Learning from gamechangers in the AI era

Ardent followers of the game of cricket would be familiar with Kerry Packer, Stuart Robertson, and Shaji Ul Mulk.? Kerry Packer, the Australian media magnate was responsible for the explosion of the one-day game (ODIs).? T20 cricket was the brainchild of Stuart Robertson of the England and Wales Cricket Board (ECB).? Shaji Ul Mulk founded the Ten10 league.? It is not that ODI, Twenty20, or Ten10 became so successful so quickly, but that nobody thought of it sooner.? The rest is history.? People like Packer, Robertson and Mulk are gamechangers.? They had a sharp nose for business, changed the rules of the game and the roles in the game.?

In the realm of technology, ChatGPT’s arrival was one such gamechanger moment that showed how machines can “learn” the complexities of human language and interaction.? While people had been experimenting with generative AI for years, suddenly Sam Altman and his OpenAI team brought it at everyone’s front door.? The floodgates were open.

It is often heard in organizations that data is extremely important to the business. However, beyond that, they do not know what to do next with data.? The status quo in most organizations is a flurry of data activity but no coherent vision of what it means to manage data.? Ditto with managing AI and Gen AI in organizations.

"The only thing we learn from history is that we learn nothing from history." - Georg Hegel

In the June 2022 Harvard Business Review article titled “Build Better Management Systems to Put Your Data to Work”, Thomas C. Redman states that “If you look under the hood of most company’s data strategy — if they have one, that is — you’ll find that most data is woefully unmanaged. It is unclear how data is meant to be used and who is responsible for it.”

While no organization sets out to inflict on themselves such problems, a lack of attention towards data management will undoubtedly result in their manifestation.

In many ways data and business strategy have not been aligned in most organizations, nor is it a priority for top management, and governance efforts have remained old fashioned, and has been inconsistent and incomplete. The emergence of Gen AI has magnified these issues, and raised new issues of security, trust, quality, and ethics.

While Gen AI offers game-changing opportunities, it is important to identify the risks and to proceed with caution, as AI linked models expose organizations to new types of vulnerabilities. ?Issues with AI and Gen AI security (e.g. prompt injection, data leaks), confidentiality, data privacy, governance, and ethics will pose a threat to organizations if not managed responsibly and effectively.

Studies have revealed that the presence of a dedicated executive-level data role in an organization is correlated with stronger performance than that achieved by organizations without such a role.? However, merely having titles like CDAO, CDAIO, CAIO etc. with no clarity of responsibilities and accountability often means such roles are setup for failure.? Businesses must take a comprehensive and hard look at the organizational design surrounding their data and AI aspirations and initiatives.

Thomas Redman summarizes in his above cited HBR article, “Businesses must craft better systems and approaches to working with data, and that starts with clarifying management responsibilities for everyone who touches data in any way, across the entire company. They can start with five steps: 1) get everyone involved, 2) build infrastructure that can overcome the limitations created by silos, 3) stop asking IT to manage data, 4) charge data teams with coaching and coordinating, and 5) get senior leadership off the sidelines.”

Organizations that have a clear vision for how they will deliver business value from their data and AI investments will be the ones that are very likely to succeed and sustain.? Investments are required in those capabilities that are essential and are needed to grow and compete. Data, analytics, and AI leadership requires time, attention, clear and effective communication, and the ability to articulate the need, establish realistic expectations, and elicit buy-in.? Business leaders must champion and advocate for investments in data, analytics, and AI.