8 AI Trends in Today's Big Enterprise

Even among some of the largest enterprise organizations, there's a big difference between the most advanced companies and others when it comes to artificial intelligence programs and deployments. While some are way ahead of the pack, there are others that are just getting started.

A new report, AI Transforming the Enterprise, from consulting giant KPMG, provides a view into top corporate leadership's perspective of where enterprises are with their efforts. How do you stack up among your peers as we enter the age of AI?

KPMG based its report on interviews with leaders in 30 large cap companies, plus analysis of job postings and media coverage for 200 of the top global companies, plus interviews with three technology companies that provide artificial intelligence technology to enterprises.

First, here's what the most advanced organizations look like. KPMG said that companies with the most mature AI capabilities are spending an average of $75 million on AI talent today with approximately 375 full-time employees working on AI. They expect those numbers to grow to between 500 and 600 workers in the next three years.

At the other end of the scale, KPMG said it found nearly a 10-fold gap in resources devoted to AI between the companies with more mature AI capabilities versus those companies at the early stages of their investments in the technology. Resources devoted to the programs matter.

But most organizations are still working to bring their artificial intelligence programs to the entire enterprise organization. The report noted that only 17% of organizations have scaled AI across the enterprise. Yet KPMG said that many companies are setting this as a key objective, and in three years half of the companies expect to be using AI at scale.

8 Trends

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

Rapid shift from experimental to applied technology. KPMG said that less than 3 years ago, many large organization leaders were just beginning to pilot AI applications. Now enterprises are converting pilot projects to production.

Automation, AI, analytics, and low-code platforms are converging. Organizations have been deploying these technologies at the same time and have learned that they work more effectively together, according to KPMG. 

Enterprise demand is growing. Across the 30 large cap companies interviewed, most reported to KPMG that their investments in AI-related talent and supporting infrastructure would increase approximately 50% to 100% in the next three years.

New organizational capabilities are critical. Successful AI programs need more than just technology. As always, the biggest challenges are cultural. You need the right talent, organizational capabilities, and processes that are driven through governance, according to KPMG. Among the companies interviewed, half said the CIO will play a leading role in overall AI strategy, and 40% said that a senior line-of-business leader would head up the program. Also, among those interviewed, 63% have established a Center of Excellence for AI strategy and 30% are in the process of formulating a Center of Excellence for AI strategy.

Internal governance is emerging as a key area. Governance around AI includes designing and deploying standard procedures for AI in areas including monitoring and managing risks, performance, and value. Governance will also oversee the end-to-end AI lifecycle, train teams according to common procedures, and create new roles and responsibilities and designate accountability.

"Strong governance creates the foundation for scale and scope: to embed AI across an organization, companies need consistent, purposeful, and responsible action across different teams," the report said. AI's expansion across the organization is dependent on the governance program.

The need to control AI. KPMG estimates that among the large companies it analyzed in this study, only 25% to 30% are investing heavily in developing control frameworks and methods to drive greater trust and transparency in AI. However, the firm says that an AI program's success may hinge on controlling the evolution of AI. Machine learning can change algorithms over time, and it can become more difficult to know why a particular decision was made. "The obvious risk is that continuous-learning systems can produce unintended or biased results," the report said. "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. While big companies will always build much of what they need in-house, some organizations may choose AI-as-a-service around a specific application or function. KPMG cites examples around automation of customer experience tools, exception handling for finance departments, and contract interpretation for law firms.

AI could shift the competitive landscape. Executives interviewed for the report believe that AI could change who the winners and losers are in the market. Those investing in AI, on average, report a 15% improvement in productivity for projects, according to KPMG.

Don't nickel and dime it

For organizations looking to gain competitive advantage from AI, KPMG recommends moving past pilots to implement the technology across the organization.

"Instead of focusing on individual use cases, consider how you can transform your business with AI," the report said.

For more on AI, machine learning, and digital transformation in the enterprise, read these articles: 

Enterprise Guide to Digital Transformation

Enterprises, Small Business, Lead Machine Learning Activity

Four Ways AI Can Augment Human Capabilities

Does AI Competence Matter?