For most organizations, the trouble isn’t usually how to get started with AI, but knowing where. Not only can machine learning and other AI techniques be applied across virtually every single industry, business function, and workflow, it is subject to immense hype, over-inflation, and promise. From chatbots to facial recognition, from automated reports to predictive maintenance, from procurement management to simulations for decision-making, AI’s applications are vast. But while identifying use cases may intrigue, the starting point for any sustainable strategy is never the technology. The starting point begins with identifying the problems and objectives and setting goals.
In corporate environments, objectives tend to start with high-level mission statements and parse into specific departmental, functional, product, team, and individual objectives in support. How can the organization improve its product experience, its employee retention, lead generation, its partner channels, or accelerate R&D? In this way, AI becomes an extension, not a bolt-on to existing digital transformation efforts.
AI transformation is an extension of digital transformation
This distinction is an important one, as companies risk following the siren hype of “building an AI strategy” rather than evaluating how existing programs could function better and letting problems guide solutions. While, obviously, there would be no AI without data, many companies struggle to understand the differences and similarities.
To date, digital transformation has been marked by digitization of information—a sort of “phase zero” for bringing an organization’s programs and processes online. Born of the age of social media, and (often) driven by marketing, digital transformation has evolved from external (customer-facing) programs to internal and cross-functional. AI, on the other hand, is driven entirely by data. It is born of analytics, increasingly a discipline within every department. Digital transformation has brought about a proliferation of points systems; AI transformation benefits from systems integration. In both cases, people and culture aren’t just crucial for adoption, they are essential for activating insights and customer success in the digital age. At the end of the day, thinking strategically about AI becomes synonymous with thinking strategically about data.
Thinking strategically about data means shifting focus from big data to good data
Digital transformation efforts have, to date, emphasized analytics and celebrated the value of big data– even though big data does not equate to the ability to activate that data. But as more companies work towards machine learning, which is more accurately understood as machine inferencing, the emphasis inevitably shifts from quantity to quality. From more precise personalization, to security and privacy protections (never mind compliance), to all manner of innovations in the name of human health, big data isn’t enough; data integrity is the real key to AI transformation.