A modern survey of senior executives and AI leaders shows that while enterprise decision-makers trust the potential of AI, many lack confidence in their company’s strategy for implementing it, as well as the readiness of the data to ensure the reliability of AI results. What’s more, 7 in 10 executives say their AI strategy is not fully aligned with their business strategy today.
Study conducted for Teradata By NewtonX, a leading global B2B market research firm, included expert interviews and quantitative research with executives and decision-makers who have inside knowledge of their company’s AI strategy and execution. All respondents are responsible for artificial intelligence or operate it in their work. While 61 percent said they fully trust the reliability and accuracy of their AI results, 40 percent do not believe their company’s data is yet ready to produce correct results.
Artificial intelligence is indispensable, but clear, tailored strategies are uncommon
While 89 percent of business executives believe AI is indispensable to staying competitive, only 56 percent say their companies have a clear AI strategy, and only 28 percent believe their AI strategy is closely aligned with broader business goals and supports them. The most successful AI implementations are at the department level – just 12% have implemented AI solutions company-wide, while 39% have implemented AI in selected departments.
Executives identify the top benefits of AI as significantly increasing productivity (51 percent) and improving customer service (50 percent). However, despite the potential of customer-facing applications, most senior managers prefer to pursue AI projects that improve internal processes because these projects tend to minimize AI risks and are seen as more likely to improve cost control than to drive growth.
- About half of managers surveyed have successfully used AI to enhance employee productivity and collaboration (54 percent) and support decision-making (50 percent), but only a third have used AI for product development (30 percent) or forecasting sales and revenue (30 percent) ). percent).
- More than half (57 percent) of surveyed executives said they were concerned about how AI errors could impact customer satisfaction, company reputation, or both, noting that greater consistency between AI and planning is needed to be successful. business.
- Even for internal projects, 63 percent of surveyed managers operate a combination of closed and public datasets, while only 29 percent rely solely on closed datasets.
- Barriers to successfully scaling AI projects include:
- AI technical talent shortage (39 percent);
- Lack of budget needed to scale AI projects (34%);
- Difficulty measuring business impact (32%); AND
- Insufficient technological infrastructure (32%).
While 73 percent of respondents see their companies as first adopters of multiple technologies, 60 percent said their level of AI adoption is just “on par” with the competition; just 27 percent consider themselves at the forefront of AI adoption in their industries.
Increasing trust is imperative
Confidence in AI designs and outcomes is critical for executives. One participant said, “…we want to be very clear with our clients about what data was used to train the models,” noting that it is basic to introduce bias into the models by selecting the wrong training sets. Another said, “… master data management is not glamorous, but… if you base everything on data and the data is flawed, you have a problem.”
In addition to unbiased data, survey participants said increased operational efficiency (74 percent), demonstration of successful operate cases (74 percent), and improved decision-making processes (57 percent) are among the most essential factors that can demonstrate an organization’s confidence in a modern AI deployment. It’s also very essential to trust AI, and it’s also essential to prioritize vendors and partners that facilitate seamless integration with top-of-the-line AI solutions (67%).
In other findings, respondents noted the following:
- Reliable and confirmed results (52%), consistency/repeatability of results (45%) and the brand of the company that built the artificial intelligence (35%) are the three most essential factors building trust in artificial intelligence.
- Security (61 percent), transparency (55 percent), governance (45 percent) and improving business performance (40 percent) were cited as key aspects of trusted AI.
Contributing to the success of artificial intelligence
Respondents identified the following key factors in their AI success to date: clear strategic vision and leadership support (46%); effectively communicating the benefits of AI to stakeholders (46%); and sufficient investment in AI technology and infrastructure (41%).
The majority of respondents (84%) said they expected results from AI projects within a year of implementation, and more than half (58%) said results would be measurable within six months. Another 60% said they had already seen a “clear return on investment” from existing AI solutions.
About the study/methodology
The survey was conducted in the U.S., Europe, the U.K. and Asia and surveyed senior executives and AI decision-makers at companies with at least 1,000 employees and annual revenues of more than $750 million. The survey included about 300 AI executives from companies including Nike, P&G, Hermes Paris, Allianz Partners, Prudential Financial, Honeywell and Novartis, with about half of respondents located in the US.