Scanning the Big 4 Gen AI Horizon (A Primer)

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Talking to people about Gen AI is like walking through a flea market with cash in your hand. Everyone wants to sell you on their product or promise. You’re in the market for a reason. Where do you start? 

There are amazing accomplishments in AI and Gen AI around nearly every corner. If you haven’t seen ChatGPT’s Sora and the ability to create life-like video from text, well, just keep your head in the sand. There are lists and lists of the Top Generative AI companies cropping up almost weekly. Of course, there’s always the GenAI landscape charts like this one from Datacamp (which are quite helpful in many cases where you need information at a glance). Not all of these are relevant to your goals, strategies, and desires. 

You don’t want to choose between all Gen AI purveyors, you need someone to help filter the marketplace down to the strategic, applicable few that will maximize your chances for success in each proof-of-concept. You want to win.

Boston Consulting Group laid out five characteristics that set apart “winners from observers” in their January 2024 article, “From Potential to Profit with GenAI:” 

  • Investment in productivity and topline growth; 
  • Systematic upskilling; 
  • Vigilance about AI cost of use; 
  • Focus on building strategic relationships; and 
  • Implementation of responsible AI principles.

The key thing to note which goes unsaid is that accomplishing these five things requires a focus on people and process, not the tech itself. Productivity is a measure of human efficiency. It requires people to adhere to and improve in using a process. The ad hoc application of GenAI will decrease your productivity if you do not get colleagues to abide by the process improvements. 

The other four characteristics are even more focused on people and leadership. Upskilling requires having training and support programs to develop skills. Cost capture, through methods like activity based costing, is more about people’s ability to track and manage tasks accurately to ensure the right allocation of resources. You can’t claim cost savings you don’t capture while simultaneously increasing the cost of compute. Building relationships and responsible principles both hinge on your people. You need leaders and people in other parts of your business to be part of this journey.

McKinsey & Company provide a reminder in this article that, “if your data isn’t ready for generative AI, your business isn’t ready for generative AI,” and the role of the Chief Data Officer is, “to be clear about where the value is and what data is needed to deliver it.” The portfolio view and framework here are a good reference for anyone looking to provide AI use cases to the C-suite. 

Bain and Company take a broader approach. Rather than providing insights bespoke to GenAI, they look at the application within industries and departments. The Bain Insights blog on Artificial Intelligence is a good resource for thinking about the impact on topics ranging from healthcare and retail to procurement and finance. 

Accenture and IBM take the informative route. Each of these large firms provide a good overview to topics in Gen AI balancing technical depth against industry nuance. Accenture’s Generative AI Insights is definitely geared to marketing, but it provides meaningful definition and context if you’re unsure about certain jargon. IBM’s Artificial Intelligence blog may be the most informative. It provides you with the spectrum of AI from the classic, machine learning beginnings all the way to “Theory of Mind AI” and AGI of current science fiction. 

There’s a lot of information piling up about AI. Nearly every company has a product infused with AI, and everyone has a perspective about the benefits. Only you know what you’re looking for in the marketplace. If you make a moment to hone your desires you can position the right next step toward success.

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