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Articles October 3, 2025

Why So Many AI Initiatives Are Failing to Deliver ROI

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CapTech

New Executive Research Reveals the Keys to AI Success

Tech executives today are navigating an environment where the demand for artificial intelligence (AI) is relentless: boards expect strategic updates, competitors accelerate adoption, and markets insist on innovation. But in the rush to deploy AI, decision-making often becomes reactive, driven by hype and short-term performance demands.

Executive Summary

To uncover the challenges organizations face in adopting AI, CapTech led a research effort that featured commissioned interviews with executives in leadership roles at multibillion-dollar corporations across the financial services, healthcare, retail, hospitality, telecom, and consumer goods industries. The findings uncovered six leadership risks across industries that consistently undermine AI return on investment (ROI), ranging from technology-first strategies to misaligned transformation efforts.

In response to the research, we’ve offered recommendations for a critical paradigm shift across each of the six trends. The organizations that succeed with AI will be those that prioritize business outcomes with governance at the core, take purposeful but realistic risks, and engage employees in the process with trust and transparency.

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Trend 01: AI Has Become a Mandate

Knowledge of AI is still very primitive. So you have people making decisions without knowing what AI can do. — VP Technology, Healthcare Digital Transformation

The Pressure: Keep Up with the (Fast-Changing) Times

AI has moved from a strategic priority to an operational mandate, pressuring tech leaders to deliver rapid AI innovation and results. A Chief Growth Officer (CGO) at a public consumer goods company shared, “Our board was like, what’s your AI strategy, folks?

Generative AI investments are expected to rise 94% this year to $61.9 billion.

Ninety-nine percent of executives plan to invest in AI this year, and 97% feel compelled to act quickly. As one fintech Chief Revenue Officer stated, “We do our roadmap every six months... intentionally, because the world is evolving way too fast.” This fast- paced environment fosters a cycle of urgency-driven adoption, where speed often eclipses strategy.

The Risk: Technology-First Strategies

With immediate business pressures dominating their attention, many executives are making reactive decisions and creating technology-first strategies that prioritize AI platforms over business outcomes. A healthcare Vice President (VP) of Technology said, “If everyone’s doing AI and you’re not... something’s wrong. So, let’s do AI. That’s hype-based... ‘We have to do AI to check the box.’” 

While over three-quarters of organizations use AI in at least one business function, only 21% have redesigned workflows to align with business outcomes. IBM’s CEO survey echoes this disconnect, with 64% of CEOs admitting they’ve invested in tech before understanding its business impact. 

These findings underscore a critical risk: when technology leads without a business anchor, AI becomes a solution in search of a problem.

The Paradigm Shift: Business-First AI Strategies

Technology should follow business needs, not lead them. Real-world use cases illustrate this shift: a healthcare VP described using AI to proactively scan patient data for early diagnosis; another is exploring AI to automate testing and quality assurance in software development. A CGO emphasized AI’s potential in demand planning and forecasting to reduce workloads and improve accuracy. These examples show that when AI is applied with intention and focus, it delivers measurable value.

CapTech has helped clients achieve measurable improvements in efficiency, cost savings, and customer satisfaction—key business objectives for these clients—through AI solutions.

$850K

in annual savings at a state’s Department of Labor through a workers compensation initiative

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80%

reduction in task time for a leading logistics provider

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30%

reduction in infrastructure costs at a leading health insurer

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CapTech

Trend 02: Risk Aversion Creates a Game of Wait-and-See

The Pressure: Don’t Be Last

The fear of being left behind is real, but so is the fear of moving too fast without a clear roadmap. This dynamic creates a tension between innovation and caution, where leaders are watching the market closely but delaying bold action.

Many executives adopt a wait-and-see approach, hoping to learn from others’ mistakes rather than risk their own. “Let’s see what other people are doing... if I want to get the advantage right now, I cannot be the last person in line,” said an Information Technology (IT) leader at a large financial services firm. Another added, “It’s a risk factor. How much are you willing to stomach?”

The Risk: Don’t Be First 

Executives are feeling the urgency to act on AI, but many are hesitant to lead. A global study of over 1,500 executives found that only 15% of executives are willing to be at the forefront of AI-driven business change. This reluctance is echoed in a Thomson Reuters C-Suite survey, where most leaders expressed a desire to see successful implementations elsewhere before committing heavily themselves. This mindset can stall progress, as organizations hesitate to experiment or invest until others prove success. The result is a slow- moving landscape where fear of failure outweighs the potential for innovation.

The Paradigm Shift: Start Now 

To move forward, executives must start to embrace meaningful risks. Starting now doesn’t mean going all in; it means launching pilots with available data and infrastructure, learning quickly, and refining along the way. Progress creates readiness, not the other way around. By taking action, even in limited scope, organizations build momentum and confidence while still managing risk and moving toward innovation. The key is to shift from hesitation to experimentation, recognizing that waiting carries its own cost.

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Trend 03: Near-Term Results Outweigh Long-Term AI Investments

We’re constantly balancing the pressure to hit quarterly numbers with the need to invest in AI capabilities that won’t show ROI for 12–18 months. It’s a tough sell internally, even when we know it’s the right move. — VP Technology, Financial Services

The Pressure: Competing Expectations for Innovation and Performance

Executives consistently report tension between delivering fast results while investing in future AI capabilities—a pressure that’s affecting decision-making across industries. AI is positioned as a game-changer, but expectations for sweeping, enterprise-wide transformation often outpace internal capabilities and clarity on ROI.

AI project failure rates appear to be elevated, as organizations attempt to rapidly deliver generative AI projects. On average, organizations scrap nearly half of their AI projects between proof of concept and broad adoption, and the percentage of companies that abandon a majority of their AI initiatives before production has surged from 17% to 42% year over year.

The Risk: Big-Bang Transformation

Thinking In response to growing expectations for results, many executives pursue ambitious, multimillion-dollar AI programs that promise transformation but lack a clear path to value. These initiatives often stall before delivering meaningful impact, weighed down by complexity, cost, and unclear ROI.

“If I was a big company, I’d be using stronger AI technology... I just do not have the dollars and human horsepower,” said a CGO at a public consumer goods company. This mindset—where scale is prioritized over specificity—can lead to wasted resources and diminished confidence in AI’s potential. Without a focused strategy, transformation becomes a burden rather than a breakthrough.

The Paradigm Shift: Start with Low-Hanging Fruit

Use AI now to strategically address near-term operational challenges in a meaningful way. To unlock real value, executives must shift from big-bang thinking to agile, outcome-driven experimentation. That means targeting high-impact opportunities aligned with business objectives, validating ROI early, and scaling with intention. By breaking transformation into smaller, iterative phases, leaders can build momentum, adapt quickly, and demonstrate business value early and often.

For example, CapTech partnered with a Fortune 50 financial services company to conduct a current- state AI assessment, which led to a 50% reduction in tool sprawl, improving infrastructure efficiency and reducing operational costs. The engagement incrementally addressed business needs with governance strategy at the forefront, and over time, it grew into an enterprise transformation, spanning AI product rationalization, governance, infrastructure, and long-term strategic planning. And it simply started with an impactful assessment.

Plus, instead of focusing solely on cost-cutting, executives can invest in AI to create new revenue streams and operational efficiencies. Small investments are better than no investment at all, especially when they deliver measurable results. This approach not only reduces risk but also fosters a culture of learning and innovation.

Trend 04: Lagging Employee Adoption Is a Widespread Challenge

We started with 500 licenses, and people are not using it... how do we make sure employees are adopting that technology? — Chief Information Officer, Telecommunications

The Pressure: Employees Are Slow to Adopt AI

While many employees express interest in AI, workplace adoption is low. In fact, CapTech’s 2025 Consumer Survey found that less than 20% of respondents’ AI usage is for work purposes. Drivers of disengagement include a lack of training and support, distrust in privacy and security, and a lack of meaningful opportunities to engage with technology. Fourteen percent of individuals report that they are uncomfortable using AI tools.

This adoption gap is especially pronounced when AI initiatives are rolled out without employee involvement or clear communication. In fact, up to 70% of AI-related change initiatives fail due to employee pushback or inadequate management support.

The Risk: AI Implementation Without Employee Engagement

Without a people-first approach, even the most promising AI investments risk falling flat. Organizations frequently underestimate the importance of trust, training, and change management in successful AI adoption. There’s a common assumption that. implementation alone will drive usage. But without engagement, adoption stalls.

“We have some people that are AI resistant, and I’m like, well, give me back your computer,” joked a Chief Brand Officer at a hospitality brand. This resistance isn’t just about technology. It’s about fear, uncertainty, and a lack of clarity around how AI will impact their roles and workflows. Over 50% of workers are worried about the future impact of AI in the workplace, and a third of workers believe it will lead to fewer job opportunities. When employees aren’t brought along on the journey, AI becomes a source of friction rather than empowerment.

The Paradigm Shift: People-First AI Transformation

To drive meaningful adoption, organizations must treat change management as a strategic imperative. Position AI as a tool for human intelligence augmentation, not replacement. This means engaging employees early, building trust through transparent data privacy and security practices, and offering training and reskilling opportunities tailored to their roles. Successful leaders proactively ask what tools work, what doesn’t, and what support employees need to succeed.

For example, AI has helped customer service agents resolve issues faster by providing context before the conversation even begins. According to a VP of Technology of a large financial institution, “AI has been helpful in being able to take care of people without needing to speak to an agent... when they do, the agent has much more information... agents can resolve issues quicker.” When employees are empowered, AI becomes a catalyst for performance, not a barrier to it.

Trend 05: Security and Compliance Are Non-negotiable

Cyber-resiliency is critical... it’s a matter of when, not if you will get attacked. — Chief Technology Officer, Healthcare

The Pressure: Heavily Regulated Industries

CapTech’s clients consistently cite policy and IT constraints as key reasons for delayed AI adoption, which reinforces the need for compliance-first innovation.

Even when AI solutions are promising, internal approvals can be slow. “You have to get the okay from compliance... some things are frowned upon,” noted an IT leader in financial services. These realities create a cautious environment where innovation must be balanced with rigorous oversight.

The Risk: Compliance Is a Blocker

A 2025 survey spanning 46 of CapTech’s clients revealed that policy restrictions, security concerns, and tooling limitations are the top three systemic blockers preventing AI adoption.

Some organizations avoid AI altogether rather than risk missteps, even when safe experimentation is possible. A third of respondents indicated that CapTech’s clients don’t use AI at all. Additionally, 10% of CapTech’s Consumer Survey respondents said their employers don’t allow AI usage.

“You can’t experiment with patient data... because of privacy laws,” explained a Chief Marketing Officer at a large healthcare system. This approach can stifle innovation and prevent organizations from exploring AI’s potential.

Other executives struggle to find vendors that meet strict security requirements. “The challenge is, these [AI startups] don’t have the security type we’re looking for... we’re looking for big organizations,” said an IT leader in financial services.

The Paradigm Shift: Compliance Is a Partner

Rather than viewing regulation as a barrier, forward- thinking organizations treat it as a creative constraint. By building AI solutions with compliance from the start, executives can reduce risk and accelerate adoption. This means choosing vendors not just for scale, but for agility, alignment, and co-creation potential. Fit-for-purpose partnerships allow organizations to innovate within guardrails, ensuring that AI initiatives are both secure and impactful. When compliance is integrated into the strategy from the very beginning, regulated industries can move from hesitation to momentum.

Ethical AI practices are not only reputational safeguards. They’re competitive advantages with consumers. Sixty-six percent of CapTech’s Consumer Survey respondents were highly concerned about their data being misused or shared without authorization, signaling that cybersecurity and transparent privacy practices sway consumer behavior.

CapTech’s partnership with a Fortune 50 financial services company led to the development of TRISTA, an AI-powered virtual assistant designed to transform trade surveillance and data analysis with governance at its core. By combining generative AI with deep contextual understanding of business logic and data structures, TRISTA enables compliance teams to surface insights faster, reduce alert fatigue, and shift from reactive investigations to proactive decision-making. This solution exemplifies how fit-for- purpose AI tools can drive measurable efficiency gains, while leading with governance and regulatory priorities.

Trend 06: Tech Solutions Tend to Scope Creep

Now that we built a retention AI-powered bot, retention becomes a CIO issue. — CIO, Telecommunications

The Pressure: New Chief Information Officer (CIO) Responsibilities

As AI becomes embedded across every department, Chief Information Officers are facing a surge in cross-functional demands. What were once isolated business challenges are now technology challenges, requiring broad coordination and strategic oversight.

At the same time, data platforms are consuming tens of millions in IT budgets, yet foundational gaps persist. Many executives report lacking the dollars, talent, or clean data needed to scale AI effectively, compounding the pressure on CIOs to deliver more with less.

The Risk: Infrastructure Overwhelm

CIOs consistently report significant increases in AI- related requests from teams that previously operated independently from IT, creating prioritization challenges and straining already limited resources. As CIOs are expected to support enterprise-wide innovation, they also manage legacy systems, security, and compliance. Without clear boundaries or strategic alignment, technology teams risk being pulled in too many directions, diluting focus and slowing progress. This scope creep can lead to burnout, budget overruns, and stalled initiatives—especially when expectations outpace infrastructure readiness.

The Paradigm Shift: Incremental Wins with Leadership Support

Incremental wins build momentum. Rather than waiting for ideal conditions, successful CIOs advocate for realistic investment strategies, launch targeted pilots, learn fast, and scale what works. That way, pragmatic budgeting and staffing strategies can create sustainable AI growth rather than overextension. Leadership support is also essential to allocate funding and help manage scope creep. The formation of an AI leadership council is a practical way to help ease the burden and empower CIOs. When leadership champions business-oriented AI initiatives, they not only unlock resources but also foster a culture of collaboration and accountability.

Generate Value at Every Stage of AI Maturity

AI maturity varies widely across organizations, yet every industry shares the same imperative: to generate business value. While 92% of companies plan to increase AI investments, only 1% consider themselves “AI mature,” with fully integrated workflows and measurable outcomes.

CapTech has identified four progressive stages of AI adoption across five key dimensions. Regardless of where an organization falls on the maturity spectrum, progress is possible, and value can be realized at every level of AI maturity.

Foundational

At the Foundational stage, executives focus on building data readiness with a scalable cloud platform, establishing governance and cyber- resiliency, and experimenting with targeted use cases that have measurable benchmarks and clear ROI metrics. These early efforts lay the groundwork for trust, scalability, and future innovation.

Emerging

In the Emerging stage, organizations begin to expand automation, integrate AI into more workflows, and upskill teams to support broader adoption. This phase is about increasing the nimbleness of work, beginning to transform your workforce, and raising baseline expectations.

Matured

As organizations reach the Matured stage, they begin to scale agentic AI, orchestrate multi- agent systems, and embed AI into core business processes. These efforts drive efficiency and unlock new capabilities across the enterprise.

Visionary

At the Visionary stage, companies enable self- optimizing, adaptive AI ecosystems that fuel continuous innovation and competitive advantage.

Reframing Executive AI Strategy

As AI adoption becomes both a strategic imperative and a source of mounting pressure, the findings from CapTech’s Executive Research underscore a critical truth: success with AI is not just about technology. It’s about leadership. Across industries, executives must move beyond reactive deployments and hype-driven decisions to embrace a paradigm rooted in governance, business outcomes, and employee engagement. 

By confronting the risks identified in this research and adopting the recommended paradigm shifts, organizations can unlock AI’s potential not only as a tool for innovation but as a catalyst for sustainable value creation.

Research Methodology

This executive research was conducted in collaboration with the Harris Poll in August 2025 through 30-minute interviews with senior executives at the C-Suite and VP levels, across technology, marketing, and customer experience functions. Participants represented multibillion- dollar organizations within the U.S.

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Brian Bischoff

Principal, CTO

Brian leads our business strategy for our practice areas and is a thought leader in digital strategy. He provides an objective perspective through the strategic analysis of current technology trends and designs and builds great experiences for our clients.

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Bree Basham

Bree Basham

Principal, Go-to-Market & Growth Strategy

Bree leads our Customer Experience practice, creating digital strategies and solutions using modern technologies to deliver meaningful and measurable experiences for our customers. She has served as a Creative Director for many omnichannel experiences within the retail space, as well as for a number of other industries that CapTech serves.

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