Scaling AI like a tech native: The CEO’s role - Deepstash

Embedding AI across an enterprise to tap its full business value requires shifting from bespoke builds to an industrialized AI factory. 

For AI to make a sizable contribution to a company’s b...

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Scaling AI like a tech native: The CEO’s role

mckinsey.com

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As AI has matured, so too have roles, processes, and technologies designed to drive its success at scale. Specialized roles such as data engineer and machine learning engineer have emerged to offer...

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A best-in-class framework for ways of working, often called MLOps (machine learning operations), now can enable organizations to take advantage of these advances and create a standard, com...

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Moving AI solutions from idea to implementation takes nine months to more than a year, making it difficult to keep up with changing market dynamics. Even after years of investment, leaders often te...

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Achieving productivity and speed requires streamlining and automating processes, as well as building reusable assets and components, managed closely for quality and risk, so that engineers spend mo...

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Another critical element for speed and productivity improvements is developing modular components, such as data pipelines and generic models that are easily customizable for use across different AI...

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Organizations often invest significant time and money in developing AI solutions only to find that the business stops using nearly 80 percent of them because they no longer provide value—and no one...

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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 b...

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The availability of technical talent is one of the biggest bottlenecks for scaling AI and analytics in general. When deployed well, MLOps can serve as part of the proposition to attract and retain ...

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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. ...

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As in any technology transformation, CEOs can break down organizational barriers by vocalizing company values and their expectations that teams will rapidly develop, deliver, and maintain systems t...

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Among the key performance metrics CEOs can champion are the following:

  • the percentage of models built that are deployed and delivering value, with an expectation of 90 percent of models ...

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One of the fundamental litmus tests for impact is the degree to which goals are shared across business leaders and the respective AI, data, and IT teams. Ideally, the majority of goals for AI and d...

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Newer roles needed on AI teams have emerged, like that of the machine learning engineer who is skilled in turning AI models into enterprise-grade production systems that run reliably. To build out ...

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