After decades of false starts and unfulfilled expectations, artificial intelligence (AI) has now gone mainstream. Companies are successfully applying AI to a wide variety of current and novel processes, products, and services, and this success comes none too soon; AI is essential for business to address the ballooning complexity brought on by digitization and big data.
Visionary executives have started to imagine the potential for implementing AI throughout their companies. For example, BCG estimates that AI could help the top ten banks generate an additional $150 billion to $220 billion in annual operating earnings.
As pioneers across industries strive to reap these rewards by scaling up AI, however, they are stumbling against what we call the “AI paradox”: it is deceptively easy to launch AI pilots and achieve powerful results. But it is fiendishly hard to move toward “AI@scale.” All sorts of problems arise, threatening to undercut the AI revolution at its inception.
The paradox is easy to explain but hard to resolve. AI forces business executives to deal simultaneously with technology infrastructure as well as more traditional business issues. The core challenge is a tightening Gordian knot. Typical IT systems consist of data input, a tool, and data output. These systems are relatively easy to modularize, encapsulate, and scale. But AI systems are not so simple. AI algorithms learn by ingesting data — the training data is an integral part of the AI tool and the overall system. This entanglement is manageable during pilots and isolated uses but becomes exponentially more difficult to address as AI systems interact and build upon one another.
What sounds like a mere technical issue has multifaceted implications and challenges for companies. For example, vendor management becomes both more strategic and more complex. For the foreseeable future, vendors will play a large role in deploying AI because they have hired so much AI talent and have even promised to help with entanglement. Companies need to prepare for working with AI vendors in ways that do not put their data at risk or create long-term dependencies but instead strengthen competitive advantage.
People challenges also loom large for AI@scale. On one hand, companies confront a scarcity of AI data scientists and systems engineers; on the other hand, current employees are concerned about interacting with machines and even about losing their jobs.
Also, the organizational demands of AI require a delicate three-dimensional balancing act. Data governance, core AI expertise, and system management should be centralized. The development of use cases, learning, and training should occur in business units or functions and be managed by agile, cross-functional teams. Finally, AI action remains decentralized — in the marketplace, on the shop floor, or in the field.
Below, we provide a roadmap to systematically resolve the AI paradox. The AI@scale program is a full transformation to a new operating model.