Scottie Bryan Discusses Artificial Intelligence and the Intelligent Executive

Scroll through any news feed and the headlines about Artificial Intelligence, or AI, abound. While Elon Musk predicts it could be our downfall and technophiles predict a brave new world, the reality is that most companies will struggle to strategically deploy Artificial Intelligence. And while an off the shelf ChatBot for your company’s website might be fun to talk at the quarterly townhall, moving revenue or expenses in a meaningful way will be out of reach for most companies for years.

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Artificial Intelligence Isn’t the Latest Fad

Over the past decade, we’ve seen plenty of technology trends come and go. Blockchain, digital currency, remote work, cloud computing were all going to revolutionize whatever topic a pundit felt needed to be revolutionized. And while all of these have real world uses and implications, their scope and impact has limits.

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Artificial intelligence is different because it is only limited by the creative power of the team wielding it and the availability/quality of the underlying data. It has near limitless applications for teams that are disciplined, strategic, and can collaborate between the business and the technologists.

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Ignore the Bandwagon

The chart below shows that during analyst calls for S&P 500 companies, AI was mentioned over one-third more often in Q2 2023 than in the prior quarter.

But talk is cheap. Companies that are committed to leveraging technologies like Artificial Intelligence don’t just talk. They execute. In The Economist’s early-adopters index, they found that those companies leading in Artificial Intelligence share several commonalities. First, they are actively hiring team members that have experience with Artificial Intelligence. According to ProjectLeads, a research firm, job openings that mention AI have increased to 5.3% vs. the three-year average of 2.5% with some industries like retail growing even faster. Other early indicators for companies serious about deploying AI in meaningful ways include filing for patents on Artificial Intelligence products as well as venture capital startups. Both examples imply that companies see a monetary value on their intellectual capital as it relates to AI.

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What To Do with Artificial Intelligence

For the executive looking to implement a program that seeks to strategically capture the value of Artificial Intelligence, there are several key factors that need to be understood and addressed:

1. Is your data ready? If your data is locked away in silos, your team is going to struggle. When the accounting data sits in the accounting system and the operations data sits with operations and the two only meet in spreadsheets, you aren't ready. You might get a vendor that can provide limited (and possibly very useful) one-off use cases but being able to leverage data across departments is a prerequisite for a sound strategy using Artificial Intelligence. You might see a tactical win, but one battle doesn't win the war.
2. Have a solid foundation. If your organization doesn’t have the basic data available for mid-level managers to easily pull information, you need to learn to crawl and walk before you can run. Going back to Business 101, if your managers can’t run basic managerial accounting type reports, your foundation probably isn’t ready for the advanced work that Artificial Intelligence requires. A centralized location for you data to live and a method to get new data into that location is a prerequisite for a data strategy of any magnitude.
3. Check your technical debt. As a cost center that rarely generates income, most IT departments lean on staffing. This means that every IT project, every new software version, and every new initiative creates additional layers of technical debt because the IT department usually gives just enough time to get the latest project operational before moving on. They don’t have the luxury of cleaning up. Making sure that your team has a handle on their technical debt and has time to build a strategy around data. This will help minimize the cost of data projects and will ensure that your IT team can remain lean and responsive to the business.
4. Build a strategy and stay the course. ChatGPT’s first iteration started in 2018. Think about what that means. It took five years and several large-scale iterations and millions of dollars to create ChatGPT. And ChatGPT had one major advantage that your company doesn’t: Laser Focus. The ChatGPT team wasn’t saddled with keeping the lights on for other data projects and initiatives or dealing with legacy challenges like your IT staff is doing. Build a strategy and give it time.
5. Push your team to deliver value tomorrow. For many companies, a digital program that is impactful enough to drive tangible value into the Income Statement and Balance Sheet can be overwhelming. Its easy for teams to get bogged down in a years-long program to master or govern or improve data. Don’t do that. After you have developed your strategy and created processes to support your strategy, pick a winning project, and deliver.
6. Leverage experts. There are a lot of great data consulting companies out there that can help your team generate value quickly. The challenge is to find the right company. Beware of teams that try to sell you the full package. If someone is telling you that they can start a data science program while getting your basic data transformed while eliminating your technical debt, run away.

Instead, look for teams that can come in and provide technical and change management assistance to help engage the units across your organization. Look for teams that aren’t promising a product so much as sustainable processes that are clear and easy for the organization to understand and use. Lastly, be wary of outside consultants that pin their success on a specific tool. If you team doesn’t have the underlying processes in place to handle data, a tool won’t solve your problems.

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Feel free to reach out to Novus for questions. We are here to help!