One minute guide to real-world AI implementation 

McKinsey just published an excellent and comprehensive paper covering how Artificial Intelligence (AI) can deliver real value for business.


The only issue – at 80 pages it’s a lot to read.

A lot of the use cases focus on retail, energy and education, one angle I find particularly are the read-across of these examples into service based and business-to-business environments. There are definitely some relevant points that could map to a services/B2B worlds: for example the automation of admin tasks for teaches, more targeted sales and marketing and more personalised customer service.

Here’s my take on the key points from the document:

1. No shortcuts: first data & digital, then AI

AI becomes impactful when it has access to large amounts of high-quality data and is integrated into automated work processes. AI is not a shortcut to these digital foundations. Rather, it is a powerful extension of them.

The firs thing firm’s need to do is come up with a real business case for AI that relates to the firm’s strategy, this requires separating the hype and buzz around AI from its actual capabilities in a specific, real-world context. It includes a realistic view of AI’s capabilities and an honest accounting of its limitations, which requires at least a high-level grasp of how AI works and how it differs from conventional technological approaches.
Each new generation of tech builds on the previous one – this suggests AI can deliver significant competitive advantages, but only for firms that are fully committed to it. Take any ingredient away—a strong digital starting point, serious adoption of AI, or a proactive strategic posture—and profit margins are much less impressive. This is consistent with McKinsey findings in the broader digital space.
Technology is a tool and in itself does not deliver competitiveness improvements.

2. Areas to focus on to create real value: project, produce, promote or provide 

To fulfil the expectations being heaped upon it, AI will need to deliver economic applications that significantly reduce costs, increase revenue, and enhance asset utilization.

Mckinsey categorized the ways in which AI can create value in four areas:(1) enabling companies to better project and forecast to anticipate demand, optimize R&D, and improve sourcing; (2) increasing companies’ ability to produce goods and services at lower cost and higher quality; (3) helping promote offerings at the right price, with the right message, and to the right target customers; and (4) allowing them to provide rich, personal, and convenient
user experiences

3. Data ecosystem & staff culture to the fore 

Firms must conduct sensible analysis of what the most valuable AI use cases are. They should also build out the supporting digital assets and capabilities. Indeed, the core elements of a successful AI transformation are the same as those for data and analytics generally. This includes building the data ecosystem, adopting the right techniques and tools, integrating technology into workplace processes, and adopting an open, collaborative culture while reskilling the workforce

4. Take a portfolio approach focused on use cases in short, medium and long term, be lean, fail fast & learn

A portfolio-based approach to AI adoption cases, looking at use cases over a one- to five-year horizon, can be helpful.

In the immediate future, McKinsey suggest a focus on use cases where there are proven technology solutions today that can be adopted at scale, such as robotic process automation and some applications of machine learning. Further out, identify use cases where a technology is emerging but not yet proven at scale. Over the longer term, McKinsey’s view is to pick one or two high-impact but unproven use cases and partner with academia or other third parties to innovate, gaining a potential first-mover advantage in the future. Across all horizons, a “test and learn” approach can help validate the business case, conducting time-limited experiments to see what really works and then scaling up successes. Fast, agile approaches are important.

5. Don’t be a hammer in search of a nail … 

To ensure a focus on the most valuable use cases, AI initiatives should be assessed and co-led by both business and technical leaders. Given the significant advancements in AI technologies in recent years, there is a tendency to compartmentalize accountability for AI with functional leaders in IT, digital, or innovation. This can result in a “hammer in search of a nail” outcome, or technologies being rolled out without compelling use cases. The orientation should be the opposite: business led and value focused. This business-led approach follows successful adoption approaches in other digital waves such as mobile, social, and analytics.

McKinsey graphics on AI:

Five years as part of a small team doing important work

It’s good to reflect, like many of you I spend a lot of time looking forwards, setting goals, moving forward, thinking about what we’re going to achieve. That’s what gets you far, but I’ve learnt it’s also important to look back, see how far you’ve come. That gives motivation, helps push on through the inevitable dips. Today was a good day to reflect – being 5 years since I started work at Redington.


5 years isn’t that long, in the context of a whole career, but its long enough to make a decent dent in things (if not in the universe then at least in one’s own small part of it) and really achieve stuff. Of course it’s also a period of time which will include some dips and speed bumps along the way (important to recognise that too). I do feel genuinely proud of what we’ve achieved and where we’ve come over last 5 years, more of that later.


As most of you’ll know I didn’t join Redington as a startup. I don’t have any stories of working in Rob’s bedroom or Dawid’s attic (as fun as I’m sure that was). I joined a 45 ish person firm in 2012 that was already working with many of the largest pension schemes in the U.K. It’s easy to remember my first days/weeks as it was just before & during the London olympics in summer of 2012. As many of you will know I spent the previous 5 years living and working in Sydney(note – in case there is still any doubt I’m not Australian: David spent the first year I worked with him thinking I was Australian, and introducing me as such – thought initially was a joke then got a bit awkward). During my years in Sydney I had a desk looking out over the harbour – straight to the ocean, getting the ferry to work. Immediately prior to starting at Redington I’d been travelling 4 months south east Asia. In fact I landed in London on a sat morning from a kick boxing camp in Thailand, started work on the Monday having bought a pair of shoes and a shirt over the weekend. So I turned up to old street on the first morning, mega relaxed, great tan and probably more of a hint of an Australian accent than I’d care to admit. Clearly neither lasted for long!


Of those 45 people around 25 are still here – e.g. Rob, Dawid, David, Pete, Alex, Jonny, Karen, Steven + others. And about 100 have joined since. It’s given me a great amount of pride and pleasure to build what we have here over those last 5 years & I really hope those people who’ve been around for some or all of that journey share that feeling, I really enjoy doing great things as a team and it’s great to look back and see what we’ve achieved together, inevitably there are dips and road bumps, and false starts – but seeing things in the round it’s overwhelmingly positive, couple of examples


Clients are of course a big part of the story of the last 5 years, and doing great things for clients, doing the right things, is at the heart of it. Too many examples to even scratch the surface but two in particular to mention:


  1. Doing the work to put the second LDI manager in place for the PPF. (2013) A highlight because, many bright and capable people in that organisation and they select from a panel of top consultants, so always a privilege to be chosen to work for them. But also because of the reach and impact of the PPF – supporting pension payments to a quarter of a million and counting pensioners & their families from schemes of failed companies.



  1. SJP, winning in competition a mandate to advise SJP on their fund range (2014), here I really started to see the power of the combination of skills we had in the firm, and it was really rewarding to win that mandate as part of a team alongside Pete, Pat and Rob.


Second theme is building assets internally, again so many things there I could mention, but one stands out:


Seeing blender and later toaster get built up from nothing to what we have today (2013-present) – observed that from a distance rather than being closely involved – hope that those of you partly or fully involved in that look on that with a great deal of pride, developing something like that from scratch isn’t the sort of thing you get to do many times in a career, great team effort to have produced the asset we have today over that period of time. That’s just one example and I know there’s a lot more to come there in the future too.


Third theme learning – learnt a lot, surprised me in a way, was a bit unexpected. Not that I thought I was the “finished article” back when I joined the firm but having spent much of my 20’s doing exams (university, masters, actuarial) and starting work, I suppose at the time I thought it was natural that I would be using those skills more rather than learning new ones, I was completely wrong on that! I might even go as far as to say I’ve learnt more so far during my 30’s than I did in my 20’s – certainly more relevant and deeper stuff. Particularly grateful of learnings from Rob, David, Mitesh.  things like: knowing your inner chimp, tackling tough conversations, setting the context, working in the feel space. If you’re interested I’ve blogged in more detail about this here and here.


So, to sum up, Seth Godin put this really well in one of his blogs – a manifesto for small teams doing important work – and that really sums up how I feel about working here (and I know that’s how many of you feel as well) – done a lot of important work over last 5 years , with the team we have today am confident we do even more over next 5. Genuinely mean it when I say that the energy and enthusiasm you all have inspires me, pushes me and gives me that spring in my step each morning.  I’m proud of what we’ve achieved,  but above all really working at Redington has kept life interesting, really means a lot to me to work somewhere you have a spring in your step walking into the office, a sense of purpose, some thing that gets you out of bed each morning, that’s what it’s about isn’t it, at the end of the day.


Books – The Checklist Manifesto

A mixture of task & communication checks help manage the problem of proliferating complexity in the modern word – that’s the relatively simple premise of Atul Gawande‘s short, but excellent book on checklists – The Checklist Manifesto.

The book is driven mainly from a medical context, that being the author’s background, and centred around the astounding data from a study supported by the World Health Organisation into the power of checklists. Although the context is broadened to be applicable to many facets of modern life-  examples and applications are also cited from construction, aviation and even finance. The humble checklist can dramatically improve baseline performance – perhaps more so than even the best new drugs or surgical technologies.

Gawande draws a key distinction between two types of error: (1) errors of ignorance (where we don’t know enough) and (2) errors of ineptitude (failing to correctly apply what we do know. Most of the failures in the modern world are of the second kind.

What were the key insights?

Well, here’s a checklist –


Books – The Undoing Project

My beachside reading on the recent winter trip to Australia was the excellent “The Undoing Project” by Michael Lewis.
Obviously when it comes to Michael Lewis expectations are high, both for the quality of the writing and depth of the research behind it. This is no exception. Some of the specific elements are familiar but Lewis does  great job of weaving the intellectual content of the Kahneman/Tversky collaboration into a compelling story about their lives and the contemporary history of the time. Which are plenty interesting in their own right. I’d say the only negative points would be an oddly-placed chapter at the start which rehashes many of the ideas from MoneyBall (it was interesting, just seemed oddly placed relative to the rest of the book) and the slight lack of compete chronological sense of order that comes with the style of hopping around and pursuing digressions. It probably makes the book more readable, to be honest, but I found myself having to go back and review sections to get the full Kahneman/Tversky timeline over the years straight in my mind.
Some of the key behavioural science insights of Kahneman and Tversky that Lewis covers and articulates so well include the following.
Kahneman and Tversky understood that the errors the mind made offered you at least a partial insight into the mechanism behind decision making. A bit like optical illusions offering an insight into the workings of vision.
“Features of similarity” Comparing two objects: the mind tends to make a  list of features, count up and compare the features that two objects have in common, in particular one object with reference to the other. for example Tel Aviv is frequently thought to be like NYC but NYC is not thought to be like Tel Aviv. NYC has more noticeable features than Tel Aviv. An absence of a feature is also a feature. “Similarity increases with the addition of common features, or the absence of distinctive features.
Transitivity in decision making. transitivity violated if someone picks tea over coffee, coffee over hot chocolate and then turns around and picks hot chocolate over tea. The features of similarity model helps explain why people will violate transitivity in this way. The context in which a choice is presented affects the choice. When presented with a choice people aren’t assessing each object on a linear scale and evaluating relative to some representative model of ideality, they are essentially counting up features they notice. but the context in which a choice is presented can have a big effect on the features that are noticeable. for examples two Americans meeting in NY vs meeting in Togo. “The similarity of objects is modified by the way in which they are classified”.
First heuristic – representativeness. When people make judgements they compare whatever they are judging to some model in their minds. How closely do the approaching clouds represent my mental model of a storm? How closely does Jeremy Lin represent my model of an NBA basketball player? It’s why players with Man Boobs don’t get selected in the NBA. It’s not that the rule of thumb is always wrong – in many ways it can work quite well. But when it does go wrong it does so in systematic ways.
Second heuristuc – availability. the more easily you can recall a scenario to mind the more “available” it is, and the more probably we find it to be. For example words starting with K vs words with K as the third letter. Again can often work well. But not in situations where misleading examples come easily to mind.
People predict by making up stories
People predict very little and explain everything
People live under uncertainty whether they like it or not
People believe they can tell the future if they work hard enough
People accept any explanation as long as it fits the facts
The handwriting was on the wall, it was just the ink that was invisible.
Man is a deterministic device thrown into a probabilistic universe
Theory of regret – emotion linked to “coming close and failing”. it skewed decisions where people are faced between a sure thing and a gamble. regret is associated with acts that modify the status quo. The pain is greater when a bad decision led to a modification of the status quo vs one that led to a retention of the status quo. Regret is closely linked to responsibility – the more control you felt you had.
Anticipation of regret is actually as powerful as regret itself. We look at a decision and anticipate the regret we might feel. Often we do not experience actual regret as it is too difficult to be sure of the counterfactual.
This all contravened expected utility theory  (which was a central part of some economic models of how individuals made decisions). Expected utility theory wasn’t just wrong, it couldn’t defend against contradictions. The Allais paradox was a good example that violated utility theory. it basically had two examples framed at different probability levels but with the same utility tradeoff underlying both of them, people chose differently depending on the framing of medium odds vs long odds.
A greater sensitivity to negative outcomes – a heightened sensitivity to pain was helpful for survival. A happy species endowed with infinite appreciation of pleasures and low sensitivity to pain would probably not survive the evolutionary battle.
Prospect theory – people approach risk very differently when it comes to losses rather than gains. risk seeking in the domain of losses and risk averse in the domain of gains. people respond to changes rather than absolute levels. but changes vs some reference point, some representation of the status quo. In experiments this is usually clearly definable, in the real world, not so much.
People also do not respond to probability in a straightforward manner. people will pay dearly for certainty. But they will treat a 90% probability as less likely than that (they do not treat a 90 chance as nine times more likely than a 10 chance). When it comes to small probabilities they do not treat a 4% chance as twice as likely than a 2% chance. if you tell someone one in a billion they treat it more like one in ten thousand – and worry too much about it (and pay more than they ought to rid themselves of that worry).
One consequence of prospect theory is that you should be able to alter the way people approach risk (risk seeking vs risk averse) by presenting problems framed in terms of losses rather than framed in terms of gains.
The endowment effect (Thaler) – people attach a strange amount of extra value to what they own (compared to what they don’t). they fail to make logical trades and switches.
The Undoing Project. The title itself refers to a theory similar to regret: counterfactual emotions, the feelings that spurred peoples’ minds to spin alternative realities. The intensity of emotions of “unrealized reality” were proportional to two things: the desirability of the alternative, and the possibility of the alternative.
Experiences that led to regret and frustration were not always easy to undo. Frustrated people needed to undo some feature of their environment, whereas regretful people needed to undo their own actions. but the basic rules of undoing are the same, they require a more or less plausible path to an alternative state. Imgination wasn’t a flight with limitless possibilities, rather it’s a tool for making sense of a world of unlimited possibilities by limiting them. The imagination obeyed ruled: the rules of undoing. The more items that were required to undo the less likely the mind would undo them. “the more consequences an event has the larger the change that is involved in eliminating the event.” also, an event becomes gradually less changeable the more it recedes in time.

Four Things I Learnt at Work in 2016

  1. Hack your own productivity, figure out what works for you 
As “knowledge workers” we all carry out a wide variety of different cognitive tasks each day: some are repetitive, some are simple but require a high degree of accuracy, some are creative while others involve problem solving or co-ordination of others. Some involve significant willpower while others may not.
Finding individual ways to maximise our own productivity can be hugely helpful – I firmly believe that the productivity of knowledge workers can easily vary by a factor of 4 or 5 times depending on various factors and circumstances, and some of these are quite simple to understand and change.
Things like choosing which tasks to take on at different points in the day, selecting the appropriate space to work in (working from home being great for some tasks, bad for others), harnessing and using your willpower most effectively and balancing requirements to meet and consult with others with working individually. Creating focus on what’s important (rather than simply urgent), and avoiding cognitive switching.

I was influenced in a lot of this thinking by Charles Duhigg‘s excellent book Smarter, Faster better which I discussed in more detail here. Mitesh Sheth also wrote up this excellent list of productivity hacks, which I contributed to.

2. Approach the world as it is, not as you’d like it to be

2016 was a year of surprises and shocks at a macro political level. Some of the events that took place challenged the world views of people – including myself. The result of the EU referendum left many people – myself included-  feeling more than a little frustrated and angry.

One positive I take from this is the opportunity it presents to acquire really valuable wisdom and experience – for those people open enough to be able to move past the frustration and approach the world as it is.
The reality is, disruptive events will create both opportunities and challenges. Spending time fighting the way the world is probably isn’t the best use of precious resources of mental energy and focus.

3. Understand the Building Blocks of Change

Changing habits at work is hard. Rolling out new systems and processes and changing old ones. It’s so vital to keep operating efficiently, but the extra burden to individuals of change in the short term will also be resisted.

This great blog by Mckinsey helped me greatly in my understanding of the 4 key requirements for workplace change:

  1. An understand of why change is necessary
  2. The capability to make the change
  3. The alignment of incentives and rewards
  4. Role modelling by senior and influential individuals
There is a lot of overlap here with takeaways of books such as Nudge and Inside the Nudge Unit. All fascinating and really powerful stuff if you can find ways to implement day to day. It feels like behavioural insights are rightly having more and more impact on policy & decisions across organisations as knowledge and appreciation of the field grows. Great to see this happening and I look forward to more insights in
4. Beware the Narrative Fallacy
In his great book Black Box Thinking, Matthew Syed talks a lot about narrative fallacy and dissonance – and the effects these can have on decision making, as does Michael Lewis in the equally excellent The Undoing Project.
The hearing and telling of stories is fundamental to who we are as humans. It’s hard-wired into us. It’s part of how we understand and make sense of an uncertain world. It was the way our ancient ancestors explained things to each other and kept children away from danger. We are fundamentally inclined to believe convincing stories.
But there’s a problem, far too often in today’s world stories are constructed that ascribe too great a role to intrinsic characteristics such as talent and too little to luck. Stories dwell on the one thing that worked, ignoring the many that didn’t. Stories can easily make us fall prey to the availability or representative bias, skewing our decision making systematically in unhelpful ways.

Making effective decisions therefore, involves getting beyond stories into data, asking the right questions, and seeking evidence (where it can be found). Testing theories, rejecting hypotheses, trying to assess against a counterfactual and learning as much from the trials that didn’t work as those that did.

2016 was the fifth year-end that I’ve been a part of the team at Redington. As we close one year and start a new one it’s a great opportunity to say thankyou to all my fantastic colleagues who genuinely keep life interesting and make it worth getting up for work each morning – which is what really matters, isn’t it? Here’s to a great 2017 and beyond.

2016’s Most Popular 

My most-read blog posts of 2016 were: 

1. Why Anthony Hilton is wrong about DB pensions 

This post, responding to the misguided (in my view) viewpoints of London Evening Standard journalist Anthony Hilton in September garnered by far the most views of any of my blogs this year (around 1400 views). 

An extended version of this article was also featured in Professional Pensions, and also became their most viewed opinion piece of the year

The article must have stuck a chord with readers in the pensions world. We do of course live in pretty challenging times for DB pension funds, with several strong macro-economic headwinds making it harder to deliver the benefits that have been promised. This year saw a vigorous debate around what should, or should not be done to the DB pensions system. This debate was further catalysed by the high-profile cases of BHS and British steel, and the debate looks set to run on into 2017. 

Given the importance of the DB system to the retirement prospects of millions of members I believe a solid debate on some of these important issues is to be welcomed, and look forward to continuing the debate productively in 2017.

2. No Ordinary Collision – the Future of Asset Management

Powerful forces of change are at play in many industries, and asset management is certainly one of them. Technological and demographic shifts will shape the future of the asset management industry, in this piece (April 2016) I discussed some of  the intersecting forces, drawing on a wide body of existing research on the future of work and finance. 

Here are my six key takeaways: 

3. Consulting firms reply to the Work & Pensions Select committee 

In the wake of the BHS pensions story, the W&PSC issued a green paper calling for views on the future of the DB pensions system in the U.K. Given the prominence of this debate and the considerable air-time it’s received this year the responses from the main actuarial & investment consulting firms were considered and insightful. What was also interesting was the diversity of views. Will most schemes pay the benefits promised? Should the role of TPR change? Should there by wholesale change to the system? Will small changes be effective? Is consolidation feasible? These were all questions on which the consulting firms gave insightful, but often differing answers. 
I hope you’ve enjoyed my blog posts this year. I look forward to sharing more in 2017. Sign up to receive updates on new posts.