Category: fintech

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:

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).