Tag: Future

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.

tl;dr

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:

The Future Shape of Asset Management – 1 Year On

A year flies by. It’s been twelve months since I first put together No Ordinary Collision: The Forces that will Shape the Asset Management Industry, a thought piece bringing together many pieces of work and research on mega trends, and which identified particular intersections between emerging trends that could be meaningful for the asset management industry. The aim wasn’t to try and make concrete predictions, or envisage a particular world. But rather to identify and observe trends and themes that might be important – many of which have already been widely covered and researched – and envisage how these might interact to influence the world of finance.

A year can fly by but at the same time it’s fascinating to see how much these trends have developed and thinking has moved on. At the end of last year’s piece I concluded with a checklist of 6 key takeaways for those of us in the industry to best position for the changes on their way. A year later I asked myself the question whether these need updating, but I remain happy that these are still broadly the most relevant themes to focus on.

Download the full document here >>

NO ORDINARY COLLISION 1 YEAR ON

Key milestones over the last year in Digital / Customer Centricity:

update-redington-20175

Key takeaways for the future – 

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 2017.mckinsey
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.

Roboadvisor Europe 2016 – The Future?

The future of asset and wealth management?

A thoroughly excellent event was organised by Level39 in London on 25 May 2015, featuring speakers from all the key players and analysts in the fairly nascent European roboadvisor scene and around 250 delegates.

My five top takeaways are below, or you can read my storify story here.

1. Mind the 2016 inflection point

Rohit Krishnan of Mckinsey made this point, but it was echoed by others. The growth rates of the two longest stablished US roboadvisors (Betterment and Wealthfront) have stalled somewhat, possibly co-inciding with the robo launches of two large incumbents Vangard and Charles Schwab. With significantly lower AUM than is probably needed to justify costs and valuations, 2016 could be an inflection point, which way will things tip?

2. Customer acquisition cost is key

It’s a closely guarded secret when it comes to individal firms, but surveys and other data in the public domain suggest that customer acquistion costs can be in the region of $300 or higher, but lifetime value of an average client may only be $250. If that’s true, then it would seem to pose a challenge to the business model.

3. But Europe is a bit different

Several of the European robos made the point that the European market is a bit different to the US. With less competition on fees in the traditional advised space, European robos charge in the region of 40-70bps rather than 20bps in the US. This means the breakeven point in AUM might be somewhat lower, 0.5-1bn was suggested.

4. It’s all about the api

There was a fascinating panel covering the tech aspects. The main takeaway being that we have entered a new era of openness, and what’s important is opennes with regard to architecture & api , to enable other components “plug in” to create an ecosystem.

5. Scale & brand are hard

These two comments stood out to me from all the points made by the startups. Firstly, building the right scale to reach 1m+ customers is difficult (more difficult than we thought said Shaun Port of Nutmeg). Secondly, several panellists commented that building the wider brand and customer awareness was key, no-one had really done it yet, and many firms were in a race to try and do so.

that’s it! plenty more I could say (and check out the storify for more).

No Ordinary Collision

Your 10-Minute Guide to the Future of Asset Management.

Download the full paper here >> no-ordinary-collision-v6-singlepage-HR

It’s always easy to ignore or dismiss forecasts of the way the future may change our industry. Some might seem too obvious, some too far-fetched. We all exist in a daily whirlwind addressing the challenges of volatile markets and demanding clients.

It’s great to take a step back though and take a moment to think about the future of the fund management industry. that’s what i had some fun doing when I sat down to write: No ordinary collision, your 10 minute guide to the future of asset management. I took the time to read through a lot of the great thought pieces out there relating to these future themes (from the likes of PWC, EY, McKinsey and Forbes) summaraise what I saw as the key themes running through them and add some thoughts of my own.
the way I see it, the asset management world looks set to be affected by future trends and themes from at least two sources,
1. the future of work
2. the future of pensions & asset management
graph6
Each of these taken on a standalone basis are facing considerable change, taken together it creates some powerful intersections that could really change the way our industry functions and the importantly the key value chains within it.
I’ve spent some time thinking abouut what I think are the key intersections (these are shown and discussed in a little more detail below). you can read the fll report here.
Clearly there’s no single blueprint for success in such a changing and disrupted world. I’ve tried to lay out what I believe are 6 key things asset managers can address. I’d love to hear your thoughts, do tweet me to start a conversation.
 6point