The unfolding of A.I. into a larger market- What organizations must do
Scaling AI like a tech native
Embedding AI across an enterprise to tap its full business value requires shifting from bespoke builds to an industrialized AI factory.
What if a company built each component of its product from scratch with every order, without any standardized or consistent parts, processes, and quality-assurance protocols? Chances are that any CEO would view such an approach as a major red flag preventing economies of scale and introducing unacceptable levels of risk—and would seek to address it immediately.
Yet every day this is how many organizations approach the development and management of artificial intelligence (AI) and analytics in general, putting themselves at a tremendous competitive disadvantage. Significant risk and inefficiencies are introduced as teams scattered across an enterprise regularly start efforts from the ground up, working manually without enterprise mechanisms for effectively and consistently deploying and monitoring the performance of live AI models.
Ultimately, for AI to make a sizable contribution to a company’s bottom line, organizations must scale the technology across the organization, infusing it in core business processes, workflows, and customer journeys to optimize decision making and operations daily. Achieving such scale requires a highly efficient AI production line, where every AI team quickly churns out dozens of race-ready, risk-compliant, reliable models.
CEOs often recognize their role in providing strategic pushes around the cultural changes, mindset shifts, and domain-based approach necessary to scale AI, but we find that few recognize their role in setting a strategic vision for the organization to build, deploy, and manage AI applications with such speed and efficiency. The first step toward taking this active role is understanding the value at stake and what’s possible with the right technologies and practices. The highly bespoke and risk-laden approach to AI applications that is common today is partly a function of decade-old data science practices, necessary in a time when there were few (if any) readily available AI platforms, automated tools, or building blocks that could be assembled to create models and analytics applications and no easy way for practitioners to share work. In recent years, massive improvements in AI tooling and technologies have dramatically transformed AI workflows, expediting the AI application life cycle and enabling consistent and reliable scaling of AI across business domains. A best-in-class framework for ways of working, often called MLOps (short for “machine learning operations”), now can enable organizations to take advantage of these advances and create a standard, company-wide AI “factory” capable of achieving scale.
