AI/ML Capabilities Fall Short of Executive Expectations, According to a New Global Study by SoftServe

AI/ML is deployed as a series of ‘science projects’ to attract talent but is not aligned with business goals

New data released by SoftServe indicates a dramatically different understanding of the value of artificial intelligence and machine learning (AI/ML) among executives at software and digital native companies.

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“What we’re seeing is a significant digital divide between company leaders who know little about how to monetize AI/ML, and the teams in charge of implementing it”

Interest in AI/ML has been heightened with the recent release of tools such as ChatGPT and the rollout of AI-powered Microsoft Bing. However, SoftServe’s survey of nearly 600 IT leaders across nine countries tells a story of significant eagerness for AI/ML, but a great disappointment with it.

While 56% say leadership should consider AI/ML an urgent priority to drive business results, 52% say it should be a priority to retain and appeal to skilled talent. Conversely, 72% of IT leaders say their executive team does not fully understand the technical capabilities of AI/ML and its potential for business success. Furthermore, of those surveyed, 42% say their leadership is not treating AI/ML as urgently as it should be — to the point where this vital investment is falling down the list of priorities.

Most concerning, 83% agree that within the next five years, the only competitively viable publishers of business and consumer software will be those that have successfully integrated AI/ML functionality into products and overall business strategy.

“What we’re seeing is a significant digital divide between company leaders who know little about how to monetize AI/ML, and the teams in charge of implementing it,” said Chuck Ros, Industry Success Leader for High Tech, SoftServe. “The team who owns AI/ML in software and digital native businesses is critically important. This determines how the technology gets aligned with the business strategy, customer needs, and how it is monetized. For most software and digital native companies, AI/ML still sits in IT, indicating that many executives are unsure of its ability to transform products, services, and the value delivered to customers.”

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The independent findings, compiled by Wakefield Research, reported the opinions of nearly 600 Chief Information Officers, Chief Data Officers, Chief Technology Officers, and Vice Presidents of IT in North America and Europe. Data highlights from the report reveal:

• 98% view AI/ML investment as a priority, but 42% believe their leadership does not place sufficient urgency on these AI/ML investments.
• The three principal reasons investments fall short of expectations:
  • 39% – lack of talent or skillset
  • 38% – inadequate or insufficient funding
  • 36% – lack of business integration
• Nearly three in ten (29%) say their organization is extremely or very unprepared to manage data to train AI/ML platforms
• Almost all (96%) are less than fully prepared.
• When it comes to AI strategy in products and features, pressure is coming more from leadership (53%) and board members (47%) than customers.

SoftServe believes there are two critical actions that software and digital native companies must take now to ensure a successful business strategy and longevity.

• Align to Business Outcomes – It may sound obvious, but surprisingly it is one of the most significant barriers to success. From organizational strategy to business outcomes, a lack of alignment perpetuates haphazard initiatives, endangering both the reputations of IT and AI/ML. First, leadership must better understand the technology’s best applications and use cases. Then, when implemented, the organization must focus on the outcome — a revenue increase from the effective monetization of this crucial technology.

• Ownership Influences Outcomes – The data demonstrates that investments in AI/ML fall far short of expectations. AI/ML investments should be managed as product innovation instead of IT “science projects” to assuage curious programmers or demanding boards of directors. For AI/ML to succeed within ISVs, ownership must shift from IT to product management, ensuring the total value of its capabilities receives the same development rigor as customer value and CX features.

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