Getting the value from AI

Artificial intelligence has moved beyond experimentation. Many organisations are now investing time and money into AI initiatives, yet a familiar challenge remains: demonstrating value in terms that resonate with executive teams.

Technology leaders often measure progress through model performance, usage levels, and automation rates. While these metrics are useful operationally, they do little to answer the questions that matter most to decision-makers.

For example, how will AI contribute to growth? Will it improve profitability? Can it reduce risk? Will it make the organisation more resilient?

AI implementation is accelerating, with enterprise deployment moving decisively from pilot phases into scaled production. According to the Salesforce CIO Study 2026, conducted with NewtonX across 200 CIOs in 24 countries, the proportion of organisations reporting full AI implementation has increased from 11% in 2024 to 42% in 2025, a 282% year-on-year rise as enterprises transition from experimentation to enterprise-wide deployment.

As AI adoption accelerates, boards are becoming more involved in oversight. At the same time, regulators are paying closer attention to data protection and the responsible deployment of AI systems.

This shift requires a different conversation. Technology leaders must move beyond discussing what AI is doing and focus instead on the business outcomes it is delivering.

Traditional AI metrics fall short

For years, technology investments have been justified through efficiency gains. The same approach has largely been applied to AI. Measures such as tickets resolved, tasks automated and hours saved can demonstrate activity, but they do not always demonstrate value. Boards do not invest in technology because it automates a process. They invest because it helps achieve strategic objectives.

As AI becomes more embedded in operations and decision-making, organisations need to evaluate it in the same way they would any other investment.

The questions become straightforward. Is revenue increasing? Are costs reducing? Has risk been lowered? Has organisational capability improved?

A more useful framework for measuring value

One practical approach is to adopt a balanced scorecard for AI. Rather than focusing on technical performance, the scorecard should connect to business outcomes.

Growth measures might include conversion rates, customer retention and revenue generated from new products or services. Efficiency measures could include cost per transaction, cost per customer interaction, or reductions in process cycle times.

Risk measures should examine areas such as data exposure, compliance issues and operational resilience. Capability measures should focus on workforce productivity, skills development and the organisation’s ability to adopt new technologies successfully.

The objective is not to create a larger dashboard. It is to identify a small number of measures that demonstrate whether AI is contributing to organisational performance.

Moving from proof of concept to proof of value

Many AI programmes struggle because success is not clearly defined. A more disciplined approach begins with a clear understanding of value. Before implementation, organisations should be able to state the expected business outcome in simple terms. For example: “If we introduce this capability into our customer service operation, we expect to reduce handling times by 15% and improve customer satisfaction within six months.”

See Also

10 things every startup founder needs to know about MTD

Establishing a baseline is equally important

Without understanding current performance, it is impossible to measure genuine improvement. Testing should then compare AI-assisted processes with existing approaches, allowing organisations to isolate the impact of the technology from other variables. Most importantly, results should be translated into financial and operational outcomes that boards can readily understand.

Different stakeholders need different evidence

Finance leaders are interested in measurable financial impact. Operations leaders focus on efficiency and service levels. Security leaders need confidence that appropriate controls are in place. Human resources leaders want to understand how technology is affecting workforce capability and productivity. Boards ultimately need a balanced view that combines performance and long-term sustainability. Successful technology leaders recognise these differences and tailor their reporting accordingly.

Governance cannot be an afterthought

One of the most significant mistakes organisations make is treating governance as a separate workstream. In reality, governance is a critical enabler of successful AI adoption. Strong governance helps organisations deploy AI more confidently, particularly in highly regulated sectors and business-critical processes.

It also provides boards with assurance that risks are monitored and managed appropriately. As AI becomes more capable, governance will increasingly become a source of competitive advantage rather than a compliance requirement.

The next phase of AI leadership

Success will not be determined by who deploys the greatest number of AI tools or runs the largest number of pilots. It will be determined by who can consistently translate technological capability into measurable business outcomes.

Boards do not need more dashboards. They need evidence. The organisations that succeed will be those that connect AI initiatives to growth, resilience and organisational capability. Those conversations belong in the boardroom, and technology leaders must be prepared to lead them.