The AI gold rush these days is littered with abandoned enterprise projects, with humans — not the technology itself — being blamed for high failure rates of AI projects.

Recent data indicates that stagnant AI projects were often the result of poor vision, mismanagement, and a lack of resources.

Eagerness from the top to become “AI-first” companies is also putting pressure on C-suite execs and other IT decision-makers who might not have the budgets, systems, or tools for success.

Though IT leaders will get better at dealing with AI as they gain experience, the learning curve is steep, said Jack Gold, an analyst at J. Gold Associates. “It’s not really all that different from past new technologies and the challenges they posed, such as early databases, the move to web and browser-based apps and more,” he said.

Early-stage venture capital (VC) firms act as validators of AI technologies. Partners are usually as engaged as the founders of AI startups, attending meetings with tech leaders, prototyping, and guiding portfolio companies.

But VCs and CIOs have different risk profiles and priorities when it comes to AI. “When the CIOs are involved, it’s in a very different way…. That CIO is thinking about whether or not they’re going to get fired,” said Julia Moore, managing partner at Breakout Ventures.

With that in mind, Computerworld talked to venture capitalists about how companies could deliver on successful AI projects.

1. Look at how AI will change business

It’s clear now that AI is transforming existing business structures, operational layers, organizational charts, and processes. “As a CIO, if you look at long term, you get better visibility of the outcomes of AI,” said Sandhya Venkatachalam, founder and partner at Axiom Partners.

“Today, a lot of these net new capabilities are taking the form of AI performing the work or producing the outcomes that humans do, versus emulating or automating software tools,” Venkatachalam said.

The shift will inevitably displace legacy systems and processes. She cited customer support as an early area ripe for upheaval.

“Who is going to disrupt Salesforce from an AI perspective?” Venkatachalam said. “Because [at] call centers…, people [used to be] answering calls; 1763803683 AI is answering calls…and you just saved a bunch of money.”

2. Focus on outcomes, not just AI technology

IT leaders should frame AI projects around results, not technology, said Moore.  “Founders look at impact as opposed to the technology in a way — is this going to change this particular industry, as opposed to what is the AI technology behind it?” Moore said.

Tech chiefs can focus on high-leverage work that creates value by automating time-consuming tasks, said Brad Harrison, founder and managing partner at Scout Ventures.

“For CIOs…, think big term, prototype, understand, worry less about the technology and worry about the outcomes — and think about big picture,” Harrison said.

3. Think about what you need tomorrow, not today

VCs typically don’t look at what buyers need right now; they look ahead. Similarly, IT leaders should look at how AI can transform their industry in the future.

The real value of AI is in displacing legacy stacks and processes, and short wins or scattered AI initiatives mean nothing, Venkatachalam said.

Adding AI to existing workflows — like building an internal large language model (LLM) — is often a waste. Enterprises are also wasting time building proprietary tools and infrastructures, which duplicates work already commoditized by big research labs, Venkatachalam said.

AI tools change every six months, and the focus should be on big-picture outcomes, not technology. “We don’t fund the 17th AI coding co-pilot, or yet another attempt to change search. Again, all good, useful stuff, completely covered, completely valued, but not the next big thing,” Venkatachalam said.

Axiom Partners’ investments include HR firms such as Circle8 and the fintech company Cannock-EDR.

4. Partner to move faster

Enterprise organizations cannot move at the speed of transformation required by AI. That’s why IT leaders should partner with AI-native startups, which typically move faster. Most companies “are not designed for the speed of transformation that’s happening right now with compute and AI,” Harrison said.

Harrison’s Scout Ventures has invested in companies building AI tools in the defense industry. His annual gatherings connect portfolio firms with enterprise partners such as Lockheed Martin, L3Harris, IBM, and Red Hat.

Enterprise IT leaders also get access to a larger community of founders working on solving AI problems. “They’re getting really, really good at layering AI into solving these different pieces of the value chain in the right way and they’re getting really good efficiency out of that,” Harrison said.

Partnering with AI-native companies saves time and money and affects success rates, especially for first-time implementers, the VCs said.

5. Align your AI strategy to verticals

AI strategies link IT directly to core products, which dictates market survival. IT decision-makers should align AI strategies to their verticals markets. 

Physical AI is considered the next big AI technology after agents in some areas. And Harrison’s investments are in verticals such as defense and law enforcement, where AI manifests in the physical world. 

The defense industry demands real-world accountability, and AI technology can’t be experimental, Harrison said. “Where it meets the physical world is where I think we can have the most impact on humanity,” he said.

Moore’s Breakout Ventures invests in early-stage AI companies building datasets and tools that ultimately affect human health. In markets such as pharma and biotech, the science business is turning into a data business, she said.

“If you look at the life sciences…, you’re dealing with physics, chemistry, biology…, a much more complex data set. And so naturally pharma has to stay ahead of the game, because the competition is all digital, it’s all data,” Moore said.

6. Create an AI-first culture

Perhaps the biggest hurdle isn’t technical, but cultural. Younger “digital natives,” especially Gen-Z workers, view AI tools differently than senior leadership.

“There is a generational difference in how people are connected… digital natives versus digital immigrants,” Harrison said.

IT leaders should step out of the corner office and engage directly with team members and AI projects, which will bring useful insights. “I’m like… use your big brain, take one hour a week and put it towards that project,” Harrison said. “I think a lot of things would be much, much better.”

7. Get your hands dirty

IT leaders need to encourage internal prototyping and experimentation to stay ahead of the fast-moving AI curve.

John Mannes, a partner at Basis Set Ventures, said his team includes machine-learning engineers and data scientists that are brainstorming, prototyping, and building tools.

Mannes said it’s much more fun for CTOs and founders when his team can say, “Yeah, we tried those seven tools for databases, too, and don’t even waste your time with these six because holy hell, right?

“You’re in the trenches,” Mannes said. “It earns trust and it makes us much smarter as well, in terms of the people we surround ourselves with and how we support them.”