A new survey of C-suite executives and AI leaders shows while enterprise decision-makers trust the potential of AI, many lack confidence in their company's strategy to execute as well as the data readiness to ensure reliability of AI outputs. Moreover, 7 in 10 executives say their AI strategy is not fully aligned to their business strategy today.

The survey, conducted for Teradata by NewtonX, a leading global B2B market research company, included expert interviews and a quantitative study of executives and decision makers who have inside knowledge into their company's AI strategy and execution. Those surveyed all have responsibility for or use AI in their jobs. While 61 percent said they fully trust the reliability and validity of their AI outputs, 40 percent do not believe their company's data is ready yet to achieve accurate outcomes.

"The foundation of AI is clean, reliable, trustworthy data because it is the backbone of AI outputs," said Jacqueline Woods, Chief Marketing Officer at Teradata. "While achieving complete trust remains elusive for many executives, our survey shows a deepening understanding of how to reach trusted AI at enterprise-scale and confirms that Teradata is well positioned to help its customers with these business objectives."

AI is Essential, but Clear, Aligned Strategies are Scarce

While 89 percent of enterprise executives believe AI is necessary to stay competitive, only 56 percent say their companies have a clear AI strategy and only 28 percent see their AI strategy as closely aligned with and supporting broader business objectives. Most successful AI implementations occur at the departmental level -- just 12 percent have deployed AI solutions company-wide, while 39 percent have implemented AI in select departments.

Executives identify the most significant benefits of AI as a substantial increase in productivity (51 percent) and improvements in customer experience (50 percent). However, despite the potential of customer-facing applications, most C-suite leaders prefer tackling AI projects that enhance internal processes, as these projects tend to minimize AI risks and are seen as more likely to improve cost control rather than drive growth.

• About half of executives surveyed have successfully leveraged AI to enhance employee productivity and collaboration (54 percent) and support decision-making (50 percent), yet only a third have used AI for product development (30 percent) or sales and revenue forecasting (30 percent).
• More than half (57 percent) of executives surveyed said they are concerned about how AI missteps could impact customer satisfaction, company reputation, or both, noting that there needs to be greater cohesiveness between AI and business planning for it to be successful.
• Even with internal projects, 63 percent of executives surveyed report using a mix of closed and public data sets, while only 29 percent rely exclusively on closed data sets.
• Barriers to scaling AI projects effectively include:
  • Scarcity of AI technical talent (39 percent);
  • Lack of budget required to scale AI projects (34 percent);
  • Difficulty in measuring business impact (32 percent); and
  • Insufficient technology infrastructure (32 percent).

While 73 percent of those surveyed see their companies as early adopters with many technologies, 60 percent said their level of AI adoption is simply "on par" with their competitors; just 27 percent see themselves as leading AI adoption in their industries.

Increasing Trust is a Mandate

Trusting in their AI projects and outcomes is critical for executives. One participant said, "... we want to be very clear with our customers what data has been used to train the models," noting that it can be easy to introduce bias into the models by choosing the wrong training sets. Another said, "... master data management is not glamorous, but ... if you're basing everything off the data and the data is flawed, then you've got a problem."

Beyond unbiased data, survey participants said enhanced efficiency in operations (74 percent), demonstrated successful use cases (74 percent), and improved decision-making processes (57 percent) are among the top factors that can showcase trust within the organization around new AI deployments. Also very important to trust in AI is prioritizing vendors and partners that facilitate seamless integration with top-tier AI solutions (67 percent).

In other findings, those surveyed noted the following:

• Reliable and validated outcomes (52 percent), consistency/repeatability of results (45 percent), and the brand of the company that built their AI (35 percent) are the three most important factors in building trust in AI.
• Security (61 percent), transparency (55 percent), governance (45 percent), and improving the company's performance (40 percent) were cited as key aspects of trusted AI.

Contributing to AI Success

Respondents ranked the following as the primary contributing factors in their AI successes to date: clear strategic vision and leadership support (46 percent); effective communication of AI benefits to stakeholders (46 percent); and sufficient investment in AI technology and infrastructure (41 percent).

Most of the respondents (84 percent) said they expect to see results from AI projects within a year of deployment, and more than half (58 percent) said the results would be quantifiable within six months. Another 60% said they have already seen "demonstrable ROI" with their existing AI solutions.

"There is tremendous opportunity to improve AI trust by ensuring greater cohesion between business and AI plans. But planning only gets you so far," Woods said. "Working with the right partners and solutions can help accelerate trust by showing accurate results and ROI from AI projects quickly. But remember, all successful AI projects start with a foundation of clean, reliable data - I call it ‘golden data' - based on solid data sets and offering full transparency, and that's where Teradata can help."