Problem Definition: The Executive Guide to Making AI Investments Pay Off

As 2026 opens up, use of artificial intelligence in the workplace is transitioning from experimental to expected. Executive teams are under pressure to “do something with AI,” and vendors are eager to demonstrate tools that promise transformation. But why do many AI initiatives stall, under deliver, or quietly fade after pilot phases?

Most struggling AI programs share a common starting point: they begin with tools instead of outcomes.

For executives in charge of finance and operations, the real opportunity lies in collaborating with executives in charge of technology to define the right problem. Organizations that treat AI as a solution in search of a use case often experience wasted spend, misaligned expectations, and deployments that never scale. Those that begin with clearly defined business objectives are far more likely to see measurable returns.

The Tool‑First Trap

Many AI adoption conversations tend to center on capabilities rather than needs. Leaders ask what the technology can do, not what the organization requires. This tool‑first approach creates several predictable challenges:

• Diffused objectives: Without a specific operational problem to solve, teams experiment broadly but produce little measurable impact.

• Escalating costs without returns: Infrastructure, licenses, consulting, and integration expenses accumulate before value is proven.

• Pilot purgatory: Projects demonstrate potential in controlled environments but never transition into production because they aren’t tied to a critical business outcome.

From a financial perspective, this pattern resembles capital investment without a defined business case. From an operational perspective, it diverts attention from initiatives that could produce immediate gains.

Why Outcomes Must Come First

Executives considering AI initiatives should start with a clear articulation of the business problem. This problem should be significant, measurable, and aligned with strategic priorities.

This means asking questions such as:

• Where are we experiencing persistent inefficiencies or bottlenecks?

• Which decisions rely on incomplete or slow analysis?

• What processes constrain growth, margin, or customer experience?

• Where does risk accumulate due to human error or limited visibility?

When AI is applied to a defined constraint — for example, reducing invoice processing time, improving demand forecasting accuracy, or accelerating claims review — the evaluation criteria become concrete. The organization can measure whether the initiative improves output, reduces costs, or enhances decision quality.

This outcome‑first approach transforms AI from an exploratory expense into a targeted operational investment.

The Cost of Vague Objectives

Vague goals such as “becoming AI‑driven” or “using AI to innovate” create ambiguity at every level of execution, resulting in a collection of disconnected experiments rather than a coordinated program.

Common consequences include wasted spend, expectation gaps (executives anticipate rapid transformation, while implementation teams confront data limitations, process complexity, and change management challenges), and stalled deployments.

One particularly challenging consequenceis adoption resistance, where employees perceive AI as abstract or threatening when its purpose is unclear, slowing integration into daily workflows.

For CFOs, the absence of a measurable objective makes it difficult to justify continued funding. For COOs, it obscures operational impact. For CIOs, it complicates architectural planning and governance. 

Defining the Right Problems

Before selecting vendors or platforms, organizations can benefit from a structured problem‑definition phase. This process can identify where AI produces meaningful business impact rather than incremental novelty.

Effective problem definition typically includes:

• Process mapping: Understanding how work actually flows across departments, including delays, manual steps, and decision points. This requires executive alignment across finance, operations, and technology. Working together ensures initiatives target problems that are operationally significant, financially justified, and technically achievable.

• Constraint identification: Pinpointing where performance limitations affect revenue, cost, risk,or customer satisfaction.

• Value modeling: Estimating the financial and operational impact of solving the problem, including both direct savings and strategic benefits.

This phase often reveals that some challenges require process redesign, policy changes, or system integration before AI can be effective. In other cases, it highlights opportunities where AI can deliver rapid, measurable improvements.

Aligning Technology with Business Strategy

AI’s potential is real, but so are the risks of unfocused adoption. Organizations that rush to implement tools without defining outcomes often discover that technology amplifies ambiguity rather than resolving it.

When problem definition precedes tool selection, conversations shift. AI becomes part of broader discussions about efficiency, resilience, growth, and competitiveness.

COOs gain visibility into process improvements. CFOs can evaluate investments based on measurable returns. CIOs can design architectures that support long‑term scalability rather than isolated experiments.

Most importantly, the organization moves from reacting to technological trends to deliberately applying innovation where it matters most.

The question for executive teams is notwhether to adopt AI, but where it will produce measurable value.

At PulseOne, we partner with AvePoint to help organizations establish the governance, data control, and operational discipline required for successful AI adoption – starting with problem-defining.

If you and your team are considering AI contact PulseOne or take our free AI Readiness Assessment to get started.

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