Don’t Fall Behind: 5 Key Things You Need to Understand About Business and AI

October, 18 2018

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Written By
Alyssa Verzino

bk blog image We interviewed Brendan Kohler on AI at Work, Co-founder and CTO at Sentenai and Co-founder at Hyperplane Venture Capital, an AI-focused venture capital fund. He had some great advice for business leaders on the laws of business and AI, here’s your cheat sheet.

1. Hiring in AI: Deploy Multiple Smaller Teams Within Different Teams

Data science teams are not built the same way that teams of software engineers are. Deploy multiple smaller teams within different parts of your organization instead of having a core data science or AI team.

A data science team is generally full of people who have deep expertise in certain kinds of statistics, and certain kinds of programming, that don't apply across other parts of an organization. We see it as being best for an organization to roll up those teams within the business units they serve. Instead of having a core IT-centric data science or AI team, we see most successful companies deploy multiple smaller teams within different parts of their organizations. Or, if they need to centralize the full team to make use of data across an organization, they'll form a data science team under a digital team, and they will recruit people across the spectrum, from data engineering to visualization experts to experts in statistics and machine learning.”

2. Investing in AI Companies: Understand the Complexities of the Business That You Want to Help

It is important to understand the complexities of the business that you want to help. The most effective solution is for smaller companies to engage with larger companies that are willing to invest the time and resources by bringing in and learning from smaller AI-focused companies.

“I think the primary mistake I've seen smaller companies I've advised and invested in make is to assume that they understand the complexities of the business they're trying to help. In our world, AI is this amazing malleable tool that we can use to solve many problems. It is important to recognize what problems are actually solvable, and what problems boil down to issues of company structure, or processes, or any other variety of potential blockers that exist within a large company that's actively trying to execute on a business model. The business owners that engage these smaller companies really need to do a lot to bring along the smaller technology company into the organization and understand how they can help. If they don't do that, these technology-focused companies generally don't have a chance. What we see as being most effective is for smaller companies to engage with companies that are willing to invest the time and resources, learning and bringing these smaller AI-focused companies into the fold.” 

3. Early Adoption of AI: Evaluate Your Options

Larger companies should evaluate all of their options when they are at an inflection point and use it as an opportunity to implement new technology.

“There are certain inflection points within an organization that's focused on executing as well as possible, where they have opportunities to adopt a different process, a different model, and different technologies. Outside of those areas, where that inflection point happens. It's very difficult for them to be able to get approval up and down an organization. Those inflection points happen every few years, every time different parts of a business stop working and corrective action has to be taken or every time IT technologies start failing to support the execution of a business as it grows. We see that a lot, especially on the software side. I think, it's important that companies at those inflection points evaluate all of their options, because they really only get those chances every few years within an organization, especially if they're a large organization. They just can't afford to move as fast as a startup or a small business can.”

4. Sorting Through “Hype”: Use Traditional Frameworks

Business leaders should make evaluations using traditional frameworks, even though these are new technologies.

“Even from an investor lens, it can often be difficult to separate companies that truly have the talent and the ability to execute on AI strategies, and those that are marketing themselves as AI because they think of it as a good way to get investment dollars. I think that also applies to selling into businesses and dealing with board objectives and adoption of technology within an organization. What I would say is that the laws of business are not suspended for blockchain, AI, and machine learning. What a business actually has to do is evaluate the offerings of the companies based on business outcomes, cost, and the time-to-value. If an organization sticks to those metrics, then it doesn't matter which of the technologies are hype or not hype. They can evaluate these businesses through traditional frameworks.”

5. Learn About New Technologies: It's Worth the Time 

The value is worth the time it takes to get up to speed and educated.

“From my perspective, it's really something that executives at large organizations should spend more time doing deep dives on. If they can yield 10% more efficiency or reduce risk in a large organization by 10% or 20% on critical business processes, that merits adoption of these technologies. You can't be afraid of it being too academic or too complex to dive in and get an understanding of how it can help.”

You can read the full transcription here to hear more from Brendan Kohler on the Laws of Business and AI.

Subscribe to Talla’s AI at Work podcast on iTunes or Google Play and share with your network.

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