According to a recent KPMG report, AI Transforming the Enterprise, companies with mature artificial intelligence (AI) deployments are spending an average of $75 million on AI talent today with departments of approximately 375 full-time employees. Within the next three years, they predict that number will increase to between 500 to 600 workers.

Of course, these organizations are on the far end of the spectrum as many companies are still struggling to develop AI programs and scale the technology across the enterprise. However, over the next three years, KPMG expects that will change, and we will see much more widespread AI adoption.

With that in mind, the following are eight key trends KPMG identified around deploying technology, organization, capital, and data strategies for AI success.

  • Rapid shift from experimental to applied technology: In under three years, most large organizations have gone from piloting AI applications to putting those programs into production, including predictive analytics use cases.
  • Convergence of automation, AI, analytics, and low-code platforms: Organizations have begun deploying these technologies concurrently and have discovered that they work more effectively together.
  • Growing enterprise demand: The majority of the organizations interviewed for the KPMG report stated that their investments in AI-related talent and supporting infrastructure would increase by approximately 50 to 100 percent in the next three years.
  • Need for new organizational capabilities: KPMG emphasized the importance of culture in fueling more widespread AI adoption. As InformationWeek’s Jessica Davis put it, “You need the right talent, organizational capabilities, and processes that are driven through governance.” As part of this trend, the majority of organizations surveyed either have established a Center for Excellence for AI strategy or are in the process of formulating one.
  • Internal governance increasingly important: As AI deployments increase, it follows that monitoring and managing risks, performance, and value will become more critical to overall success.
  • Need to control AI: KPMG believes that the success of an AI program hinges on controlling the evolution of the technology as machine learning can change algorithms over time, making it more difficult to know why a decision was made. According to the report, “The cost of getting AI wrong extends far beyond the financials—lost revenue, fines from compliance failures—to reputational, brand, and ethical concerns.”
  • Rise of AI-as-a-Service: As AI technology matures, some smaller organizations may select AI-as-a-Service around a specific application or function, including automation of customer experience, exception handling for finance departments, and contract interpretation for law firms among the examples cited by KPMG.
  • Changing competitive landscape: The executives interviewed by KPMG believe that AI could be a market game changer and level the playing field across numerous industries.

So, how does your organization’s AI strategies stack up against the trends outlined above?

Regardless of where you currently fall on the AI deployment journey, one thing is clear: AI is well on its way to driving big change enterprise wide. For more on what this could mean for your company, check out InformationWeek’s article on the KPMG report here.

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