The Essence of an AI Company: More Than Just AI

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AI companies more than just AI

Imagine you’re given a Rubik’s Cube, a symbol of complexity and ingenuity, but without any prior knowledge of how it works or how to solve it. You could twist and turn it randomly, hoping for the best. Maybe, through pure chance, you stumble upon the solution. But does this make you a master of the Rubik’s Cube? Unlikely.

Similarly, merely owning an Artificial Intelligence (AI) model doesn't define you as an AI company. "What truly constitutes an AI company?" I found myself pondering this question again, following an article I authored last year about adopting a Copernican mindset in implementing AI (link).

In that piece, I argued that a successful AI transformation transcends technology. It's about a radical shift in mindset, culture, and strategy. The 'what' and 'how' of AI only gain meaning when tied to a substantial 'why'.

 
A prominent issue in AI transformations across many organizations is not a scarcity of ideas, but rather a stark absence of behavioural change. Traditional thinking and outdated infrastructure often act as heavy anchors, pulling down potentially buoyant AI initiatives.

 

Sure, you could assemble a group of data scientists and engineers and have them churn out machine learning algorithms in the corporate basement. But does this transform you into an AI company? Not quite. The defining factor of a remarkable AI company is its proficiency in the tasks AI facilitates extraordinarily well.

 
What are the common traits of AI companies? Let’s review a few.

 

Mastering the Art of Data Collection

 

AI companies excel in strategic data acquisition, drawing from both internal and external sources. This is why many major consumer tech companies maintain products that don't monetize directly but serve as data acquisition tools to be monetized elsewhere. Some of the best AI companies launch products knowing they won't generate direct revenue, but rather serve as rich data mines.

 

Hierarchical Decentralization - Let the Experts Take the Wheel

 
A striking characteristic of AI companies is their inclination to delegate decision-making authority from the top levels of the C-suites to data scientists, engineers, and other specialized roles.
This approach diverges from traditional non-AI companies where key decisions are monopolized by the C-suites, with the rest simply executing these decisions. 

The hierarchical decision-making model is outdated in the AI era, as those working directly with technology have the technical knowledge and understanding to make the most effective decisions. As Steve Jobs so eloquently put it, “It doesn’t make sense to hire smart people to tell them what to do, we hire them so they can tell us what to do."

 

Fostering Unified Data Systems: The Lifeblood of AI

 
AI companies thrive on unified data systems. An ecosystem of fifty different databases managed by twenty different managers creates chaos that no data team can untangle. Thus, many great AI companies preemptively invest in consolidating their data into unified data lakes. This increases the chances for their teams to connect the dots and allows data scientists and analysts to focus on analyzing data rather than verifying it.

 

Embracing Automation

 
AI companies are adept at identifying automation opportunities. Many tasks within companies are monotonous and don't contribute significant value. AI companies excel in identifying such tasks and deploying machine learning algorithms and ETL mapping to automate these processes, thereby freeing up valuable human time for tasks that genuinely add value to the business.

 

Emergence of AI-Related Roles


AI companies are known for cultivating new roles, including AI heads and managers, data scientists, and machine learning engineers. They also place a strong emphasis on AI ethics. These companies are innovative in their approach to task distribution among team members, ensuring that each role contributes effectively to the overall AI strategy.

AI Documentation: The Roadmap to Accountability

Should you ever find yourself questioning the worth of your data team's efforts spent on documentation, it's certainly time to reconsider. The ability to understand what to document and how to do it effectively is fast becoming a crucial skill for AI teams. As the storm of Generative AI sweeps across the AI field, the need for regulation in the industry has never been more apparent.


Traditional software documentation, while useful, doesn't quite cut it when it comes to providing the necessary evidence for auditors. And while we're still waiting for AI-specific guidelines to emerge, the recent movement by the European Union underscores the importance of documenting AI systems. It's about more than just ticking boxes - it's about fostering transparency, promoting accountability, and enhancing our understanding of these complex systems.


 

Conclusion

 

The transformation to an AI company is an ongoing journey, not a destination. It's a paradigm shift that continually evolves the organization’s mindset.

 
Rushing to ride the wave of generative AI and showcasing tools like ChatGPT doesn't necessarily transform your company into an AI entity. Becoming a truly AI-driven company requires a deeper change that permeates the entire organizational structure.

 

Unfortunately, some C-suite executives have unrealistic expectations of AI, often pressuring data teams to deliver results that go beyond what today’s technology can do—or what the company’s resources can support.

 
The tendency of the media and academic literature to only highlight AI successes, without mentioning failures, often fosters a misguided belief that AI can do everything. The reality is, it can't. To learn more about the challenges AI hasn't overcome, check out this post: link.

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