Value mining and the fine art of finding value from AI

As a C-Suite exec, you are accountable for your AI investments. You need confidence in the process that surfaces opportunities to you, trust in the technology proposed to exploit them, and belief in the value attached to them.

From our experience at Valliance, that's often not the case. Here’s why.

The strategic bias

The persistent belief that valuable AI opportunities must be large-scale and impressive. They should transform entire business functions or unlock new revenue streams. Anything smaller seems unworthy of investment.

This belief causes organisations to overlook things that will never reach the C-Suite, even though they can be transformative. Many genuinely valuable use-cases sound mundane when described, or are an amalgamation of many small things. They might free staff up for higher value tasks, reduce errors, and prevent regulatory issues, but won't make it to the boardroom.

The value challenge

Value takes multiple forms, yet when talking AI we seem to always think in terms of 'time saved', begging the question about how that actually creates value. We're increasingly seeing new frameworks that consider a much wider range of value levers that have more credibility than this.

Reducing compliance risk. Unlocking increased capacity. Increasing decision-making confidence. Improving talent retention by eliminating mundane work. Preventing customer complaints. Enhancing customer service. Guarding against reputational and brand risk. Reducing climate impact. There are many more.

Unless your value attribution model considers all these and more, you'll miss opportunities that ultimately impact the bottom line.

The provenance problem

The people selecting AI use-cases that reach you are typically several steps removed from the work itself. They're creating hypotheses about processes they haven't personally executed in years. Perhaps they're consultants who don't even work in your organisation.

Everything they present is theory, based on conversations with people who are often in fear they may be put out of a job aren't opening up about the fragile workarounds and daily frustrations that represent the real opportunities.

Building confidence in AI

Although the C-Suite is accountable for AI investment, this is a group being increasingly open about their lack of confidence or understanding of AI, or whose working knowledge is out of date. You can't lead what you don't understand, yet you are asked to be confident and decisive in it.

So, how are we thinking about this challenge?

We believe Value-Mining can be more effective, repeatable and create more value, and that’s just one of the things we’re working on right now.

Think holistically and look everywhere

It's called 'mining' for a reason. It's difficult, and you have to dig deep to find meaningful challenges that are feasible to solve. This requires considering everybody in the organisation. This is not democratisation of the process, but recognition that real-world knowledge is distributed across different teams, shifts, locations, and functions. Each group has their own inefficiencies, frustrations, ideas, and knowledge about why previous solutions didn't work.

It's tempting to use tech vendors who will process-mine your network traffic to find process bottlenecks. Although good insight, it won't tell you why those things exist, or what would be better. That comes from people close to the problems. The real golden nugget is probably sitting within a fragile, elaborate workaround that someone perfected over years, or in a new joiner who sees things very differently.

We believe that the key to this is a systematic and deliberate approach to coaxing out that knowhow and those nuggets.

Empathy and trust

The point is to find opportunities to unlock value, not to put people out of work. When value-mining, we need to create a safety net made of trust, not just good intentions. Leaders must paint a picture where AI is the rising tide that lifts all boats, not the iceberg that sinks them.

Without trust, you'll receive a carefully curated list of opportunities that threaten nobody but improve nothing. We believe that bringing techniques more at home in product design and ethnography, we can develop the trust required to uncover possibilities you'd never have imagined.

Always-on value mining

The best opportunities don't wait for annual planning sessions, particularly lightweight optimisations that can be implemented quickly. We need to shift to an always-on process that continually seeks out the best use-cases, but also with capacity to listen, sense opportunities, and be open to suggestions from anywhere in the organisation.

We are already working with enterprises on AI-enabled methods of supporting business users to develop their use-cases and value attribution, then triaging them for further investigation by expert teams, unblocking the pipeline, and enabling this to work at real scale.

Rapid evaluation

Although thinking creates value, there's no value in thinking. Once we have a credible use-case, the most valuable activity is to purposefully test or evaluate it, not ponder it. If there's no value to be had, or it fails on feasibility, move on. This is an integral part of the Value-Mining process, not the next step.

Build an evaluation framework. Align it to strategy and key results, and ensure it always translates to tangible value. Always use it.

Build rapid testing capability. Most use-cases will fail. Accept this. The goal is to test many ideas quickly and cheaply. We're already doing this to great effect with enterprises who are ending up in places they didn't expect, but recognise as where they need to be.

Build AI fluency

Update leadership's understanding of current AI capabilities. Without it, they can't recognise genuine opportunities when presented, or they become over-optimistic, or fail to understand limitations and risks. Technical literacy in leadership directly determines their ability to evaluate use-cases effectively.

This is why we've developed and run our first executive enablement programmes as part of our People-First service, creating safe spaces for leaders to reset their AI understanding by getting hands-on with it.

Effective Value-Mining requires true collaboration and facilitation. It requires thinking differently, being open and honest, along with a well-exercised muscle in how to systematically dig out those hidden gems in a way that engenders trust and cooperation, and is scrupulously rigorous in evaluation.

We think we've already developed that muscle, and look forward to introducing you to how we will work differently with you to do that.