Hi there,
In today’s article:
The first prompt advantage
How things actually work
Data quality matters
Ownership and accountability
Determined machine problem solvers
Avoid failure modes
Why it’s different this time
Low-cost automated audits
What the AI sunlight finds
A lot of people think that an AI transformation is just going to help them do things better, faster and cheaper, and that that’s that. My view is a little bit different.
I think AI is first and foremost a sunlight disinfectant that enables us to understand what’s really going on under the hood at a low technical level of detail in a much more efficient way than we might have had to do the investigation just one year ago.
This change is in what we see across the operating model. We’re still working in much the same way, but with the help of AI agents, we can rapidly find the bottlenecks and use agentic workflows to solve them much more quickly than we could in the past.
If you change how a business is using AI to an adversarial and audit-type style, the first thing you start to see is all of the little bits of extra fix and repair and maintenance work you need to do. You could almost think of it as an engine tuning or performance improvement exercise.
Every business has $100 bills sitting on the footpath that they haven’t picked up yet. I don’t think there’s much benefit in trying to put cosmetic KPIs around AI usage that don’t link up to what you actually want to achieve strategically.
When you start applying AI well, you start to get detailed, low-level insights into what is going on in your operating model that may not have previously been reported to you unless you had an incident and had to do a deep dive on a particular function or process.
The first prompt advantage
You start with a blank prompt window in the tool of your choice. An AI agent won’t start automatically fixing your business. You still need to steer it in the right direction and set things up so that it can actually operate with the level of agency frontier models have today.
This is why I think that leaders getting on the tools and working extensively with AI is important. If they haven’t experienced both the success modes and failure modes themselves, the way they guide and govern AI transformation is going to be undercooked.
All of the standard things you need to think about when doing a transformation apply to an AI transformation. To make the right decisions and nudge the AI models in the right direction, having knowledge about how things actually work under the hood in a business or the curiosity and tacit knowledge to figure out where the answers are or who has them is more important than ever as a skill.
This comes naturally to a lot of business leaders when they are in direct control and directly accountable for a piece of work. They can operate. They have the context. They know the right questions to ask.
I think many people are missing that if you put in a goal that is modest, you will get a modest outcome. The AI models can do much more for us if we think expansively about what is possible.
I think one of the reasons why a lot of people are very sceptical of AI is that it’s very easy to get stuck in a doom loop of prompting. You can set up a very fancy set of workflows and dashboards and look like you’ve done an awful lot of work, but the business metrics that you care about don’t move.
How things actually work
Processes, and how they’re designed, how long they’ve been operating, and the problems that people already know there are, are another key area here for exploration and interrogation.
The processes that everyone knows how they work often don’t actually map to exactly how they’re configured in systems and exactly how things flow from customer to firm to customer again, or customer to supplier to you.
You need to understand and appreciate that one of the downsides of being in a continuous-improvement, AI-first era is that every little process improvement win you achieve can probably be made better with the next generation of models.
In my corporate experience, I did a lot of business analysis work, sitting with people, watching how they work, asking them questions and tracing entire processes end to end and documenting them. When you do that process many times over many years, you understand the gap between how we think our business works and how it actually works at the front line. It can be two very different things.
It actually links back to the decision-making challenge around how we execute AI transformation, because what I see as one of the biggest value-adds of AI agents is that sunlight exercise: the requirements gathering, the configuration validation, mapping the plans and documentation (or lack of documentation) to what actually exists in our operating model today. This exercise is really important.
There’s this gap between tacit knowledge, which lives in people’s heads, and explicit knowledge, which is written down in documentation. A lot of that gap is the IP or competitive differentiation that enables firms to deliver value to their customers.
One thing which AI helps us do is close that gap at a much faster pace than having a team of business analysts come in and do interviews with everyone. They can still do that, and in fact, if I was tasked with setting up a requirements-gathering exercise today, I would be leveraging AI tools by doing things like recording all the transcripts and feeding them straight to my preferred AI agent to synthesise and tease out all of the things to make sure nothing’s been missed.
Why? Because context is everything in the AI era, and anything that isn’t accessible to an AI agent is something that you can’t use to optimise your operating model further.
Data quality matters
Having worked in a data analytics team and having a lot of personal experience with data cleaning, data scrubbing, data processing and figuring out how to actually get golden-source data into a beautiful dashboard for stakeholders, I care about data quality.
I know that throughout the corporate world, and the business world in particular, data governance has been a buzzword as opposed to how people are actually operating their businesses.
This is one thing which I’ve written about a lot on Getting AI To Work, but data quality matters so much because if an AI agent is looking at the wrong piece of context, or has an inaccurate idea about what different data points mean or the value they have in optimising different processes or delivering specific bits of value to customers or complying with specific regulatory or legislative requirements, you can have really unfortunate consequences from not taking it seriously.
What are AI agents doing in this aspect of thinking about AI transformation? Well, AI agents can help you do those data quality deep dives and build out data remediation plans at pace.
You need to have a lot of testing, quality control and business expertise sitting across these processes, but to my mind this is yet another example of how, when you’re doing these transformations, a lot of the tasks which may have needed to have been done sequentially can now be done in parallel.
At the same time as doing all your deep diving on your operating model, you can kick off data quality interrogation agents to go and look at things and understand: how are things flowing? Where do you not have those business definitions? Where are the blockers in your data ecosystem that could lead to inadvertent AI agent mistakes?
Ownership and accountability
A lot of organisation charts have evolved over time and constantly get restructured or reorganised based on how leaders want to manage things and achieve other objectives through the stock-standard corporate approach of disestablishing roles, moving things offshore, or changing who reports to who in order to achieve the outcome they personally want inside the organisation’s goals.
Now, you can see from the attempts of the frontier AI labs to not really talk about the liability for actions of AI agents that, in corporate environments, making sure that someone is accountable for every single thing that an AI agent does in your business is quite an important thing.
There’s also a problem where that gap between what AI agents can do well, can’t do well, and need human help or help outside of the firm needs all of the right governance controls sitting around it, so that you know and can understand to the best that you can inside the volume of work that is now going to be done in these AI-first operating models, where things are happening at a high and detailed level alike.
Because when things go wrong, and they will go wrong, rapid remediation is now an expectation from external stakeholders. Sitting around for weeks and months doing a response to a regulatory incident isn’t going to cut it. People now expect the speed of AI disaster to match the speed of AI-enabled response and recovery.
So this ownership and accountability problem is a serious issue because, in my opinion, you need both the right business knowledge and technical knowledge. It’s no longer good enough to be delegating AI-related things to technical people.
One way to think about it might be: the most successful business leaders over the coming years are going to be the most AI-native ones, who have leaned into this technology to apply their domain knowledge and expertise to get the leverage and have the ownership over the overarching quality of every component of that operating model they are responsible for.
Determined machine problem solvers
Why do AI agents serve as auditors so easily? Well, if you give a human worker a task, they are going to route around the gaps in knowledge through doing the work. They’re going to go and ask people they know. They’re going to ask people they know who else they should talk to. They’re going to look at a system and know from their own experience the documentation and some basic research: what’s working, what’s not working.
The difference is with AI agents, they will not route around anything. They will either get stuck or complete what they’re asked to do. If they are aggressive and goal-maximising, they will find things which people are shocked and surprised about.
One thing I’ve found useful in my work so far in this AI journey is seeing the low-level things that get surfaced when AI agents operating at speed to achieve a goal run headfirst into the blockers. I think about times in my corporate career when low-level issues were sometimes only surfaced after enormous amounts of effort.
The fact that we can now just ask an agent to audit an entire database and prepare a detailed report is useful. We still need the experience and skills to judge and assess the output.
The flip side of having all of this is that the logs and audit trail are now evidence. So if someone in your business asks a question and finds something that requires regulatory disclosure or a chat with the auditors, more companies are going to find themselves having to do that.
From a wider societal perspective, this is probably better for increasing quality and compliance across all manner of industries because AI agents will start to surface things that even the best human experts haven’t considered over many years and regulators would love to hear about.
Avoid failure modes
So what I’ve seen myself is that when I’m working through these sorts of tasks, the first run doesn’t really run into model limitations. It’s almost always context and access. So the key part of getting this investigative and exploratory side of an AI transformation right is around getting things set up properly and making sure you give things access.
I believe that a lot of pilot projects and proof-of-concept projects are completely set up to fail because they’re given such a narrow-scoped set of things to focus on that the wider value of AI isn’t being leveraged.
There are so many things which are really interesting to me, especially things like system misconfigurations, process steps that people have forgotten about or aren’t clearly documented, and handoffs that only work because there’s tacit knowledge about what needs to be done when. People haven’t stepped back and thought about end-to-end processes and how they should actually be working.
So there are a lot of issues in existing operating models that we might have dealt with previously through process improvement, continuous improvement, digital transformation, or however we might want to label these sorts of exercises. But all of these things are still present when we are thinking about doing an AI transformation. It doesn’t just stop.
So is this all just a rerun of “garbage in, garbage out”? I don’t think so. It is true that bad inputs have always produced bad outputs, but there are some things about AI and its capabilities that make it a little bit different this time.