Achieving broader scale success with artificial intelligence (AI) requires active participation from the C-Suite, including the CEO. Success factors include knowing who should run AI in the C-Suite, bridging the data science divide among senior executives, and building a C-Suite for today’s data-driven world

We’ve unpacked a recent McKinsey & Company article, which examines why many companies struggle to scale AI across the company and offers tips on how to overcome them, including the role of the CEO. Part of the AI adoption problem lies in the fact that enterprise projects have typically been siloed and scattered across the company, resulting in inefficient deployments and inconsistent outcomes. However, as more AI projects have come to completion and companies start to learn by doing, the emergence of machine learning operations or MLOps is coming to the rescue. 

According to the article, “Embedding AI across an enterprise to tap its full business value requires shifting from bespoke builds to an industrialized AI factory.” MLOps can help by creating standard, company-wide methods and processes for deploying AI. As a result, companies achieve faster AI deployments with broader employee adoption.

So, how can the CEO help?

According to the McKinsey article, today’s CEOs need to take an active role in scaling AI across the company. But this needs to move beyond communications, shifting mindsets, and building data-driven employee cultures (which are still critical to success). To take adoption to the next level, today’s CEOs need to set a strategic vision for AI, including how to build, deploy, and manage AI applications across the company. Here’s our summary of some of the useful tips outlined in the article:

  1. Setting a clear aspiration for AI impact and productivity

According to the McKinsey article, “Implementing MLOps requires significant cultural shifts to loosen firmly rooted, siloed ways of working and focus teams on creating a factory-like environment around AI development and management.” CEOs have the power to break down organizational barriers and roadblocks. Being clear and vocal on AI company expectations and aspirations to all employees sets the right tone.

  1. Ensuring shared goals and accountability among business, AI, data, and IT teams

Sharing responsibilities and goals across teams is a simple yet often forgotten step in most AI projects. In short, AI goals should support company goals, and business leaders from all departments should be involved in setting them and being accountable for them. The CEO can help by driving better cross-company collaboration, including when investing in AI tooling, technology, and platforms. 

  1. Investing in upskilling existing AI talent and new roles

We’ve written about the importance of employee training and upskilling workers before. From evolving current data science roles to creating entirely new AI-oriented positions, the path to building company-wide AI skill sets and teams remains a work in progress. CEOs can help by putting the right operational practices, tools, and teams in place to promote AI for the long term.

To take a deeper look at how to scale AI across your business, check out the complete McKinsey article.