What Business Need To Do Right Now

What does it mean to be on the vanguard of enterprise applications when it comes to artificial intelligence?

That’s a big question, and it’s fairly broad. Every industry is incorporating AI into its landscape. Practically every business is considering how to add AI into its tool kit. And that means considering overall strategy.

So what should companies do?

Developing cutting-edge applications has to do with being confident in your forward motion as a company. It has to do with finding those opportunities that are the most powerful, not just settling for the first thing you think of, or casting around for some kind of random plug-in.

Increasingly, experts are suggesting that businesses should “solve their own biggest problem” with AI. That’s something I’m hearing again and again in the business community.

I came across this quote from Casse Kozyrkov, which was actually delivered to me by ChatGPT when I asked it to find a quote from an actual data scientist about business strategy:

“If your strategic plan is just to ‘do AI’ or ‘sprinkle some machine learning on it,’ you’re aiming for a solution in search of a problem. Instead, start by clarifying the problems you want to solve and how they’ll create value for your organization.”
— Cassie Kozyrkov, “What a CEO needs to know about machine learning,”

One thing that was interesting was the model’s displayed chain of thought of reasoning as it brought me this quote. It started with naming none other than Andrew Ng, who we actually had at our IIA event in Davos. However, it eventually chose Kozyrkov’s quote to highlight. Since we have unlimited space here on the Internet, I’m attaching the model’s chain of thought to the bottom of this piece, in case you’re interested.

Thoughts from an Interview

In a segment at IAA’s event at Davos, Karuana Gatimu of Microsoft interviewed Inma Martinez, who is doing some of this important work as a prominent AI advisor to businesses.

Martinez identified one main criterion for cutting-edge applications: programs that will work at scale.

“People want to optimize,” she said. “People want to create prediction, detection, whatever … you want to do. The little trick is you do it at scale – that is what really creates something very solid,(so) that you can build competitive advantage, that you can build progress, that you can really sleep at night, that that thing is (working well) in the background, and it really is okay.”

Gatimu agreed, noting that something like a proof of concept might be great, but ultimately lacks that scalability until it’s ready for prime time.

Talking about “AI anxiety,” Martinez suggested a people-first strategy, as she talked about getting lots of people involved, and encouraging buying in.

Then she talked about that basic idea that we started with – thinking of a problem that the business cannot solve, and applying AI to solve it.

“Think of a problem that currently, with your resources, you’re unable to solve,” she said, “and then ask yourself: which AI tools can truly help you deal with all these complexities and challenges, and then build what level of trust and efficiency you want to achieve?”

Building Out Advancements

Later in the interview, the two talked about AI in healthcare, and better patient care. They talked about agriculture and sustainability, and what’s happening in India, where farmers in the field are doing more with better data-driven strategy. And they talked about using Copilot for research.

“I think AI is demonstrating (its) power … to identify unknown unknowns,” Martinez said. “What (that) means is … you want to create for operational efficiency, but with AI, because it’s able to grab data that you didn’t even know was there, it just brings you perspectives and information that you never guessed.”

More on Business Strategy from ChatGPT

Inspired by the model’s delivery of quotes from human experts, I also went ahead and asked ChatGPT about five ways to apply corporate strategy with AI. I got these:

Data-driven strategy development

Predictive analytics and forecasting

Scenario planning and optimization

Personalization and targeted engagement

Continuous monitoring and real time adaptation

“By combining these approaches, businesses can use AI not just as a standalone tool, but as a strategic resource for competitive advantage—helping them move toward optimal decisions and long-term resilience,” the model concluded.

Which of these is most relevant to your business?

The first of these five uses speaks to that initial idea that you should take a big problem and solve it with AI.

So if you’re a CEO or C-level person and you’re reading this, hopefully you came away with some good insights on what you should actually do to drive AI strategy forward, not just at your business, but in the business world in general.

Addendum: ChatGPT CoT for AI Quote:

Reasoned for 48 seconds

Locating impactful insights

I’m gathering quotes from leading data scientists like Andrew Ng, DJ Patil, Hilary Mason, and Cassie Kozyrkov, focusing on their views on corporate strategy for AI, ensuring they are widely recognized and cited.

Identifying key figures

I’m noting Andrew Ng’s insights on AI transforming business and DJ Patil’s emphasis on data science for corporate strategy. Cassie Kozyrkov’s perspectives on AI projects are also considered.

Confirming quotes

I’m verifying Andrew Ng’s comment on integrating AI into corporate strategy. Also, considering DJ Patil’s relevance and Cassie Kozyrkov’s perspective on AI’s strategic role in business.

Gathering relevant insights

I’m gathering Andrew Ng’s insights to ensure their accuracy. Other sources like DJ Patil and Cassie Kozyrkov’s perspectives are also being considered for a comprehensive understanding of AI’s strategic importance.

Highlighting key actions

Executives are urged to identify AI’s business value, gather necessary resources and data, and ensure AI investments align with strategic goals to effectively solve specific business problems.

Highlighting Cassie Kozyrkov’s role

Cassie Kozyrkov’s Medium post on “What is a Chief Decision Scientist” underscores the crucial role of aligning data science tools with business strategy for effective decision-making.

Evaluating project alignment

It’s interesting to see that AI initiatives should align with the organization’s business goals and culture, ensuring strategic coherence and avoiding suboptimal results that fail to scale effectively.

Aligning AI initiatives

Ensuring AI aligns with business goals and culture is crucial for strategic coherence and scaling success, avoiding suboptimal outcomes.

Ensuring strategic approach

It’s critical to establish a clear AI strategy to identify opportunities and systematically build capabilities, ensuring alignment with top-level goals and fostering business value.

Establishing AI strategy

Corporate leaders should outline how AI aligns with their business goals and systematically build the necessary capabilities to achieve this vision, ensuring strategic execution and alignment.

Below is a widely cited perspective from Cassie Kozyrkov, Chief Decision Scientist at Google, highlighting the importance of aligning AI initiatives with business goals (rather than doing AI for AI’s sake):

“If your strategic plan is just to ‘do AI’ or ‘sprinkle some machine learning on it,’ you’re aiming for a solution in search of a problem. Instead, start by clarifying the problems you want to solve and how they’ll create value for your organization.”
— Cassie Kozyrkov, “What a CEO needs to know about machine learning,” Medium

Kozyrkov is a recognized leader in data science and decision intelligence. Her quote speaks directly to the notion that corporate AI strategy shouldn’t be about adopting “AI” in the abstract, but about focusing on specific, high-impact business objectives—and ensuring data and AI capabilities are developed with clear strategic intent.