Artificial Intelligence & Machine Learning

What's Preventing CIOs From Achieving Their AI Goals?

Almost every CIO aspires to use generative AI and artificial intelligence to drive business growth, improve customer experiences, innovate and automate processes. That means going beyond productivity use cases and embedding AI into enterprise applications and business processes. Due to formidable challenges, just one in five AI projects move beyond the proof of concept stage.

An article in Harvard Business Review puts the failure rate as high as 80% - almost double the rate of corporate IT project failures a decade ago.

Gartner says at least 30% of gen AI projects will be abandoned after the proof-of-concept stage by the end of 2025. Key reasons include poor data quality, inadequate governance, escalating costs and unclear business value.

"After last year's hype, although executives are impatient to see returns on gen AI investments, organizations are still struggling to prove and realize gen AI's business value. As the scope of initiatives widens, the financial burden of developing and deploying gen AI models is increasingly felt," said Rita Sallam, chief of research, data, analytics and AI and distinguished vice president analyst at Gartner, at the Gartner Data & Analytics Summit in Sydney.

According to an MIT Technology Review report, 72% of surveyed executives identified data as the biggest challenge for AI. Major obstacles include poor data management and infrastructure, internal process rigidities, and talent shortages. The report, created in partnership with Databricks, is based on a global survey of 600 senior data and technology executives, including 10 from Fortune 500 companies and startups.

Sixty-eight percent say unifying their data platforms for analytics and AI is crucial. Poor data quality can result in inaccurate outcomes and biases from AI models. Today, application data is in silos. It must first be aggregated, cleaned, organized and labeled to produce an appropriate dataset to train a model using machine learning.

Businesses Are Optimistic About AI

There is much optimism about using AI in business in 2025 and beyond. The MIT report says 94% of respondents are already using AI in their line of businesses and more than half expect AI to be widespread by 2025.

A recent global survey by Google Cloud, based on responses from 2,500 senior leaders in global enterprises with more than $10 million revenue, found that gen AI is a key driver of business transformation. By incorporating AI into their operations, companies can achieve substantial financial returns, improve efficiency and sustain growth. One key finding is that 74% of enterprises using gen AI reported ROI within the first year and 86% of those reported increased revenue of 6% or more. Yet many organizations are still in the exploratory stage, trying to understand which business areas will benefit from AI.

CIO and Analyst Views

While CEOs and boards have high aspirations about AI, those expectations are unrealistic due to unclear business value.

"AI is a vast topic, and, as an organization, we need a lot of maturity. We want to understand what can be applied to our business, and we will know that maybe six to nine months from now," said Nitin A. Harne, senior director of enterprise IT and cloud transformation at Capgemini.

Rather than just diving in and spending a lot of money on AI projects, organizations are more prudent than ever, especially since IT budgets are constrained.

"While no CIO wants to be left behind, they are also prudent about their AI adoption journeys and how they implement the technology for business in a responsible manner," said Dr. Jai Ganesh, chief product officer, HARMAN International. "While there are many business use cases, enterprises are prioritizing these on a must-have immediately to implement basis." Dr. Ganesh is involved with HARMAN's digital transformation solutions business, which offers consulting and ER&D services for customers. He also oversees AI implementation across his company.

Technology leaders say it will take at least two to three years before AI becomes mainstream across the enterprise. Rakesh Jayaprakash, chief analytics evangelist at ManageEngine, told ISMG that we would start to see "very tangible results" at a larger scale in another one or two years (see: ManageEngine's New Approach to Better Business Insights). "Tangible results" refer to commoditization of AI, which accelerates the ROI, he said.

"While there is a lot of hype around AI now, the true value comes when the organizations are able to see the outcomes," Jayaprakash said. "Right now, many organizations jump in with very high expectations of what is possible through AI, because we've started to use tools such as ChatGPT to accomplish very simple tasks. But when it comes to organization-level use cases, those are a little more complex." He suggests that organizations should first hone their AI algorithms and platforms to suit their unique needs.

Earlier this year, Ashley Casovan, board member at Responsible AI Institute and managing director of IAPP, told ISMG that robust governance and standards are needed for AI adoption at scale (see: Robust Governance, Standards Needed for AI Adoption at Scale).

Escalating Costs

As CIOs present their AI vision and strategy, boards also ask about cost and ROI. According to a Gartner research, a major challenge for organizations arises in justifying the substantial investment in gen AI for productivity enhancement, which can be difficult to directly translate into financial benefit. Many organizations are using gen AI to transform their business models and create new business opportunities. But these deployment approaches come with significant costs, ranging from $5 to $20 million.

And that is just for gen AI. The cost would be a lot higher for embedding AI into enterprise applications and business processes. CIOs will benefit from a watertight business plan that shows business value with strong outcomes before proceeding with such investments.