- Generative AI is unlike any technology that has come before. It’s swiftly disrupting business and society, forcing leaders to rethink their assumptions, plans, and strategies in real time.
- To help CEOs stay on top of the fast-shifting changes, the IBM Institute for Business Value (IBM IBV) is releasing a series of targeted, research-backed guides to generative AI, on topics from data cybersecurity to tech investment strategy to customer experience.
- This is part five: Platforms, data, and governance.
Generative AI is unlike any technology that has come before. It’s swiftly disrupting business and society, forcing leaders to rethink their assumptions, plans, and strategies in real time.
To help CEOs stay on top of the fast-shifting changes, the IBM Institute for Business Value (IBM IBV) is releasing a series of targeted, research-backed guides to generative AI, on topics from data cybersecurity to tech investment strategy to customer experience.
This is part five: Platforms, data, and governance.
Generative AI is unlike any technology that has come before. It’s swiftly disrupting business and society, forcing leaders to rethink their assumptions, plans, and strategies in real time.
To help CEOs stay on top of the fast-shifting changes, the IBM Institute for Business Value (IBM IBV) is releasing a series of targeted, research-backed guides to generative AI, on topics from data cybersecurity to tech investment strategy to customer experience.
This is part five: Platforms, data, and governance.
Generative AI is disrupting the disruptors—and platform-based businesses have the edge.
From Netflix to Nvidia, today’s top-performing companies are platform-based. Their business models are built on digital platforms that connect people and players to deliver greater value. Rather than simply selling products, they serve as conduits for the market itself.
The first wave of platform-based businesses took over entire sectors by serving customers faster and more effectively than ever before. And many incumbents have yet to catch up. In fact, a quick search of share prices shows the gap between platform-natives and traditional businesses is only growing.
Generative AI offers a chance to close the chasm. It evens the playing field, letting businesses do more with less on every front. But productivity gains are just the beginning. The true reward will come from business model innovation.
To win in the new market landscape, companies must become creators—not just consumers—of business and technology platforms.
While business platforms focus on facilitating interactions across an ecosystem, technology platforms provide the framework for developing and managing the applications businesses use. And, in the business models of the future, these platforms are inextricably linked. A generative AI platform, for instance, provides the technical support needed to operate a platform-based business.
This type of business model innovation is dependent on a modern IT architecture—and the principles of trustworthy AI. Both generative AI and business platforms demand access to vast stores of data that stretch beyond traditional borders. In the platform economy, open ecosystems are no longer optional.
This gives AI and data governance—traditionally an IT concern—firm footing in C-suite conversations. To gain a competitive edge, companies will need to cut through the red tape. At the same time, they must take a strategic approach to AI ethics, ensuring that platforms are transparent, trusted, and fair.
The IBM Institute for Business Value has identified three things every leader needs to know:
1. Generative AI offers a second bite at the platform apple.
2. Data dependency is both a challenge and an advantage.
3. AI and data governance are board-level concerns.
And three things every leader needs to do right now:
1. Jump at the opportunity for a disruptive do-over.
2. Outfit a generative AI data expedition.
3. Put governance at the heart of the generative AI lifecycle.
An updated version of this chapter with 2024 data is now available in the latest edition of the IBM Institute for Business Value's book, The CEO's Guide to Generative AI.
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Originally published 12 September 2023
1. Platforms
+ Generative AI
What you need to know
Generative AI offers a second bite at the platform business model apple
Platforms are at the core of the world’s most valuable companies. Their success has been hard to replicate, but generative AI makes a new wave of business model innovation possible. In fact, executives anticipate 57% greater returns on platform investments in 2023 relative to 2020. Even more telling, 94% of you said you planned to participate in platform business models in 2022, up from only 46% in 2018.
Successful platforms unite the right data, model architectures, governance, and computing infrastructure to enable reliable value creation across the ecosystem, with “anyone, anywhere.” However, Harvard Business Review found that only 17% of platforms have succeeded in the past 20 years.
Generative AI could be the missing ingredient. It instills platform superpowers by transforming functions across the organization. Executives say they expect generative AI to have the most impact in sales (57%), research and innovation (55%), product development (40%), and customer service (37%).
What you need to do
Jump at the opportunity for a disruptive do-over
Collect all the platform puzzle pieces you couldn’t gather the last time you thought about becoming a platform business.
- Act like a startup. Avoid incrementalism. Design a generative AI platform business that could be your biggest, highest-growth, most profitable business unit within three years.
- Design for outcomes, adapt to surprises. Structure the platform around real value-adds to platform participants at every touchpoint. Establish a mechanism to constantly assess and iterate the model based on increasing data volumes.
- Test before you invest. Test the new generative AI capabilities your platform depends on before pulling the trigger on major investments. Learn lessons from how your customer-facing AI initiatives are faring.
2. Data
+ Generative AI
What you need to know
Data dependency is both a challenge and an advantage
Data is the new oil—expansive, expensive, and difficult to extract. If it’s dirty, it can pollute an entire ecosystem. But when tapped responsibly, it could be a gold mine.
Generative AI has made data even more valuable, as companies race to tap its potential faster than the competition. And companies with high data wealth are gaining an edge. Enterprises that have large stores of high-quality data, monetize data effectively, and say their data is trusted by internal and external stakeholders realize almost double the ROI from their AI capabilities (9% versus 4.8% for all others).
But data wealth is often out of reach for companies that try to go it alone. In fact, 53% of CEOs say that a lack of proprietary data will be a barrier to successful generative AI initiatives. Platform business models can help companies overcome this hurdle by sourcing proprietary data from all ecosystem participants, as well as customers.
A generative AI platform can, in turn, fuel business model innovation by integrating cycles of data preparation, model training and tuning, and application development and deployment. This approach delivers flywheel effects: The more data on the platform, the more value to customers; the more customers, the more data, and the better the generative AI model can be trained. It also makes partnerships more valuable overall. Executives cite greater ecosystem collaboration as the #1 expected benefit of adopting generative AI for innovation.
However, the basic plumbing must be in place for value to flow across the ecosystem. Integrating data stores, digital products, and automated workflows will be essential as openness demands interoperability. The IBV 2023 Chief Data Officer Study shows that the highest-performing, most interoperable data operations employ silo-busting practices and technologies, such as employing hybrid cloud (78%), implementing process and task mining (70%), and using data fabric architecture (68%).
What you need to do
Outfit a generative AI data expedition
Find the data your platform needs in data lakes, data mines, data warehouses, content management systems, even laptop hard drives.
- Define requisite data sets. Work backward from the customer experience: What will a generative AI platform need to offer to attract customers and ecosystem participants? What data will those generative AI value propositions depend on?
- Explore all data sources. Ruthlessly mine the unstructured data that you need. Develop this data mining capability as a lever of competitive advantage that can differentiate your platform value proposition from the competition.
- Ask the ecosystem for help. Expand your data expedition to include your customers, potential ecosystem participants, and their customers. Amplify the platform’s network effects by tapping into their data streams.
1. Platforms
+ Generative AI
What you need to know
AI and data governance are board-level concerns
Can generative AI be trusted? This question is at the core of the debate around how and where companies should tap this powerful innovation. In today’s world, with massive amounts of data (think bytes on the order of 10 with 20 or so zeroes behind it) from multiple sources being used to train generative AI models, understanding data and its governance is more important than ever.
CEOs get that. Most say concerns about data lineage and provenance (61%) and data security (57%) will be a barrier to adopting generative AI. 45% of CEOs say data privacy is a barrier.
In this environment, AI and data governance isn’t just an IT issue—it’s a strategy for value creation. What a company can do with AI is defined, in large part, by how it selects, governs, analyzes, and applies data across the enterprise. And trust is built by communicating that process transparently.
Best-in-class companies, which see double the average AI ROI (13% versus 5.9%), assess infrastructure and processes to balance AI experimentation with industrial-strength scaling. Data teams review governance, management, ethics, literacy, and other frameworks needed for people to access, understand, and have faith in enterprise and ecosystem data.
Companies that elevate the AI and data governance conversation to the C-suite have the potential to overcome the obstacles hindering their platform ambitions—and earn the trust of employees, ecosystem partners, and customers.
What you need to do
Put governance at the heart of the generative AI lifecycle
Make governance a fixture on the executive leadership team’s agenda. Balance the power of generative AI with the guardrails required for trustworthy execution.
- Build a governance-savvy executive team. Educate your team and the board. Then make AI and data governance a recurring agenda item at board meetings, ensuring it gets the attention it warrants. Don’t just delegate and forget; active leadership is essential.
- Govern the whole system, not bits and pieces. Build governance into each stage of the AI lifecycle. Break the design and execution of AI and data governance out of organizational silos. You need an end-to-end system.
- Put someone in charge. Appoint and empower a senior executive to lead AI and data governance across the enterprise. Actively mitigate the risks of failure due to fragmented ownership and accountability.
The statistics informing the insights on this page are sourced from proprietary IBM Institute for Business Value data, as well as several external sources. IBM Institute for Business Value data includes a survey of 200 US-based CEOs’ perceptions of generative AI conducted in April–May 2023; a survey of 315 executives in the US, UK, Germany, Australia, and Singapore during May–June 2023 regarding the application of generative AI for open innovation; a 2021 survey of 2,895 global executives regarding business transformation; the 2023 Chief Data Officer Study; Generating ROI with AI; and COVID-19 and the future of business. External sources include S&P Global, Statista, and Harvard Business Review.
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An updated version of this chapter with 2024 data is now available in the latest edition of the IBM Institute for Business Value's book, The CEO's Guide to Generative AI.
Additional content
Download report translations
Originally published 12 September 2023
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