Three steps to extracting business value from AI

Where’s the commercial value?

How do we extract business value from AI? What are the relevant commercial models when talking about AI as a corporate asset? How can we use AI to give us a competitive edge? Answers to questions like this can be hard to find and are sorely needed. AI – like all other assets of a company – does not possess economic value as such, but is a tool that helps the company generate more money for shareholders, or otherwise prosper now and in the future. Companies that wish to harness AI technologies should strive to understand how their assets are turned into tangible business value and competitive edge in the marketplace.

The commercial aspects of AI are not a mysterious or complex “black box” - something that companies just can’t crack. The opposite is true: all companies can and should master the extraction of business value from AI. Many of the thoughts and frameworks related to business value and competitiveness that are already widely used by companies are as valid for AI as they are for any other important assets.

Business value and economic value are multifaceted and complex concepts, but they can be boiled down to a few key elements:

• Growth
• Profitability
• Innovation
• Return-on-investment
• Commercial models
• Pricing
• Company reputation.

In the real world this picture gets more complex fast, as each element contains dozens of different levers and drivers. Management decisions and actions have an impact on each and every one.

In the future, if AI becomes ubiquitous and a kind of “new electricity”, an almost infinite amount of ways for extracting economic value will present themselves, in almost all functions, processes and industries of the firm. But today’s reality is very different. The AI maturity level of most companies is very low, so trying to list all the potential ways to benefit from AI makes little sense at this point.

Constructing a simple process for exploring some potential low-hanging fruits and getting to work on them straight away makes a lot more sense. We see it as a three-step process:

1. What is the nature of your business and the direction you are about to take?

Is your business built on cost leadership? Or on relentless focus on satisfying customer needs? Do you go for fast growth in your industry? Or rely on heavy investments in R&D, innovation, product quality or talent? Do you make long-term investments in heavy assets like real estate or equipment? Think about which are the most critical elements of your business in the coming years and how you are planning on generating more value for customers and shareholders? The choice you’ve made can be your starting point in your commercial approach with AI. When exploring opportunities to extract value from AI, you can build on your company’s current strengths and competitive edge, trying to make them even stronger through AI. Starting with something familiar usually makes a lot of sense.

Potential opportunities and strategies in extracting value from AI

Consider the characteristics and drivers of the industry that you are in, too. For example, if your company is in the retail business, you might be able to create commercial value with AI by optimizing advertising, payments and inventory or by predicting consumer demand. These are all critical for your industry. If your business is focused on manufacturing goods to be purchased by other companies, you might bet on using AI in production automation or in the optimisation of maintenance and logistics.

So the starting point of your commercial approach with AI can be determined by the nature of your business, the industry in which you operate, your competitive position and the direction your company wants to take in the coming years.

Of course, you shouldn’t focus all your efforts in these somewhat obvious areas. Remember to look to other companies with different market circumstances or objectives for insights, inspiration and best practices. Keep in mind that AI is disruptive technology. It might help you change the rules of the game in your industry and allow you to radically rethink your competitive strategy - if that proves necessary or lucrative. So, in addition to emphasising your existing strengths, implementation of AI is also an excellent opportunity to challenge your current thinking and look for completely new approaches in business.

2. What assets do you have in place and what are your commercial goals?

Companies typically develop their AI maturity gradually, moving from one stage to another. First, they realize they have valuable data, data infrastructure and data management processes, nascent algorithms and a decent amount of computing power at their disposal. Then they learn how to develop applications, products and services that harness AI in one way or another. Eventually, companies move towards a coherent C-level management agenda that focuses on fully exploiting AI in practically all operations and functions as well as exploring AI-enabled opportunities to redefine the business the company is in.

Like for the first step, here, too, it makes sense to assess the circumstances. What is your current AI maturity level? Do you have data infrastructure, data management processes, algorithms or computing power? Do you already know how to harness computer vision, natural language processing, face recognition, predictive analytics or other AI capabilities? Do you already have products, services or applications that rely on AI? Or a management agenda that aims to maximize the value of AI everywhere in your organization?

Now return to step one and look at the nature of your business and your objectives. What do you want to achieve? Revenue growth, cost control, asset efficiency, impactful organization or perhaps repositioning your business?

Next, map the two together to see “what you have” (your AI maturity) and “what you want” (your commercial objectives). The picture below illustrates the mapping exercise. Green highlights exemplary company assets that can be turned into serving the commercial objectives of the firm.

The mapping exercise helps you identify how your existing AI assets (and maturity) could potentially be turned into commercial value. It also helps you understand where the obvious gaps that might prevent you from making money with AI soon are, and which actions you should take next to fill them. In case of significant gaps between “what you want” and “what you have”, you should build your commercial approach with AI on existing strengths of your company, rather than to trying to do something that just isn’t realistic yet.

3. Which tactics best capture value?

Once you’ve done the thinking and mapped out the nature of your business, your competitive edge, your industry, your objectives and your assets, it’s time to move to selecting more specific tactics that help you to best capture business value.

Here again, there are several aspects to be considered. Could you make direct revenues by selling your AI assets to your customers? Or could you increase revenue indirectly by, for example, optimizing your commercial operations? Or could you harness AI in order to get the most out of your other assets, such as fixed or human capital? How about repositioning your company in the marketplace via a disruptive new AI-focused vision that inspires and attracts clients? The picture below lists some of the potential tactics “on the menu”.

We’ve chosen 19 different tactics that we see firms taking - ones we consider low-hanging fruits and good starting points for many companies. The list is by no means comprehensive, so you might end up analyzing your company and finding new areas of business where AI could be applicable and deliver great commercial value.

Please note that the optimal commercial value of your AI assets might be generated as a result of the chosen set of tactics. Some AI-enabled applications or products not only help you generate more revenue, but also optimize ways of working or eliminate unnecessary manual work, which has a positive impact on cost savings. So choosing tactics for your commercial AI approach doesn’t have to be an “either-or” choice - it can often be “both-and”. Combining tactics in a creative way is highly recommended. The maximum impact on competitiveness typically doesn’t come from point solutions but from a holistic AI approach that caters various value drivers of your firm. Optionally, you could create an “AI P&L” calculation for your company by listing all the relevant AI application areas and the drivers for economic value. The value drivers in your AI P&L could be something along the lines of “By applying AI in our more intelligent products, we can increase their unit prices by 10% in our business area X” or “By applying AI in our knowledge management, we can resource our development projects 20% better next year”.

The final AI P&L of your company is a consolidation of these identified drivers and their estimated business impact.

Next steps?

Futurice has worked with many companies, like Supercell, Barona, Exfo, Kesko and many more, helping them build their AI assets so that they can create more commercial value and improve their competitiveness, too.

The three-step process presented above is our first stab at demystifying the topic of extracting commercial value from AI. They really boil down to:

• The why (your objectives, competitive edge and direction)
• The what (your assets and gaps)
• The how (your tactics).

Start small. Launch experiments that aim to extract value from AI - like a pilot with the tactics mentioned above or an asset/gap analysis - take what you learn and iterate to refine your approach for rapid advancements and maximum value. Co-creating your AI plan with your clients helps you better understand their willingness and ability to pay for your AI assets and knowhow.

Obstacles to benefiting commercially from AI often come from inside of the organization. Few companies have yet understood the extent of the transformation needed when applying AI to business. Commercial opportunities may look lucrative, but the work effort and time needed for results might be overwhelming for many.

Companies that have relied on detailed plans, strict targets, corporate rules and incremental change will often find it hard to thrive in the AI age, when business is being run through observation, iteration, prediction and continuous learning based on new data.

True improvement in competitiveness requires a big change in thinking, management attention, and dedication and commitment from the organization. In companies that have cracked it already, several C-level people are spending time on the topic and driving the development. There’s typically a Chief Digital Officer in place who looks after the AI development across the organization. Basically, the managers of the firm need to ask: are we willing and able to do what it takes to harness AI for competitiveness, business value and company renewal?

Going forward, it would be great to see business people pay closer attention to understanding AI, getting more active in the current, quite lively AI discussion and contributing in areas like refined commercial and business models as well as tactics that can help companies gain more competitiveness from AI. A more multifaceted discussion of the subject would help all of us address AI as a multidisciplinary question, outside the silo of software development and data science. We would like to see a greater interest in C-level executives and board members of companies trained to better understand AI, resulting in better dialogue around commercial aspects of AI on throughout organizations.

There are plenty more opportunities for mutual learning for all.

What do you think? Was this approach useful, tangible and simple enough? Did it help you to understand the topic better? Could it be applied in your company? How would you make it even better? Please let us know and send your thoughts by emailing mika.ruokonen@futurice.com.

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