As a CEO, scaling AI in your organization can seem like a daunting task. However, with the right approach, it can be as intuitive as any tech-native venture. Let's delve into the role of a CEO in this transformative journey.
Scaling AI
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.
The CEO’s Role
CEOs play a critical role in three key areas: setting aspirations, facilitating shared goals and accountability, and investing in talent.
- Building an MLOps capability will materially shift how data scientists, engineers, and technologists work as they move from bespoke builds to a more industrialized production approach.
Ensuring Shared Goals and Joint Accountability
Shared goals and joint accountability among business, AI, data, and IT teams
- The degree to which goals are shared across the teams should be a litmus test for impact
- Business leaders should be able to articulate what value they expect from AI and how it will come to fruition
- Collaboration around strategic technology investments
- Comprehensive MLOps practices ensure a road map to reduce both complexity and technical debt when integrating new technologies
Investing in upskilling existing AI talent and new roles
While organizations realize the value of AI, many fail to scale up because they lack the right operational practices, tools, and teams.
- MLOps can help companies incorporate these tools with proven software-engineering practices to accelerate the development of reliable AI systems.
Reducing risk to ensure regulatory compliance and trust at scale
Despite substantial investments in governance, many organizations still lack visibility into the risks their AI models pose and what, if any, steps have been taken to mitigate them
- While a robust risk-management program driven by legal, risk, and AI professionals must underlie any company’s AI program, many of the measures for managing these risks rely on the practices used by AI teams
- MLOps bakes comprehensive risk-mitigation measures into the AI application life cycle by reducing manual errors through automated and continuous testing
The bar for AI keeps rising
In the early days of AI, the business benefits of the technology were not apparent.
- Without a focus on achieving AI at scale, the data scientists created “shadow” IT environments on their laptops, using their preferred tools to fashion custom models from scratch and preparing data differently for each model
- Today, market forces and consumer demands leave no room for inefficiencies
- A rapidly expanding stack of technologies and services has enabled teams to move from a manual and development-focused approach to one that’s more automated, modular, and fit to address the entire AI life cycle
Better talent retention and acquisition for implementing AI at scale
MLOps can serve as part of the proposition to attract and retain critical talent
- Without a robust MLOps practice, top tech talent will quickly become frustrated by working on transactional tasks and not seeing their work have a tangible business impact
Setting a clear aspiration for impact and productivity
CEOs should be clear that AI systems operate at the level of other business-critical systems that must run 24/7
- Among the key performance metrics CEOs can champion are the percentage of models built that are deployed and delivering value, with an expectation of 90 percent of models in production having real business impact
- The total impact and ROI from AI as a measurement of true scalability
- Near-real-time identification of model degradation and risks, including shifts in underlying data
Increasing productivity and speed to embed AI organization-wide
Companies applying MLOps can go from idea to a live solution in just two to 12 weeks without increasing head count or technical debt, reducing time to value and freeing teams to scale AI faster
- This requires streamlining and automating processes, as well as building reusable assets and components, managed closely for quality and risk, so that engineers spend more time putting components together instead of building everything from scratch
Enhancing reliability to ensure 24/7 operation of AI solutions
Companies using comprehensive MLOps practices shelve 30 percent fewer models and increase the value they realize from their AI work by as much as 60 percent
- Integrate continuous monitoring and efficacy testing of models into their workflow
- Make sure monitoring team is independent from the teams that build the models to ensure independent validation of results
