processes. AI governance ensures that this power is used in a controlled, traceable, and accountable manner. When these elements are not addressed together, the potential of AI projects to reach production and deliver sustainable value is reduced.
For AI to create real value, particularly in analytical applications, access to enterprise data alone is not enough. The system also needs to understand the analytical knowledge the organization has accumulated over the years: how metrics are interpreted, business rules, decision criteria, and exceptions.
In an organization, what is considered the right outcome—or how a particular metric should be interpreted under specific circumstances—is not always contained within the data itself. This knowledge resides in the institutional memory built over years by analysts, business experts, and executives.
If we give AI only the data, we leave a significant part of this analytical memory behind. Business rules, exceptions, and decision logic also need to be explicitly transferred into the system.
The Difference Between a Pilot and Production: Not Working Once, but Continuing to Work Reliably
Another critical issue in moving AI pilots into production is the ability to measure how the system behaves under real-world conditions.
A controlled pilot can produce successful results using selected data and scenarios. In a production environment, however, the system must also operate reliably under unforeseen inputs and conditions.
A demo only needs to work once. A production environment needs to work continuously under conditions that cannot always be controlled. The fundamental challenge here is not only the intelligence of the model, but also a trust gap.
The Role of Humans and AI Needs to Be Redesigned
As AI becomes embedded in real business processes, organizations also need to redefine how responsibilities and decision-making are shared between humans and AI.
The purpose of AI should not be to remove humans from the decision-making process. It should enable people to make more, faster, and higher-quality decisions.
People need to understand why the system has made a particular recommendation, know under which conditions they can trust it, and be able to intervene when necessary.
Competitive Advantage Will Come from the Speed of Moving from Data to Action
Companies are increasingly gaining access to similar AI models and growing volumes of data. The key differentiator in the future will be how effectively organizations can combine data with their analytical knowledge, business context, and decision-making mechanisms and turn it into the right action.
Access to more data and more powerful models alone will not constitute a sustainable competitive advantage.
The differentiator will be how quickly a company can transfer its own business logic, analytical knowledge, processes, and experience into these systems and turn the right decision into action.
Before starting an AI project, companies should ask themselves:
- Which business outcome do we want to change?
- Which decisions do we need to make better to achieve this?
- How should we transform the processes connected to these decisions?
- How will we transfer our analytical knowledge, business rules, and exceptions into AI?
- How will we continuously measure whether the system is working correctly?
- How will we divide responsibilities between humans and AI?
- And only then: What data and technology do we need?
The real challenge in AI is not running a pilot, but putting that pilot into practice. The way forward is to start not with technology, but with business value, decisions, and processes—and to transform an organization’s knowledge into a structure that AI can understand and use.
Turning AI into enterprise value will depend not only on knowledge of models and tools, but also on the software, analytical, and industry expertise accumulated over the years.
OBASE AIR combines OBASE’s more than 30 years of technology and industry experience with AI, enabling organizations to transfer their own business knowledge, decision logic, and processes into AI systems—and to move from data to the right decision and from decision to action faster, more reliably, and in a controlled manner.