I’m David Knott. I’ve been working in enterprise technology for over forty years and I’m still learning. This blog is based on mistakes, failures, lessons and some things I find interesting:
Living with uncertainty: be ready to be wrong
Are you ready to be wrong?
Let’s imagine that you have followed the first seven steps in this article about becoming an enterprise AI leader (and maybe even attended the course to be launched later this year). You have learnt the fundamentals of computing, examined and adapted your leadership style, developed your leadership team, figured out your values and ways to stick to them, built a robust supply chain, re-imagined your enterprise and found a practical route to implementation.
And then everything changes. A leading AI company releases a whole new category of model which promises capabilities beyond anything you have seen before. Or the same company burns through its investors’ money, fails to raise new funds and the models you rely on vanish from the market. Or powerful, highly optimised new Open Source models are released which collapse the price of training and inference. Or companies come under pressure to recoup their capital investments and prices shoot up.
Managing the change: don’t turn your experts into novices
Technology people often forget the human side of change management. It is hard enough to design and build systems, integrate them, migrate data, configure infrastructure, and get everything working and stable. Technology can be erratic and unpredictable: who has time for even more erratic and unpredictable humans? Perhaps if we tack a training course and a couple of videos on the end of the systems rollout, we can achieve a bulk update of the human config files.
The error of thinking this way was demonstrated to me when I was working on a large scale merger between two banks. The technology choice was easy: we picked the systems of one bank, and then started planning how to migrate data from one set of systems to the other. Our designs were filled with data extracts and transforms, temporary integration layers, reconciliation and testing suites. We thought that our hardest problem was fitting all the migration work into a constrained time slot.
Imagining the enterprise: look through both lenses of your AI goggles
You’re looking at the problem wrong.
This is one of the perennial laments of technologists, particularly enterprise architects, when they work with leaders who claim to want to transform their business. Somebody senior, such as the Chief Operating Officer, or the person who runs all of the call centres, will invite the technologists in, and say that they want to fundamentally reimagine the way their business works. Yet, when the technologists start to produce designs and make proposals, it turns out that they have a rather different understanding of what fundamental means.
Leading with integrity: ethics does not equal compliance
This is the most important programme in the world because the regulator says so.
It’s a regular ritual in any large corporation: reviewing the proposed change portfolio for the coming year. The list of proposals is always longer than the company can afford, and, even if it could afford to do everything, there are only so many things it can do. Naturally, everyone proposing a change programme tries to make it sound as if it is more vital than all of the others: it is the one thing that will cut costs, boost revenue or protect the organisation from existential threats.
In regulated industries, such as finance, utilities and healthcare, one of the strongest justifications offered is compliance. The idea is that, if the change is required by law or regulation, the company is obliged to do it: there is no choice.
Tackling the AI market: more than just a product
My Dad used to enjoy buying things. Or, to be more accurate, he enjoyed the process of getting ready to buy things.
If we needed a new washing machine, or a microwave, or, best of all, a car, he would break out his graph paper and pencil, and a big pile of Which? magazines and other literature. He would make a list of criteria, and he would go through all of the candidate products, scoring them out of ten, adding the scores up, and adjusting them if he wasn’t convinced by the answer. And then we would tour the shops, interrogating the sales staff to a level of detail they were not always prepared for.
Building the leadership team: more than a seat at the table
How inclusive is your leadership team? Hopefully it is inclusive in all the dimensions of diversity, but do you actually include all of your leaders as genuine parts of the team? Or do you think of some parts of your leadership team as ‘core business’ and other parts of your leadership team as ‘support functions’?
Regarding your team in this way creates an implicit hierarchy of leadership positions, even if all leaders enjoy similar grades and reward packages. Metaphorically, the CEO, or head of the business division sits at the head of the leadership table. The people who run revenue generating business divisions are clustered close by. The CFO may sit at the leader’s right hand, depending on the financial position of the organisation. But the head of HR is probably further down the table, while the head of IT is down the corridor fixing the printer, and the head of procurement is out at the shops, haggling over prices.
Changing your behaviour: getting out of Boss Mode
Two of the most difficult transitions I have had to make in my career have been due to changes in my perception of what it means to be a leader.
The first came when I moved from a management role to a leadership role. This transition is rarely recognised and often poorly understood, especially in specialist fields such as technology. Notoriously, great technologists are frequently left to flounder when they are promoted to become managers. It’s not surprising that there is even less support when they cross the fuzzy boundary from management to leadership.
Learning the fundamentals: beyond the IT slot machine
Business executives often experience their IT department as a slot machine: something that consumes money, occasionally gives a payout, and whose inner workings are opaque and inscrutable.
Sure, sometimes the slot machine gets new lights and buzzers which claim to give an idea of what it is going to do (project plans, progress reports and dashboards), and sometimes it gets new buttons which make players feel as if they are in control (steering committees, governance processes and change boards). But the machine still seems to keep on doing the same old random things, as if the lights, buzzers and buttons weren’t actually connected to anything.
Eight steps to enterprise AI leadership
True AI expertise is rare. AI leadership expertise, the ability to lead organisations which have embedded AI into their operating models, is even rarer. And enterprise AI leadership, the ability to lead the adoption and use of AI within established businesses, is so rare as to be virtually non-existent.
This rarity seems to persist, despite the number of press releases, articles and social media posts which claim that ‘AI is transforming industries’. My perception is that most leaders in most industries would admit that they find AI as baffling as it is exciting, as frightening as it is promising, and that they wish that it would slow down for long enough for them to catch up.
<x> is too important to delegate to your Chief <x> Officer
I started my career in the late 1980s, when the term ‘Chief Technology Officer’ was just starting to be used in companies. (It wasn’t used in the government department where I was working: that still referred to IT as ‘Automated Data Processing’. This was a place where we still had to write out our documentation by hand and send it to the typing pool to be processed,)
Since that time, I have seen the creation of many Chief <x> Officer job titles in the field of enterprise computing: alongside the Chief Information Officer and Chief Technology Officer, we have had the Chief Digital Officer, the Chief Information Security Officer, the Chief Architect and the Chief Data Officer. The latter has undergone mutation in recent years, showing up as the Chief Data and Analytics Officer (CDAO) or, now, the Chief Data and Artificial Intelligence Officer (CDAIO - a title which I can’t help singing to the tune of Old MacDonald Had a Farm - C-D-A-I-O).
Reflections on a vertical learning curve
Today is my last working day as CTO for the UK Government.
Three years ago, I had just learnt that I might be offered this job. I was completely uncertain about whether I should say yes or not. Fortunately, my wife, as ever, gave me good advice – to talk to some experts and mentors, who knew me and the kind of work I did, and to get their perspectives.
Those conversations yielded two insights. First (from people who were naturally analytical), when we plotted pros and cons, the pros sounded like reasons to do the job, and the cons sounded like work to be done – and therefore more reasons to do the job. Second (from people who thought in terms of purpose), I discovered a strong sense of public duty: for an example, an Australian friend told me that if his country asked him to do an equivalent job, he would accept with pride.
Look on the bright side: the power of applied optimism
Somewhere in the 20th century, at the beginning of my career, I worked with a project manager on several projects. After a while, they told me why they liked to have me on their team.
I waited for them to tell me that it was due to my technical brilliance, my architectural insight, or my all-round charm and charisma. Instead, they told me that it was because I was an optimist, and that I was quite vocal about my optimism.
That gave me an immediate lesson in self-awareness: that the attributes that people value you for are not always those that you value in yourself. I would much rather have been known as the technical wizard than as the person who says, ‘Don’t worry, everything will probably be alright’. Especially as, on most IT projects, such optimism is often seen as a sign of naivety and inexperience.