Why faster isn’t always better with AI

For most organizations, the business case for AI starts with productivity.

Teams identify repetitive work, estimate the effort involved and calculate how much of that effort AI could remove. It creates an appealing equation: automate enough tasks across enough people and the accumulated time savings should translate into meaningful value.

That equation is more complicated than it looks. Saving an hour does not necessarily create an hour of value, and introducing AI creates demands of its own: data needs to be prepared, technology integrated, outputs governed and people equipped to work differently.

In other words, making an existing activity faster does not automatically make the commercial engine better. The bigger opportunity comes when AI enables organizations to do something that previously wasn’t practical to do at all.

The AI productivity paradox

New technology does not automatically translate into higher productivity. AI may reduce the effort required to complete a task, but implementing it introduces work elsewhere. Data has to be made usable, processes need to change, outputs need oversight, teams need new skills and organizations need to decide where AI fits into existing systems and ways of working.

Research into AI adoption has described this as an AI productivity paradox: businesses can invest in technologies intended to improve productivity without seeing an immediate improvement in performance.

It echoes a much older pattern. When computers first entered the workplace, organizations did not suddenly become dramatically more productive simply because employees had access to them. Capturing their value required businesses to redesign processes, roles and operating models around what the technology made possible.

AI presents a similar challenge: if an organization introduces AI but leaves everything surrounding the work unchanged, the result may simply be a faster version of the same process, with a new set of implementation costs attached.

10% faster isn’t the most interesting outcome

This is why productivity alone can be the wrong lens for prioritizing commercial AI opportunities.

There will undoubtedly be value in using AI to remove manual effort from everyday commercial activities and give teams more capacity to focus on higher-value work, but saving time only creates value if that capacity is subsequently used productively.

There is another category of opportunity with potentially greater upside: using AI to perform valuable commercial activities that weren’t happening consistently before. Most organizations have plenty of them: accounts that don’t receive enough research because sellers don’t have the capacity, customer data that isn’t analyzed frequently enough to identify opportunities, propositions that aren’t tailored because there are too many combinations of customers and products, or cross-sell opportunities that remain dependent on individual relationships and memory.

The issue isn’t that organizations don’t recognize the value of these activities. The economics have historically made doing them properly, for every customer and every opportunity, unrealistic. AI can change those economics.

Find the work your organization can’t afford to do today

That creates a different starting point for identifying AI use cases. Instead of beginning with ‘where are our people spending the most time?’, commercial leaders can ask: ‘what would we do much more often if time and capacity were no longer the constraint?’

That question opens up a very different set of opportunities.

Prospect research could happen for every priority account rather than only the largest opportunities. Customer signals could be analyzed continuously rather than during periodic account reviews. Commercial teams could identify relevant cross-sell opportunities across an entire customer base rather than relying on individual sellers to connect the dots.

In each case, AI isn’t simply making an existing activity cheaper or faster. It is making that activity viable at a scale that wasn’t possible before.

A process becoming 20% faster is an efficiency improvement. A capability going from occasional to continuous, or from a handful of customers to thousands, can change how the commercial organization operates.

The best AI opportunities are usually closest to the problem

There is a second consequence of thinking about AI this way. The people most likely to identify these opportunities are rarely sitting in a central AI team.

They are the people managing tenders, serving customers, developing propositions, analyzing accounts and running commercial processes every day. They understand where work breaks down, where important activities get deprioritised and where lack of capacity creates compromises.

That doesn’t mean AI should become a decentralized free-for-all. Central technology and AI teams have an important role to play in establishing infrastructure, security, governance and reusable capabilities; however, identifying the problem, redesigning the workflow and owning the resulting business outcome needs to happen much closer to the function. Otherwise, organizations risk building increasingly long lists of AI use cases without changing how work actually gets done.

The most effective model combines central enablement with distributed ownership: provide the organization with the foundations to use AI safely and effectively, then empower the people closest to commercial problems to determine where it creates value.

Productivity is the starting point, not the ambition

There is nothing wrong with using AI to save time, but if that is the extent of the ambition, organizations risk underestimating what the technology makes possible.

The bigger opportunity is to remove the constraints that prevent commercially valuable work from happening at scale: analysis that takes too long, customers that don’t receive enough attention, opportunities nobody has capacity to investigate or processes that are too expensive to execute consistently.

The organizations that capture the greatest commercial value from AI won’t necessarily be the ones that save the most hours.

They’ll be the ones that use AI not simply to do the same work faster, but to make commercially valuable work possible that wasn’t possible before.

See how we help organizations identify and scale AI opportunities that create meaningful commercial value.