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CEO to CEO: An AI Q&A with Bob Higgins (Part 1 of 2) 

In a recent conversation with Gary McClure, a senior consultant at Thrivence, we explored how leaders can effectively introduce AI into their firms. The discussion led to an insightful article outlining several recommendations, which you can read here. This inspired me to reflect on the frequent questions I receive from leaders about AI. This two-part article compiles those questions along with my responses, aiming to provide valuable insights. — Bob


Question: Leaders often want to study other leaders. It’s an effective strategy to grow from the experiences of others. One of the most frequent questions I get asked is “What AI challenges have other CEOs and principals dealt with? What can I learn from them?” 

Answer: Several months ago there was a symposium in Nashville that convened about 150 leaders of engineering and related firms. That very question was posed electronically to the group. Here’s what that group collectively revealed were their hurdles to implementing AI: 

1. Understanding AI’s capabilities and limitations. They want at least a base understanding if they are going to be the organization’s AI champion. 

My recommendation: make it a priority to invest in learning the basics of AI yourself. Become fluent in it so you can have meaningful dialogue with your team and be able to set a2 thoughtful direction. 

2. Education and training. They recognize a huge need for equipping and training their leadership team and staff. They will be the other vocal AI advocates the workforce will look to. 

My recommendation: similar to educating yourself, encourage that same dedication to learning AI basics across the team. Even with non-technical functions such as HR and Marketing, all areas will be impacted by AI to some degree.   

3. Trust and acceptance issues. Trust in AI’s capabilities and somewhat spotty outputs, as well as overcoming internal skepticism and resistance, have been bigger barriers than many expected. 

My recommendation: leaders need to emphasize employee adoption from the start. This is crucial for widespread and quick acceptance. And there are lots of ways to do that: from engaging them in selection of use cases and providing fun AI training…to modeling AI use for them and ensuring quick AI wins to prove its value. 

4. Integration and implementation challenges. Integrating AI into their existing systems and processes, adapting it to the firm’s operational needs, and aligning it with their long-term strategic goals. 

My recommendation: leaders often skip over the fundamental step of assessing AI readiness…what is their current state of data, technology, governance, strategy, and other important factors? This one step could uncover most of the implementation issues that arise later. 

Question: Another need simply is where to start. They ask “What parts of our firm make the most sense in which to start AI integration?”. 

Answer: I almost always advise starting now (if they haven’t already) and starting small. Regarding where, I believe you can be functionally agnostic, meaning you don’t have to start in IT or Service Delivery, for example. Rather, most firms are looking for characteristics such as: 

  • Highly repetitive tasks and processing routine transactions. AI can execute those consistently and tirelessly. 
  • Tasks involving large amounts of data. AI can quickly process, analyze, and find patterns in massive amounts of data…much better than we can.  
  • Environments that have high structure and rules. They have patterns, objectivity, and logic. For example, scheduling and process optimization. 
  • Personalization is huge. AI is great for analyzing user data and preferences to provide individualized recommendations. 
  • Of course, needs for complex calculations and simulations. As you know, AI is wonderful for dynamic modeling, forecasting, and scientific simulations. 

Question: In our last article we talked about the leader’s first focus should be on their employees, not necessarily the technology. A related question I often hear is “How do we gain the approval and acceptance of AI by our employees? Many are anxious about how AI is going to impact the workforce.”

Answer: This is such an important question. Some of the techniques that help employees embrace AI quickly and energetically include: 

  • Be transparent about their concerns. Communicate early and often the many benefits of AI, specifically how your firm will be using AI, and what specifically you are doing to address their concerns. Authentic communications is key.  
  • Having an easily articulated AI vison or strategy. If your team can understand a general AI roadmap for the firm, how it supports the company’s objectives, and most importantly, how they fit in…it takes away a bit of the unknown. They feel “OK, I know where we’re headed and how it impacts me.” 
  • Provide ample opportunity for AI learning. Encourage AI literacy with workshops, lunch-n-learns, and similar ways for them to get practical and hands-on with it. This approach takes the mystery away and fosters a culture of curiosity and learning. 
  • Ensure your leadership team are worthy AI champions. As you know, they are always on display. If they aren’t fully bought-in on your AI strategy and model it for others, then there is little chance your workforce will embrace it. 
  • Turn them loose to experiment. You’ll be surprised how innovative they can be. At Barge we held an innovations contest to generate potential AI use cases. Pretty creative ideas…and they felt ownership! It’s a wonderful involvement technique. 
  • Celebrate small wins. Reward the behaviors you desire. Ask employees to share how they are leveraging AI successfully in their roles. Even let them teach other colleagues. It’s the Influencer Principle at work; sometimes employees trust change communicated from someone like themselves. 

Question: Security and privacy seem to top the list of leader concerns around AI. Leaders ask “What are the ways we can use AI so we don’t compromise any of our proprietary data?”. 

Answer: I surely can relate; we too make this a priority. Protecting sensitive information will be highly situational among firms, depending on many different factors. But here are some common approaches that may help guard your company data. 

  • Focus on use cases such as predictive maintenance, process optimization, and simulations that don’t require direct access to proprietary designs/IP 
  • Leverage AI assistants and chatbots that use public data sources and knowledge bases 
  • Implement robust data governance policies, access controls, and encryption 

In our engineering world at Barge, we see uses for AI that don’t leverage proprietary data and IP. Examples include: 

  • Predictive maintenance and equipment monitoring that don’t use custom designs or product specs 
  • Process optimization and simulation without exposing sensitive data 
  • Intelligent assistants and chatbots 
  • CAD and modeling 
  • Cyber security and threat detection 
  • Remote monitoring, and others 

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