organisation – latchana.co.uk

Why Your AI Tooling Isn’t Working: It’s Not the Tech, It’s the Culture

Recently I delivered a presentation about being outcome-focused when introducing tooling and technology, over merely measuring output. Output tends to target lines of code, task completion and busy work. Whereas a focus on outcomes tends to measure proof-of-value, business challenges being met and sustained user satisfaction.

Many organisations have spent time and treasure seeking success with AI tooling, yet struggle to attain valuable user and business impact. So measuring and targeting success with AI is an increasing concern. To put it bluntly, for some, their claims and reputations are on the line.

I cautioned against an over-focus on output by citing Goodhart’s Law, which states “When a measure becomes a target, it ceases to be a good measure”. This has been recently demonstrated by the phenomenon of tokenmaxxing.

An attendee asked, how can you promote tool adoption that is not merely targeted by measuring tool sign-ins? Doing so could lead to users just signing in to show apparent usage, but not actually getting value from the tool. It could also be a result of teams being coerced to use a tool, despite their experience that the tool isn’t helping them.

The attendee’s question deserves careful consideration. My recommendation is not to think about metrics initially. Rather to focus on the user problem to be solved, and how that can ladder-up to business impact. Take a product-centric approach which incrementally validates whether the tool is desirable, viable and feasible.

Desirability leads to user behaviour changes. These are the leading indicators that can be measured by repeat usage, user recommendations and user satisfaction. Viability is the consequential business impact. These are the lagging indicators that can be measured by cost reduction, revenue growth and market share. Feasibility ensures a tool can be built, financed and operationalised.

While the DVF framework provides the ‘what,’ alone it’ll fail to address the ‘why’ organisations struggle—the hidden, cultural inhibitors to success. So, for many organisations the near-insurmountable challenge isn’t the relationship between types of metrics. It’s the competing pressures, ill-fitting culture and organisational psychology.

Sadly, many organisations have become so pressure-bound for ROI from AI, or certain leaders are so narrowly focused on resource utilisation, that genuinely addressing user needs and creating business impact is an afterthought.

For many organisations, teams don’t feel safe to push back. Teams silently suffer the consequences of the sunk cost fallacy and commitment bias.

So, organisations shouldn’t only institute disciplines that judge success with the right balance of measures. They need to re-imagine their culture and operations for safe-to-learn innovation that selects for tools that genuinely address user and business needs. Using models such as Westrum’s cultural topologies, culture itself can be examined and influenced.

Give me a call if you’d like to know more.

Ron Westrum’s model of Organisation Culture

When AI Becomes a Target: The Perverse Outcomes of Compelled Usage

We’ve seen this movie before. Organisations are so keen for return on their AI investments, they compel their workforce to utilise AI tools, which leads to perverse outcomes.

A recent example is Amazon setting a target for staff to use internal AI tools. To meet the target, and to consume AI tokens, some individuals have apparently automated unnecessary activities. This phenomenon of tokenmaxxing improves the rank on an internal leaderboard.

Obviously, this spurious use of AI tools wasn’t the intent. Such unintended consequences are an example of Goodhart’s Law:

“When a measure becomes a target, it ceases to be a good measure”

Rather than set targets, organisations ought to consider the underlying needs and purpose of their workforce and jointly discover solutions that will alleviate their problems.

The most impactful problem-solution fit might not even be technological. Often it is related to some misaligned combination of purpose, priorities, coordination, and process.

So rather than succumbing to pressure and becoming fixated on rolling out ill-fitting AI tools, genuinely partner with your workforce to develop sustainable solutions to their challenges. The set of solutions may or may not include AI tools.

The work I do helps managers and teams identify the mismatch between purpose and tools. It leads to a deeper appreciation and a set of interventions which genuinely enable greater individual and team performance.

The rule of thumb is to focus on purpose & people, process and tools, in that order of importance. It’s not to repeat the bad movie plot of compelling AI usage by any means.

AI and Organisational Fragility

Recent research in The Lancet reveals a systemic warning: after using AI assistance, experienced doctors’ independent diagnostic skills dropped by 20%.

This is the Automation Paradox — where “efficiency” atrophies the very intuition we rely on.

If we outsource the “hard-won lessons” to algorithms, we will likely lose the expertise for independent thought.

Are we augmenting our people or hollowing them out, leading to organisational fragility?

We must protect human judgment by designing for productive friction, ensuring AI remains a tool, not a crutch.

Ping me if you’d like to explore this.

Say No to GenAI solution-first thinking

MIT’s recent The GenAI Divide: State of AI in Business 2025 report states that “The 95% failure rate for enterprise AI solutions represents the clearest manifestation of the GenAI Divide”. It says this is caused not by the quality of the tools, but rather the “learning gap” for both the tool and organisation, and flaws in enterprise integration.

In an attempt to gain efficiencies and innovate, I’ve seen organisations excited to roll out GenAI tools. As the MIT report indicates, they should first fundamentally understanding the systems and people such initiatives are purportedly trying to support.

I believe they ought to do this by first asking leaders to align on the most critical customer & colleagues challenge to overcome, and the business outcomes that are most pressing. Those leaders should then learn how systems, processes and incentives may need to change. Such changes should be explored and measured by trialing different approaches, some of which may utilise technologies such as GenAI.

Such a strategy aligns to the principle of People, Process, Tooling, in That Order. This is one of the Better Value Sooner Safer Happier principles for business agility.

One reason to start with People is that any technological system will not have the tacit knowledge that exists within and between individuals (thank you John Abram for bringing this to my attention). Tacit knowledge is the practical “know-how” that’s difficult to articulate and rarely written-down. It’s often expressed in an unanticipated manner, and only at the time of application.

For example, consider an experienced salesperson teaching a junior employee. During a sales meeting, the latter will learn through observation, imitation, studying body language, and through anecdotal storytelling. These are too ineffable to be derived through studying employee handbooks, process documentation or through GenAI systems.

So, without adopting this outcome-first and customer & colleague centric approach, I fear many GenAI-led initiatives will be fundamentally flawed. They’ll add to the sorry litany of failed technology-focused transformations. The MIT report bares this out.

So, say no to GenAI solution-first thinking. Instead align on the problem to be solved and give colleagues the right freedoms to improve how they serve their customers, which may utilise technologies such as GenAI.

Don’t create pain by rolling out GenAI tooling based on poorly tested assumptions and expect colleagues to use it. Many will suffer in silence, and use the tool begrudgingly. Ironically it’ll likely add to inefficiencies, not reduce them.

Finally, consider employing visual tools such as Agendashift’s Changeban to place a strong emphasis on knowledge discovery and organisational learning. Collectively learn and measure, rather than assuming then integrating!

Contact me if you’d like to learn more.

Time to stop talking about Transformation and Agile?

Economic and business uncertainty means many organisations are retrenching and protecting their operations and market offerings. Cost-cutting, head-count freezes and workforce reductions are likely levers organisations are focusing upon. This indelibly marks the climate within organisations, the concerns of leaders and anxieties of their staff.

There’s a lessening appetite for transformational work and once trending approaches such as Agile are no longer top of mind.

So, as change agents working with colleagues and clients, we need to be very careful using terms such as Transformation and Agile. Directly focusing on such activities and approaches are likely to have little currency and draw little attention.

Instead, when consulting and supporting colleagues and clients, try to pick up on their language and empathise with their position. In confidence, try to help them articulate their concerns.

Use techniques such as Agendashift’s 15-minute FOTO which helps them shift from obstacles to outcomes.

I’m not saying dismiss the values and principles of agility. I’m suggesting having these at the back of your mind, and play them into your client’s or colleague’s situation without hitting them over the head with Agile and Transformation. They’re likely already bruised by ill-fitting adoption of such approaches and initiatives.

Work with them to run safe-to-learn change experiments. Such experiments are likely to focus on protecting value with less – less funding and sadly potentially less colleagues.

So, unless they use them, stop starting conversations with terms such as Transformation and Agile. Instead, use their language, recognise their change anxiety, and co-discover approaches which enable their organisations to respond to a rapidly changing world.