Highlights –

  • The proportion of businesses falling into the “underachiever” category—those that have implemented many AI initiatives but produced subpar results—rose from 17% to 22% this year.
  • The Deloitte report also warned that businesses eventually target a broader spectrum of use cases when they spend more on AI.

Deloitte announced the release of the fifth edition of its State of AI in the Enterprise research report, which polled over 2,600 executives worldwide on how companies and sectors implement and expand Artificial Intelligence (AI) projects.

The Deloitte survey revealed that while AI continues to inch tantalizingly closer to the enterprise’s core—94% of business leaders concur that AI will be essential to success over the next five years—for some, results appear to be lagging.

For instance, 79% of respondents—up from 62% last year—said they had successfully deployed three or more types of AI applications at full scale. However, the percentage of businesses falling into the “underachiever” category—those that have implemented a larger number of AI initiatives but produced subpar results—rose from 17% to 22% this year.

One may think this to be in contradiction, but according to Beena Ammanath, executive director of the Global Deloitte AI Institute, it is not unexpected. She continued by saying what is surprising is how quickly the AI landscape is changing—to the point that what started as a Deloitte study produced every other year is now being produced annually.

She said, “The AI space is evolving so fast, and the technology is growing so rapidly.” The fact that many companies are underachieving with the outcomes of their AI investments “tells you how fast the technology is being deployed.” She said that while investment in AI is there, many people cannot stay up in gaining value due to the rapid pace of AI research and implementations.

Deloitte recommends four AI action stages

To fuel the company’s AI transformation entirely, the Deloitte report focuses on four essential measures that can immediately power widespread benefit from AI.

First, it’s crucial to invest in culture and leadership. Ammanath stated, “AI is not something that can be relegated to your IT or back-end teams. This can have a real impact on your core business; it can create new revenue opportunities, new product ideas that just didn’t exist before.”

Instead, what’s needed is for business leaders to buy in, not just to implement technology but bring in the right talent and improve the company’s culture.

“The entire organization needs to be on board with the technology, whether it’s the AI team deploying and building the tool or you have an AI team actually creating the models,” she added.

The same is not the case in most organizations. The survey claims that while 43% of respondents said they had appointed a leader to ensure successful human and AI collaboration, concrete actions have lagged.

The key is to transform operations

According to the Deloitte analysis, high-outcome firms are “significantly more likely to adopt additional operational leading practices.”

These include tracking the ROI of implemented models and applications. Eighty-six percent of high-outcome businesses do this compared to only 71% of low-outcome companies.

Additionally, high-outcome organizations are more likely to have the quality of data in AI models and a well-documented process for governance, follow documented MLOps procedures and a documented AI model life cycle publication strategy, leverage a common and consistent platform for AI model and application development and use an AI quality and risk management process and framework to assess AI model bias and other risks before models go into production.

The survey noted that the results are particularly pertinent considering that a majority (60%) of respondents saw AI solutions as strategically “extremely important” for the success of their firms, including more than 55% of respondents from low-outcome organizations.

Coordinating technology and talent

Given that both human talent and AI-driven technology have specific skill sets, the Deloitte analysis showed that firms must plan their investments in both AI technology and talent concurrently.

According to the report, an organization’s ability to develop differentiated tools and applications with AI still heavily depends on the talent it can recruit internally. However, Ammanath also stressed the significance of ensuring that everyone in the organization is familiar with the fundamentals of AI.

“You [may not] need to know what a diffusion model is, for example, but you need to have a high-level understanding of what AI is … that basic foundational training should be something that you provide to every employee,” she said. Then, firms must offer role-based training so that, for instance, a data scientist assisting a marketing team is aware of the precise inquiries required to assess a vendor.

Lastly, businesses must offer governance frameworks so that staff members are not expected to be experts in every AI tool. Ammanath advised, “There are tactical things you can do to actually start addressing these issues and make this less complicated for your employees right now.”

Choose the appropriate AI use cases

The Deloitte report also warned that businesses eventually address a broader spectrum of use cases as they increase investments in AI. When choosing which business processes to implement first, organizations should exercise caution because the decisions they make today could determine how soon they achieve good outcomes and develop momentum.

Ammanath declared, “Doing AI for the sake of AI is never a smart idea. You know, you start buying some of these tools or setting up a Data Science Center of Excellence without having a clear pathway towards the value – whether it’s not deploying it in the right function or not putting the right level of thought into it – it can actually create a false start and impact the end goal”.

Ammanath remains optimistic

Ammanath describes herself as an “AI optimist” who sees a lot of potential in businesses that are now falling short in the results of their AI projects.

She added, “I do hope that underachievers are able to tap into the ecosystem, learn from these best practices, and get more value. I honestly believe that AI can bring a lot of value, and we can address the risks, but we also need to focus on the value creation”.