Generative AI has democratized AI – what does this mean for COEs?

Commissioned: To centralize or decentralize? That was once the salient question for many enterprises formulating a strategy for deploying artificial intelligence. Whether it was nobler to support a singular AI department or suffer the slings and arrows that accompany distributed AI projects.

Tectonic shifts in technology can render such debates moot. Is that happening now, as generative AI catalyzes creativity in businesses, enabling employees to create texts, images and even software code on the fly?

Some of these experiments are useful; others not so much. There will be more failures along the path to innovation, which is littered with the bones of fallen tech projects.

What is crystal clear: Generative AI has democratized AI consumption for enterprises in ways that previous AI applications could not. The genie, in its many forms and functions, has shot out of the bottle.

The way it was

To streamline and curate our AI competency or allow projects to roam unchecked and hope for the best? It’s a fair question, with mixed approaches.

Over the years, some organizations consolidated AI capabilities in one department, often established as an AI center of excellence (COE). The COE was often composed of database engineers, data scientists and other specialists trained in querying machine learning (ML) models.

The inverse of COEs was highly decentralized. In classic, do-it-yourself fashion, business leaders experimented with some tools in the market on AI projects that might eventually foster innovation. Naturally, these projects tended to be more rudimentary than those created by COE members.

Both approaches had their pros and cons.

Centralizing AI functions afforded organizations the ability to dictate strategy and policy and control costs, thereby reducing risks. But COEs’ dedication to rigorous processes had its drawbacks. Typically, the COE received specifications and built a deliverable over several months. Over a long enough timeline, the goal posts moved. As data grew stale, the output rarely resembled the desired outcome.

Conversely, distributed AI functions granted business experts the freedom to quickly experiment and explore so that data remains fresh and current. Projects may have lead to some insights that were harder to cultivate in an AI COE, which lacked the domain expertise of a business line.

However, ad-hoc efforts often resulted in projects with no demonstrable ROI for the business. And lacking the kind of guardrails present in a COE, these efforts were often risky to the business.

How organizations approached AI varied from business to business, based on leaderships’ philosophies and appetite for risk, which are informed by internal capabilities and competencies.

Generative AI changed the paradigm

The arrival of generative AI clarifies the question of whether to centralize or distribute AI functions.

Today, average Joes and Janes interface directly with AI technologies using natural human language rather than special tools that query AI models.

Knowledge workers create cogent texts using Google Bard and ChatGPT. Graphic designers craft new image content with DALL·E and Midjourney. Software developers write basic apps with Copilot or Codeium.

Increasingly, employees layer these capabilities, creating mashups of text, graphic and code creation technologies to generate marketing content, analytics reports, or other dashboards – without the help of data experts who might spend months putting something more sophisticated together.

To be clear, generative AI cannot replace the expertise offered by AI COE specialists. It can’t teach somebody the intricacies of TensorFlow, the magic of Kafka, or other sophisticated tools used to query AI and ML models – yet.

Generative AI has democratized content creation as much as smartphones have facilitated access to information to anyone on the go – anywhere in the world.

Thinking through the implications

IT departments often hold the keys to many technologies, but generative AI is a different animal, requiring IT leaders to consider the impact of its use within the department and across the broader business.

As with technologies that are new to your business, you’ll huddle with C-suite peers on rules and guardrails to make sure the business and its employees are covered from a compliance, risk and security standpoint. And you’ll guard against potential lawsuits alleging content created by generative AI tools infringes on intellectual property rights and protections.

Yet this may be easier said than done for many organizations.

Fewer than half of U.S. executives surveyed by KPMG said they have the right technology, talent and governance to implement generative AI. Moreover, executives plan to spend the next 6 to 12 months increasing their understanding of how generative AI works and investing in tools. This is critical for the C-suite and board of directors, according to Atif Zaim, National Managing Principal, Advisory, KPMG.

“They have a responsibility to understand how generative AI and other emerging technologies will change their business and their workforce and to ensure they have sustainable and responsible innovation strategies that will provide a competitive advantage and maintain trust in their organization,”

To be sure, the democratization of generative AI means your rivals have ready access to these tools, too. Take care not to lose the name of action.

How will your organization use these emerging technologies to future proof your business and gain competitive advantages?

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