Adaptability and Prototyping matter more than picking the \"right\" tools

Most firms are approaching AI in a way that feels familiar. A set of tools is selected, a rollout plan is defined, teams are trained, and the organisation moves forward with a new “AI-enabled” operating model.

That approach worked reasonably well for previous waves of enterprise technology. It is struggling to hold up in the current environment. The problem is not capability. It is speed of change. Models are improving rapidly, new tools are entering the market, and workflows that look efficient today are often replaced within months. In that context, a fixed implementation quickly becomes a constraint. Instead of enabling productivity, it locks the business into a set of choices that are already being overtaken.

This is where many AI programmes start to underperform. They assume stability in a system that is still evolving.

This is not just a technology issue. It is an economic one. What firms are facing is a high-uncertainty environment where the returns to different tools and workflows are not yet well understood. In these conditions, economists tend to emphasise the value of flexibility. When outcomes are uncertain and changing, there is a premium on keeping options open rather than committing too early.

This is often described as option value. Small, reversible investments allow you to learn about what works without locking in decisions that may prove suboptimal. Over time, those learning cycles compound into better allocation of resources.

Applied to AI, this suggests a different approach. Instead of treating adoption as a one-off decision, it becomes a process of experimentation and selection. The firm is not trying to pick the perfect setup upfront. It is trying to build a mechanism for discovering it.

This is where the gap between expectation and reality often emerges. Many AI tools demonstrate impressive capabilities in isolation. The challenge is translating those capabilities into measurable gains within real workflows. Prototyping bridges that gap. Rather than redesigning entire functions, firms test specific use cases in a controlled way. A team might trial different approaches to document review, internal analysis, or customer support, measuring how long tasks take, how output changes, and whether quality is maintained.

The key is that this is done deliberately. The objective is not just to “try AI”, but to generate evidence. Which tools actually save time? Where does output increase? Where do gains disappear once real-world constraints are introduced? Without that step, it is easy to overestimate impact. With it, firms start to build a grounded understanding of where value is created.

The idea of experimentation is widely accepted. The execution tends to be inconsistent.

A common issue is that workflows are not properly understood at the outset. AI is layered onto processes that have not been clearly mapped, making it difficult to identify where inefficiencies actually sit or how they should be addressed.

Even where pilots are run, evaluation is often informal. Teams report that something feels faster or easier, but there is no consistent measurement of time saved, output generated, or impact on costs and revenue. This makes it hard to compare use cases or prioritise what should be scaled.

Finally, there is often no clear pathway from pilot to rollout. Promising experiments remain isolated, while the broader organisation continues operating as before. The result is activity without accumulation. Effort is expended, but it does not compound into meaningful productivity gains.

The firms that are starting to see real benefits from AI are not necessarily those using the most advanced tools. They are the ones that have built a process around how they adopt them.

They are clear on where to test, disciplined in how they measure outcomes, and deliberate about how successful use cases are scaled. They revisit decisions as tools improve, rather than treating initial choices as fixed. Over time, this creates a different kind of advantage. The organisation becomes better at absorbing new technologies and translating them into value. Productivity gains are not driven by a single implementation, but by a series of better decisions.

For most firms, the challenge is not access to AI tools. It is turning experimentation into consistent, measurable impact. This is where RG Economics comes in and can help.

1. Map workflows and identify where AI can realistically create value

This involves breaking down how work is currently done across key functions and identifying where time is spent, where bottlenecks exist, and where AI could materially improve speed, cost or quality. The output is a prioritised set of pilot opportunities grounded in actual operations, rather than generic use cases.

2. Design and run structured pilots with clear metrics

Instead of ad hoc experimentation, pilots are set up with defined objectives and measurable outcomes. This includes selecting a small number of tools or approaches, testing them within real workflows, and tracking metrics such as time saved, throughput, error rates and cost impact.

3. Evaluate whether productivity gains are real and scalable

Not all apparent efficiency gains hold up in practice. This step focuses on using operational data to assess which pilots deliver genuine improvements and which do not. The result is a clear, evidence-based view of where value is being created and where it is not.

4. Build a repeatable system for scaling and adapting over time

Once successful use cases are identified, the challenge becomes scaling them across teams and updating workflows accordingly. This includes defining how decisions are made, how tools are revisited as the market evolves, and how the organisation maintains flexibility rather than locking into a fixed setup.

The underlying point is straightforward. In a fast-moving environment, the advantage does not come from making the right decision once. It comes from building a system that allows you to keep making better decisions over time.