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What every CEO needs to know about AI. Part one: growth

It’s a mistake to simply delegate AI to your digital team. Here’s how you can use it to transform every aspect of your business

By Abhijit Akerkar 25 September 2018

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Artificial Intelligence (AI) allows companies to think the previously unthinkable. Yet, while tech giants such as Microsoft, Amazon, Facebook, Google and Baidu have AI at their very core and harness its potential routinely, most other non-tech companies are only now beginning to wake up to the opportunities that AI is presenting to them.

Few CEOs realise that AI can help them achieve their business objectives and shape P&L. They know how to use organisation redesign, cost-cutting, mergers and acquisitions, business and product launches and geographic expansion, but fail to see that AI is a powerful tool to be added to their toolkit.

Since most CEOs of non-tech companies don’t know how to integrate AI into their business strategies, they tend to delegate the AI work to their digital teams. But these teams inevitably look at it from the technology perspective rather than the P&L perspective. And what I see is digital teams getting lost in the trees at the expense of the forest. The latter needs senior business leaders to fully understand and appreciate how AI can help them.

 Few CEOs realise that AI can help them achieve their business objectives and shape P&L.

Every CEO should be able to reimagine their business and P&L with the help of AI. As a guide, I have identified 17 levers that CEOs can shape with the help of AI to boost their P&L. The framework I have developed (below) can be used both as a barometer for assessing the current state of your teams’ efforts around AI and for spotting new opportunities. The 17 levers show how AI can help drive superior growth, increase return on capital, and manage both desirable and undesirable risks.

This framework will help you have structured and focused discussions with your teams around how to harness the power of AI, looking at questions such as:

  •  What is the potential size of the prize?
  •  At what stage is the market evolution?
  •  What have early adopters achieved?
  •  Which pitfalls to avoid?
framework
Sustain superior growth

Let’s start by looking at growth. Sustaining superior growth is elusive. There is no winning formula. But successful companies seem to get the concoction right of strategy, capital allocation, innovation, market expansion, and excellence in sales and marketing. The last three of these levers have been the focus of the early adopters of AI.


Creating the new

The secret to driving growth is to be bold and develop an entirely new product that wasn’t possible before. Take the example of Amazon’s smart speaker Alexa – now in one in every six American households. It is an innovation that has broken the previous stranglehold that telecoms giants, phone manufacturers and providers of home entertainment systems had on the “voice” coming into the home.

In retail, the online supermarket group Ocado has put AI at the very core of the way it runs its new Customer Fulfilment Centre – something we used to call a warehouse. The company maintains that the time it takes to assemble an order has been slashed from a couple of hours to a few minutes. Algorithms run the warehouse to ensure speed, accuracy and quality – even making sure that a watermelon isn’t put on top of a carton of eggs. And not only does the AI platform allow Ocado to run its own operation efficiently: it has sold the system to other retailers overseas.

Small advances, such as adding some AI to an existing product, might help you stay competitive. But it’s unlikely to turbocharge your revenue or profits unless you are Apple launching iPhoneX with facial recognition feature or you can modularise your product and command price for the AI module.


Reaching a wider universe of clients

Forward-looking companies have started using AI to allow them to serve customers who were previously considered not worth bothering about.

Take the provision of credit. Machine learning can look at data such as an individual’s past shopping habits, the payment of utility bills and rent to build up a picture of their likely credit-worthiness. Armed with this information, a company can then decide whether to grant a loan – even if the person concerned has no past credit history. Globally, about 1.7 billion adults remain unbanked. Meticulous attention needs to be paid, however, to privacy laws and customer consent and preference; the sector is facing a few regulatory headwinds.

 Forward-looking companies have started using AI to allow them to serve customers who were previously considered not worth bothering about.

Similarly, a small business can be assessed when it seeks credit: AI can look at information such as sales volumes, customer reviews and data gleaned from places including Facebook, LinkedIn, eBay and PayPal in weighing up whether a company is likely to pay its bills.


Getting more from existing customers

AI is now close to making mass-personalisation possible. It can bring together traditional data with detailed information about a customer’s behaviour gathered from sources such as online browsing, social media and wearables. This then allows the right product to be offered at the right time with right message. McKinsey has estimated that such mass- personalisation can lift sales by 10% or more.

With Amazon, for example, we already see machine learning exploited to make suggestions to existing customers: they are presented with a recommended “next product to buy”. These ideas are generated by looking at your demographic profile, what you have bought in the past and what has been bought by customers with a similar profile. At one point, some 30% of Amazon’s sales were prompted by its recommendation engine.

Business-to-business companies can adopt a similar strategy by mining past ordering patters and comparing them with the sales made to similar customers.

And don’t ignore AI’s potential for reducing churn and holding on to customers. Machine learning can look at such metrics as the frequency with which a customer logs on, their response rate to emails and how often they call service desks. Then it can estimate the likelihood that a customer will defect to a rival, and intervene to try to reduce the number of cancellations.

Take the example of Netflix. Customers are likely to lose interest if it takes them more than 60 to 90 seconds to find something they want to watch. By using personalisation and recommendations, the company reckons it saves more than $1 billion (£757 million) in revenues that it would otherwise have lost.


Getting the right price

There is no law of nature that says every customer should pay the same price for a given product or service – nor, indeed, why a single customer should pay the same price each time she or he buys a given thing.

Uber provides the best-known example of variable pricing: when demand is high in a particular area relative to the number of drivers available, the price of a ride will rise.

The same idea can be applied to a myriad of other industries. Mobile phone operators are experimenting with the use of machine learning to predict demand and gauge the price sensitivity of small cohorts of consumers. They can then use this information to decide the demand-price trade-off to maximise revenues and profitability.

And in the business-to-business arena, companies can use data science to pinpoint clusters of customers with similar buying patterns, identify similar deals and generate information about what prices are being paid. Arming salespeople with this information can help them negotiate the best possible prices without losing business.


Increasing sales productivity

Around two-thirds of a salesperson’s time is taken up with routine jobs such as making contact with potential clients, setting up appointments, taking orders and preparing contracts. Companies have begun automating activities such as these, freeing up salespeople to close deals, nurture relationships and manage deals that are out of the ordinary.

A company that can identify the leads that are most likely to lead to a sale will have an advantage over its competitors. Companies are using AI’s predictive capabilities to pinpoint the most promising leads and routing those to sales people who are best suited to close those deals based on their past sales history. Additionally, companies are recording, transcribing and analysing sales calls, demonstrations and meetings using AI. Looking at how the most successful sales representatives led conversations helps companies to coach other staff to up their game and secure more deals.


Making marketing spend more effective

Personalisation is now at the core of marketing. It can yield huge gains in the returns achieved on marketing spend.

But forward-looking marketers can now go further. Analysing customers’ behaviour is only the start. Companies can now target their marketing in a way that is attuned to a customer’s behaviour, preferences, and sentiment, creating emotionally intelligent personalised content using natural language processing (NLP) technology which could be deployed at scale across all marketing channels. Citi, the global bank, has employed Persado, a marketing language cloud company and has seen a 70% increase in its email “open rate” and a 114% increase in the click-through rate.

Abhijit Akerkar (MBA2008) is Head of Applied Sciences, Business Integration, Lloyds Banking Group

What every CEO needs to know about AI. Part two: return on capital

Gain a technical and management understanding of using big data to drive highly profitable business decision-making

Comments (1)

Simbo 1 months and 28 days ago

Wow|! This is a wide insight into how to harness the power of AI. I now see things from another angle. Every CEO is looking at how to create, deliver and capture value in economic, social and cultural concepts in a more effective manner and reduced cost to yield increased bottom line. Thus, the research has been able to explain this. Thanks to the research team.

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