/Building your business strategy for AI (via Qpute.com)
Building your business strategy for AI

Building your business strategy for AI (via Qpute.com)


Ever since Klaus Schwab, the founder of the World Economic forum, first coined the phrase The Fourth Industrial Revolution in a book of the same name in 2016, many of us have been trying to define what it means to be living and working in the fourth industrial revolution.

Some argue that it marks the period when the technological and physical or biological start to fuse. Others prefer to define it in terms of the confluence of numerous, yet distinct, tech ‘mega trends’ which currently includes artificial intelligence (AI), robotics, 3D printing, drones, IoT, virtual reality, but could equally include quantum computing, genetic engineering and more.

However, a key element of this change is undeniably AI. AI and its subset machine learning (ML), focuses on the development of computer programs that can teach themselves to learn, understand, plan and act when provided with data — from speeding up insurance claims to prompting our online entertainment choices.

Financial institutions are already making large-scale investments in AI. According to financial research firm Autonomous, by 2030 financial institutions can look forward to a 22% cost reduction in operating expenses due to AI if they apply ML to some of their key business processes and customer interactions.

A report from PwC entitled Global Artificial Intelligence Study: Exploiting the AI Revolution, estimates that AI will contribute $15.7 trn to the global economy by 2030.  AI is expected to boost North America’s GDP by 14% by the same year.

So, where are the AI early adopter use cases? Here’s a few that I spotted:

1. AI-led Insurance claims processing: Online insurers can leverage AI ‘bots’ to automate the claims process from start to finish. Instead of the days or months it traditionally took to settle a claim, an AI bot can complete the whole process from claims receipt, policy reference, fraud detection, pay-out and notification to customers in three seconds.

2. Most personal lines underwriting will be driven by AI: McKinsey predicts that by 2030, manual underwriting will have ceased to exist for most personal and small business products across life, property and casualty insurance. The process of underwriting will shrink to seconds as most of the underwriting is supported by AI/ML tech.

3. Your content preferences will be controlled by AI: Video and music streaming companies already using AI-based autonomous recommendation engines that combine segment trends, ratings, and content similarity to personalise suggestions and engage consumers. Such engagement not only increases retention, it’s claimed, but also enables content providers to collect extra data on individuals, improve the personalisation of offerings and sell more.

4. Human capital management led by AI: Juggler is PwC’s own AI-driven HCM solution for getting its 16,000 valued people deployed optimally across 20,000 billable engagements, at the right date, for the right amount of time and utilising their unique sets of skills.

Many now argue that if businesses that want to continue growing from here on out must build, trial, and ultimately roll out AI tools and strategies over the next few years.

Building your AI strategy

But where to start with an AI Strategy for your business? Start with a pressing business requirement or constraint – perhaps a process which is proving cumbersome and expensive to run today or better still, something that is not only costing large and growing amounts of money to run today but also an area that is putting your business’ reputation at risk if you do not get it right.

This naturally leads you in the direction of an interface with your customers. In the platform world, this might lead you to use ML to iteratively improve client onboarding processes or pensions transfers, for example.

Can you create an AI chatbot capable of answering over 80% of new customers’ questions and requests which they generally ask in the process of setting up a SIPP through your platform for example? Can ML be applied to weeding out the remaining 20% of responses or queries which demand referral to an automated triage service? The triage service can then establish if the customer really should be advised to talk to a regulated adviser.

If there is one AI-driven technology that is seeing wide application across multiple markets right now its chatbots. According to Gartner, 70% of white-collar workers will interact with a chatbot daily by next year. For Juniper Research, chatbots will deliver $112bn of cost savings across the financial services, healthcare and retail sectors in 2023 alone.  Let’s take a closer look at selecting the right target for an AI project:

Start with your people

If it’s proving a struggle to lock onto a target for AI ‘super charging’, you might want to ask your staff what tasks they find boring, time-consuming, uncreative and unmotivating. For example, in the pensions administration world there is far too much rekeying of client data still going on to get policies on the books. Can AI be applied to automating the process of data transfer, flagging when manual intervention is needed to check an anomaly, complete or edit a record? Once this data cleansing work is taken away from administration teams they will be freed up to do more creative work to find ways of improving or streamlining systems and processes.

…Or with your customer

You could instead start by investigating your customers’ main pain points. Where are you seeing the most customer complaints or which process is taking so long it is attracting wider industry criticism? Explore which types of complaint are leading to the highest loss of business and which is also exposing the business to reputational damage.

What’s the knock-on effect of these target issues for your admin or servicing teams? Once you have defined those priority areas, properly define what that is costing your business annually. More positively, explore whether new digital channels can be created for servicing and supporting customers without tying up additional resources.

Perhaps the highest-profile wins for AI are in the area of helping customers to complete processes themselves without having to pass a request back to the provider’s admin or customer service teams for actioning. It ought to be possible for an adviser to go into a personalised account view within an online portal to request a valuation for one client; rebalance a portfolio for another; set up a larger contribution; or alert the provider to the need to issue a wake-up pack as one of their customers approaches age 55.

Indeed, this dashboard view ought to be able to provide the ages of an adviser’s entire customer base and then flag a series of options for better serving customers reaching key milestone dates like stated retirement age target, state pension age, age 75 and so on.

Is it about improving customer self-service, making it more dynamic and memorable and helping to command customer loyalty?  The best self-service offerings also have fantastic AI-driven capability of knowing when a customer should speak directly with someone inside your organisation. That digital interface to human handover needs to be seamless.

Ethical governance

Go back to the ethics around the building of robots which were discussed by the likes of Isaac Asimov: “A robot may not injure a human being or, through inaction, allow a human being to come to harm.”

AI creates new risks and data privacy issues. For this reason, PwC has developed a Responsible AI Toolkit. The foundation for Responsible AI is an enterprise governance framework, focusing on the risks and controls along your organisation’s AI journey—from top to bottom.   PwC (and most other consultancy group) have developed robust AI project governance models. These frameworks enable oversight and clear roles and responsibilities, articulating requirements and mechanisms for traceability and ongoing assessment.

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Source: PwC Responsible AI Framework

It’s important to deliver services that support your customers but do not come at the expense of your employees’ jobs or indeed the quality of the work they do today. In other words, think in terms of creating services that improve pressing problems for customers and/or open up routes to improved efficiencies, new sales and increased customer loyalty and value. Build with growth and upgrading the quality and intellectual challenge) of work in mind, as well as to improve customer experience, value and loyalty. Don’t build to make departments redundant.

Finding the AI project ‘sweet spot’

If there are a large number of data points and lots of data to sift through, then AI is likely to be able to help pull that data out and analyse it fast. Can this work be combined with building an automated digital customer journey for a specific process involving a number of explainable choices such as drip-feed drawdown splits? Is it about automating some processes to deliver actionable intelligence back to the customer in answer to their query, all at any time of the day or night?

If so, then the benefits of the changes are likely to be obvious to customers, your administration staff, as well as senior management looking for rapid return on any technology investment required.

Perhaps the ultimate sweet spot for any AI trial development then is to define projects that promise to improve your customers’ experience of your products or services, while simultaneously reducing or taking out mundane, manual tasks which tie up lots of your employees’ time but are neither rewarding nor creative.

Chris Read is group CEO at Dunstan Thomas

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