How Legacy Businesses Can Switch to a Platform Model

0

The growth of artificial intelligence has enabled a variety of new strategies and business models, from programmatic ad targeting to the sharing economy to the metaverse. The companies that have been most successful in using these models – digital natives, almost only one – have been “multi-sided platforms”, in which a company at the center of an ecosystem or a corporate network coordinates the services and reduces friction for customers. Facebook, Apple, Airbnb, Amazon, Google, Uber, Alibaba, Tencent and the other major platform companies have created value at astonishing rates.

It is not a coincidence. Research has shown that multi-sided platforms have the highest valuations of mainstream alternative business models – more than four times the annual revenue multiples attached to some legacy business models. This is largely because they grow rapidly and need to own relatively few assets themselves.

Platform business models typically generate large volumes of data from all ecosystem participants, and AI is needed to make sense of it all. Machine learning helps match customers with the products and services they need or want, and provides a seamless experience across the ecosystem. And the millions of customers who use the platforms need highly efficient customer service, i.e. intelligent agents and chatbots. So it’s no surprise that the top platform companies listed above are also global leaders in applying AI to their businesses.

But traditional companies can also organize multi-sided platforms. They too can use data and AI models to orchestrate services for customers in a business ecosystem. This requires new strategies, new technologies, and new business relationships, but when businesses successfully transition, they can achieve the rapid growth and customer retention that native digital platforms have accomplished.

There is evidence that more traditional companies that use AI aggressively take an ecosystem approach (and perhaps eventually a platform-based approach). In the Deloitte 2021 State of AI in the enterprise survey, the top two groups of AI users in the survey were significantly more likely to have two or more ecosystem relationships (83% among the top two groups, versus 70% and 59% among the top two). weaker groups). Companies with more diverse ecosystems were 1.4 times more likely to use AI in a way that differentiates them from their competitors. Additionally, organizations with diverse ecosystems were also much more likely to have a transformative view of AI, have enterprise-wide AI strategies, and use AI as a strategic differentiator.

These companies may not have full-fledged platform business models, but building broader ecosystem relationships is a first step toward AI-powered platforms. Beyond that step, here’s how companies are transforming into platforms with AI.

Not just digital natives

A few “legacy” companies have already created AI-enabled platform models. By using these models, companies generate more customers, leading to more data, leading to better models, leading to better customer offers – a virtuous circle. Others are at earlier stages, but hope to eventually achieve the same result.

Perhaps the best example of the virtuous circle of platforms is China’s Ping An, which started as an insurance company in 1988 but now describes itself in terms of its five ecosystems – financial services, healthcare, smart cities, automobiles and real estate. — each of which constitutes a platform. In healthcare, for example, Ping An’s platform connects government, patients, medical service providers, health insurers and technology. The Ping An Good Doctor system offers online and in-person consultations, and uses AI to deliver medical advice to members with mobile devices.

The size of the ecosystem is staggering – it provides diagnosis and treatment for over 3,000 common diseases, has nearly 350 million users, over 1,800 doctors and nurses and nearly 10,000 healthcare experts across China. It is a partner of 110,000 pharmacies, 49,000 clinics and more than 2,000 medical examination centers. In 2020, he performed more than 830,000 medical transactions per day. These numbers not only illustrate the size of China’s population, but also the rapid scaling possible with a platform-based business model.

Although the main value of the platform is to develop the business and provide efficient healthcare, it is also useful for accumulating information to train AI models. The Ping An healthcare ecosystem can rely on payer claim and payment data, healthcare provider treatment data, pharmacy prescription data, patient symptom data and others. types of data from other members of the ecosystem. As of 2020, Ping An had data on over 30,000 diseases and over 1 billion medical consultation records.

Several other AI-powered companies, including Skywise from Airbus, Shell, Anthem and SOMPO in Japan, are also pursuing the ecosystem idea, but are at earlier stages than Ping An. At this point, they are exploring still business and revenue models, but are pursuing approaches to sharing and integrating data, and beginning to develop AI applications to analyze data.

How Midsize Businesses Can Compete

However, it’s not just big companies with big R&D budgets that can make this pivot. CCC Intelligent Solutions, founded in 1980, exemplifies how a mid-sized company can compete effectively using an AI-powered platform model. Its platform is focused on digitizing the auto insurance economy and reducing claims and damage repair friction for millions of drivers every year. Through its relationships with over 300 insurers, over 27,000 repair shops, over 4,000 parts suppliers and all major automotive OEMs, it has amassed over $1 trillion in historical claims data, billions of historical images and other auto parts data. , repair shops, collision accidents, regulations, telematics and several other entities. As with many of the other ecosystems mentioned above, each new member brings more value to the network and more data, which leads to better AI models.

CCC aggregates data – and increasingly powers AI-based decisions – for its platform to quickly and efficiently address end-user complaints. All resulting transactions take place in the cloud, which connects 30,000 companies, 500,000 individual users and $100 billion in business transactions.

Over the past few years, CCC has developed a “contactless” complaints offer which is used by the USAA and other major insurers. Insured customers involved in an accident can take guided photos on their mobile devices, send them to their insurer, and receive an automated estimate in seconds. Such AI-powered innovation required years of technological refinement, as well as working with ecosystem members to embed the capability into their claims and repair processes.

What it takes to succeed with AI-based platforms

The companies mostly have different business needs and provide different services, but there are commonalities in how they have approached their platform pivots. Businesses that want to build and thrive with AI-powered platforms need to complete a series of steps. They understand:

Strategize how ecosystem relationships will enhance your offerings and seek out those partnerships.

The business strategy will dictate the platforms your business should create and how that will enhance its products and services. Implementing the strategy may require the creation or purchase of new business capabilities. Ping An, for example, decided that instead of just offering insurance services, it would build a financial supermarket for customers. It already had some capabilities, but it built a wealth management offering (Lufax) and bought a car portal (Autohome).

Make sure the data is provided with the relationship.

Much of the value of the platform lies in access to partner data. So make sure that partnership agreements include access to the necessary data and the ability to use it in AI models such as customer/deal matches and recommendations.

Develop an API-based IT service architecture.

Ecosystem partners will need easy access to data and decisions made by AI systems. By far the easiest way to do this is to use Application Program Interface (API) architectures. CCC, for example, has built its cloud-based API network that allows vendors to easily interface with the business.

Identify key decisions that AI needs to make and gather data to train models.

In most cases, AI will be used to make a decision. For Ping An’s healthcare platform, key decisions include what a patient’s most likely diagnosis is, whether the patient should see a doctor, and what treatment is recommended. Decisions facilitated by CCC’s platform include the exact damage to a vehicle and the cost to restore it, which ecosystem partners should be involved in the repair, and what services are needed.

Design a process that is transparent from the customer’s perspective.

A big part of the appeal of a platform model for customers is to remove friction so they don’t have to understand all the participants and the complexities involved in a solution, whether medical treatment, collision repair or aircraft maintenance. Companies building a platform should work with their partners to design and implement a smooth and seamless process to meet customer needs.

Use data from across the ecosystem to improve models and offerings.

The machine learning models that power platform decisions are not a “set and forget” approach. They will improve in prediction or recommendation over time if they are retrained on new data. They should be retrained whenever new major data sources appear, or when they are no longer doing an effective job at the decision they are responsible for.

The native digital platform companies and legacy companies we also studied illustrate the value of an AI-powered platform business model for companies and their customers. It’s hard to grow quickly without a close set of business partners, and it’s hard to make sense of their data and deliver value to all parties without AI. We expect to see many more of these platforms in the future.

Share.

Comments are closed.