By As companies continue to increase investment in artificial intelligence, Kamal Yadav, Principal Data and Insight Analyst at Brambles, believes one of the most common mistakes organizations make is treating technical improvement as proof of AI success. Higher model accuracy, better data quality scores, faster processing, and newly delivered dashboards may all indicate progress, but they do not necessarily show that AI has created measurable value for the business.
According to his perspective, executive leaders evaluate AI differently from technical teams. Boards, CFOs, and commercial decision-makers are not only asking whether a model has improved. They want to understand whether AI has helped increase revenue, improve margins, reduce operational risk, strengthen customer experience, speed up decision-making, or release employees from repetitive work so they can focus on higher-value activities.
This difference is becoming more important as organizations move beyond individual AI pilots and begin scaling AI across the enterprise. Many companies can demonstrate that a model performs better in technical terms, but fewer can clearly explain how that improvement has changed business performance. This is where data and AI leaders need to change the conversation.
An improvement in forecast accuracy, for example, matters at the executive level only when it is connected to outcomes such as lower inventory costs, better service levels, fewer stockouts, or improved working capital. In the same way, better data quality becomes commercially meaningful when it leads to fewer failed orders, fewer customer issues, faster sales execution, or more dependable decision-making.
The key point is straightforward: technical progress alone does not equal business impact.
AI Value Often Escapes Through Operational Weakness
Many AI initiatives lose value not because the underlying technology is poor, but because the business environment around the technology is not prepared to support it. Organizations may invest significantly in AI models, cloud infrastructure, automation tools, and analytics platforms while still facing weak workflows, unclear accountability, inconsistent processes, and limited user trust.
When this happens, AI investment can behave like water poured into a leaking container. Resources are committed, but value slips away through gaps in operations.
A flawed process cannot be transformed simply by automating it. If a workflow is inconsistent, automation may only increase the speed of inconsistency. If the data is unreliable, employees may question AI-driven recommendations. If ownership is unclear, decisions may still be delayed. If users do not trust the system, adoption will remain limited regardless of how well the model performs technically.
This issue is becoming even more visible as organizations adopt generative AI and agentic AI at speed. Many businesses are building intelligent assistants and automated workflows, but they are not always redesigning the processes, governance models, and decision rights needed to support them. The result can be costly automation placed on top of already fragile business processes.
This is one reason many AI pilots create early excitement but struggle to scale. A pilot may work well, the technology may appear promising, and leaders may show interest. However, when the solution is introduced into the wider business environment, the surrounding process may not be mature enough to absorb the change.
In many cases, the biggest barrier is not the algorithm itself. It is the operating model that surrounds it.
Foundational Capabilities Must Grow With AI Delivery
Another major challenge is the tendency to separate visible AI use cases from the foundational work required to sustain them. Some organizations pursue quick AI wins while assuming that data quality, governance, process design, and ownership issues can be addressed later.
In reality, postponing that foundational work often becomes a reason AI initiatives fail to scale.
A more practical approach is to avoid both extremes. Organizations should not delay all AI activity until every data foundation is perfect. At the same time, they cannot ignore the capabilities that make AI reliable, trusted, and scalable.
Strong AI strategies build foundations and use cases together. For example, a pricing recommendation engine may create immediate commercial benefit while also revealing gaps in product master data. Rather than treating those gaps as a separate technical cleanup project, the pricing use case can provide a clear business reason to improve the underlying data foundation.
This makes foundational investment easier to justify because it links data quality, governance, semantic layers, workflow design, and ownership directly to business outcomes. These are not just technical architecture concerns. They become practical enablers of revenue growth, risk reduction, operational consistency, and customer trust.
Organizations Should Measure Net Value, Not Only Generated Value
AI value should be measured in terms of net impact, not only the value a system appears to generate.
Many AI business cases focus on positive outcomes such as revenue growth, time savings, cost reduction, accuracy improvement, or productivity gains. These indicators are important, but they do not tell the complete story.
A more mature approach considers net value: the value created after subtracting the value lost through weak execution.
Value can be lost through failed transactions, rework, customer dissatisfaction, operational exceptions, inconsistent data, inefficient workflows, or poor implementation. Each issue may seem minor in isolation, but together they can affect profitability, trust, and long-term business performance.
Customers do not experience these failures as internal data or process problems. They experience them as poor service. A valid address being rejected, an expired promotional code being sent, or incorrect product information causing an order failure can quickly damage confidence in the business.
Once that confidence is damaged, rebuilding it can take significant time and cost.
For this reason, AI measurement should include both the value AI creates and the hidden losses caused by poor execution. Leaders should ask what value is being created, what value is being protected, what value is being lost through operational weakness, and what the true net effect is on the business.
These questions help organizations develop a more realistic and commercially useful view of AI performance.
AI Investment Should Begin With the Business Decision
Successful AI investment starts with the business decision, not with the technology.
Too often, organizations begin with a desire to use a new capability such as generative AI, machine learning, AI agents, or knowledge graphs. Only afterward do they look for a business problem that fits the technology. This approach can lead to weak alignment and make value difficult to demonstrate.
A stronger approach begins by asking three questions: What business decision needs to change? What would success look like in business terms? How does the AI solution directly address the problem?
This decision-led approach helps teams identify who will act differently, what action will change, and how the business will know whether the AI system has worked. It also reduces the risk of technology-led projects becoming disconnected from commercial priorities.
In this view, AI should not be adopted simply because it is advanced, popular, or widely discussed. It should be adopted because it improves a decision that matters to the organization.
Human Judgment Still Plays a Critical Role
AI should often be positioned as an advisory capability rather than a fully autonomous decision-maker, particularly in sensitive commercial areas such as pricing, sales, credit, risk, and customer management.
These decisions frequently require context, relationship awareness, customer history, negotiation understanding, and professional judgment. AI can support teams by providing recommended ranges, explanations, risk signals, and visibility into trade-offs. However, the final decision may still need to remain with experienced business professionals.
This model, where AI informs and humans decide, can improve adoption because it presents AI as a tool that strengthens expertise rather than replacing it. Employees are more likely to trust and use AI when it helps them make better decisions instead of appearing to undermine their role.
The broader lesson is that AI adoption is not purely a technical challenge. It is also an organizational and behavioral one. Successful AI programs are built around the way people make decisions, not only around the way models produce outputs.
Data Leaders Must Become Profit Enablers
The wider message is that data and AI leaders need to redefine their role within the enterprise. They cannot be seen only as technical delivery teams, platform owners, or cost centers. They must increasingly act as enablers of business performance.
This shift requires commercial fluency. Data and AI leaders need to understand revenue, margin, cost, risk, customer experience, and operational trade-offs. They must be able to explain not only what has been built, but why it matters to the organization.
The most effective leaders in this space combine technical depth with business understanding. They connect platforms to decisions, models to margins, governance to risk reduction, and data quality to customer trust.
AI value is not created by technology alone. It emerges when models, people, processes, governance, and business decisions work together.
As companies move further into generative AI, automation, and agentic systems, this distinction will become even more important. The organizations that succeed will not simply be those that deploy the most advanced tools. They will be the ones that can show, in clear business language, that AI is delivering measurable value.
For Kamal, this is the real measure of AI maturity: not whether AI looks impressive on a technical dashboard, but whether it delivers value the business can see, measure, and trust.