New data and AI-enabled business models - How to set the right ambition level

Posted by Futurice on Jun 10, 2020 3:23:10 PM

In the midst of the recent Covid-19 pandemic and the changes in the global business environment that followed, executives have faced many disruptive forces, including changing attitudes and expectations among their customers, tougher global competition, and redundancy or erosion of existing business models.

As a result, many companies have begun to explore transformative data and AI-enabled business models, including selling data, provision of data platforms, implementation of new data and AI-enabled service concepts, and many more.

These new business models may help firms redefine their recipes for successful and stable business for the long term, thus adding resiliency to their business. This might mean e.g. reducing dependency on legacy businesses and assets, staying relevant to customers, opening new avenues for growth, or differentiating from the competition. The importance of new and existing experimental and exploratory initiatives tends to grow in turbulent times.

The key decision that a company wishing to renew business models with data and AI needs to make is: What is our ambition level?

● Are we satisfied with ‘augmenting’ or ‘boosting’ our existing business models and offering with data and AI, i.e. taking a rather conservative and incremental approach? Nothing wrong with that, of course!

● Do we want to start exploring further horizons and areas beyond our current business, e.g. new markets or customer segments or new offerings?

● Do we wish to attack a totally new industry segment by harnessing our data and AI assets – which brings with it totally new customers and a new competitive environment?

Let’s explore these options.

The incremental approach - Boosting existing business models

It’s unlikely that the majority of companies would choose a radical approach to their data and AI-enabled business model transformation. For many, the best and easiest choice is to start with the ‘obvious’ opportunities, the low-hanging fruits, or boosting existing businesses.

This might mean taking a specific AI technology, such as image recognition, robotic process automation or speech recognition, and applying it to a core process in order to gain improvements and, finally, turn the improvements into value for the existing customers in the existing markets through an existing offering and revenue model. In many industries, this is still disruptive and helps management make smarter decisions and increase competitive advantage against competitors. A data and AI-enabled company – but within the existing business framework – is still a data and AI-enabled company. That alone can be revolutionary in its own right and it’s revolutionary enough for plenty of companies.

The important question here is whether incremental improvement in the existing business is enough in the long run, given that many companies currently invest in data and AI, and some of them might potentially choose a more disruptive approach that might at some point threaten the more risk-averse companies.

Going for growth - Looking for new offerings and opportunities

If the company decides to raise its ambition level and start to seek for totally new offerings and opportunities through data and AI, it taps into a whole new universe of interesting problems worth solving and significantly bigger business potential: major differentiation against competitors, opportunity to raise competitiveness to a new level, the opportunity to claim a completely new position in the industry value chain and a transformative opportunity to create a big amount of new value. And much more.

Naturally, the risk of failure increases as well. The further a company moves from its traditional core business, the more uncertainties there are from a reputation, legal, technical, or business perspective. Deciding on the appropriate ambition level should deliberate and carefully thought out. It may have long-standing implications.

One can also make an important distinction between “AI-inside” offerings and “AI-first” offerings. Whereas AI-inside design means that a few AI features have been added to an old offering, the AI-first design commonly means that the “deal between what humans do and what machines do has been renegotiated”, and by using the AI-first offering the humans will stop doing something that they used to do by themselves.

In AI-first offerings, there are typically a handful of deep AI-centric features that remove legacy features altogether and create an entirely new, AI-centric product experience in which the offering cannot survive a broken AI.

Playing the transformation game - Entering new industry segments

The ultimate disruptive move, thanks to data and AI, would be to enter a completely new industry segment. Let’s look at two examples.

A company sells connected products (cars or production machines, for instance), and through data and intelligent systems learns to know and predict the usage patterns of its products, it could potentially start selling insurances for its devices. The data and AI technologies in this case would be used to calculate potential errors or maintenance needs in products and this insight would then be turned into probabilities - which are needed in the insurance business. Tesla, for instance, has already started selling its own car insurance in California.

A telecom company serves its consumer customers with mobile phones and related subscriptions and collects valuable data on their location and creditworthiness. It could leverage the data and build alternative and complementary revenue streams from banking services, like French telecoms giant Orange has done: it launched its banking services in 2017 and has since become a major challenger to incumbents such as BNP Paribas, Societe Generale and Credit Agricole in the French market.

How to make the smart choice

Given the many options that companies have, what is the recommended ambition level? There is, of course, no standard universally applicable answer to this question. It depends on the market, the competition, and the company as well as its executives themselves. The right answer can be explored by asking, for instance, the following questions:

● Where do we see the most lucrative data and AI opportunities? Within existing markets? Beyond them, e.g. among new customers or offerings? Or perhaps between industry segments?

● After studying our market and customers, do we see an opportunity to disrupt our own industry using data and AI? How could we drive disruption and take advantage?

● Which other companies have data and algorithms that enable them to enter our industry segment and disrupt it? If that happens, what is the impact on our company and what could we do?

● Which new markets or industry segments could we potentially enter using our data and algorithms?

● Would some other company have relevant data and AI capabilities that could help us enter a new industry segment? If so, should we collaborate with them?

● Given the market and the competition as well as the opportunities we see, is taking the incremental approach enough? Would we be satisfied following others, and by doing that, staying in the game?

● Do our capabilities, organization, leadership style, and company culture favor driving data and AI-enabled disruption or choosing an incremental approach?

As the examples above illustrate, there are endless opportunities for companies to renew their business using data and AI. There is strong evidence that most successful companies in the world are not confined by industry boundaries or industry forces - instead, they often try to crush those and disrupt others.

There is no right or wrong when it comes to a company’s data and AI-related ambitions. Not every company needs to become an AI-first company. Not every company needs to develop completely new data and AI offerings and revenue models. Or hunt for new customers. Or attack new industry segments. Ambition is always a matter of choice and sometimes a prudent incremental approach might be a smarter choice than the transformative, radical, and risky one.

Whatever you decide, it’s important to follow the latest developments in data and AI and not miss data and AI-enabled market opportunities.


By Mika Ruokonen, Artificial Intelligence Transformation, Industrial Clients, Business Director at Futurice


Topics: AI, Data, Machine Learning, Business