The tenor of the conversation involving investments in artificial intelligence (AI) is starting to shift from trying to identify potential opportunities for creating a sustainable competitive advantage to realization that organizations may now be falling behind. In effect, AI is rapidly becoming a new set of table stakes that organizations are required to make if they expect to remain competitive.
As a result, more business and IT leaders are once again having a classic build versus buy conversation. In many cases, capabilities that once required a significant amount of investment to build and customize AI models are now becoming features of applications. That doesn’t mean organizations will abandon efforts to customize AI models altogether, but it does mean they will become much more selective about which projects to fund if it looks like a provider of a commercial application is going to make one or more AI capabilities more easily accessible.
Additionally, more thought is being put into the size of the AI models that might be required to drive a specific use case. Accessing AI models in the cloud based on the cost of input and output tokens quickly adds up, so in many instances it now makes more sense to custom a smaller instance of large language model (LLM) that has been trained using a narrow set of data that has been closely vetted for a specific use case.
In some cases, application providers are passing those costs on to customers, while others are beginning to view AI as just another feature to be provided for a minimal extra cost as part of an effort to prevent customers from potentially defecting to a rival.
Regardless of approach, the one thing that is certain is that competitive pressure is increasing. A survey of 213 CEOs conducted by The Futurum Group, for example, finds more than three quarters (77%) are feeling either a high (35%) or moderate (42%) sense of competitive pressure. Rather than being something that the board of directors is driving or a move to shore up the value of a company, strategic investments in AI are now much more tied to achieving and maintaining operational efficiency.
“We’re seeing a fundamental shift from AI as innovation theater to AI as operational necessity,” says Nick Patience, vice president and practice lead for AI for The Futurum Group. “Companies are getting ruthlessly practical – they’re choosing smaller, targeted models over expensive flagship deployments because survival trumps sophistication.”
All these issues and more will be explored next week during an AI Unleashed virtual summit hosted by Six Five Media, but the one thing is that many early adopters of AI are revisiting their strategies. The overall goal is to find a way to reduce the total cost of pervasively applying AI in a way that enables organizations to remain competitive, while hopefully identifying strategic initiatives that provide a meaningful competitive advantage.
How long that advantage can be maintained will vary widely. As organizations invest more in AI, they will become more adept at closing any gaps in capabilities that emerge. That’s assuming, of course, that the organization has made some level of investment that provides the foundation needed to adroitly respond to any competitive threat fueled by AI technologies that might suddenly appear with little to no actual warning.
