11 surefire ways to fail with AI

Enterprises are investing a lot of money in artificial intelligence tools, services, and in-house strategies. But unfortunately, big outlays don’t guarantee success.

“AI is everywhere — transforming industries, reshaping workflows, and promising a future of limitless possibilities,” says Paul Pallath, vice president of applied AI at technology consulting firm Searce. “But for every AI success story, there’s a silent failure: expensive pilots that never scale, models that reinforce bias, and systems that become obsolete within months.”

The difference between success and failure lies in how AI is implemented, governed, and sustained, Pallath says. “To get AI right, organizations must avoid the most common — and costly — missteps.

Here are 11 ways organizations fail with AI, and suggestions of ways to address these pitfalls.

Not including users in AI planning

“The fastest way to doom an AI initiative? Treat it as a tech project instead of a business transformation,” Pallath says. “AI doesn’t function in isolation — it thrives on human insight, trust, and collaboration.”

The assumption that just providing tools will automatically draw users is a costly myth, Pallath says. “It has led to countless failed implementations where AI solutions sit unused, misaligned with actual workflows, or met with skepticism,” he says. “AI must integrate seamlessly into workflows, align with employee responsibilities, and be supported by clear governance. Without buy-in, AI risks being underutilized or outright rejected, rendering investments ineffective.”

Engage employees from the outset, involve them in AI’s development, and foster transparency, Pallath says. “Co-create governance frameworks that ensure AI aligns with business realities, empowering teams to trust, refine, and maximize its potential,” he says. “The key is building AI with your people, not despite them.”

Neglecting to train and educate

AI can have a bad reputation, with employees jittery about losing their jobs to machines. It’s up to leadership to ensure that people understand how and why their organizations are using AI tools and data.

Without a workforce that embraces AI, “achieving real business impact is challenging,” says Sreekanth Menon, global leader of AI/ML at professional services and solutions firm Genpact. “This necessitates leadership prioritizing a digital-first culture and actively supporting employees through the transition.”

To ease employee concerns about AI, leaders should offer comprehensive AI training across departments, Menon says. “By educating employees on how AI can improve their work, not just make it faster, organizations can foster a culture of curiosity and acceptance toward AI, which is crucial for success,” he says.

“No single type of training will be appropriate for all staff that will be touched by AI,” says Douglas Robbins, vice president of engineering and prototyping at technology and research and development company MITRE Labs. “For example, hands-on developers will need a different level of AI understanding than those in acquisition.”

IT leaders are not lost on the importance of AI training, as 95% believe AI projects will fail without staff who can effectively use AI tools. It’s just that only 40% of IT leaders say their organizations have formal AI training for employees, according to a recent survey from Pluralsight.

Shortchanging the importance of an actionable AI roadmap

Each organization’s path to AI maturity will be slightly different, Robbins says. “Develop an AI roadmap that documents the value proposition aligned to mission, as well as when, who, and how capabilities will be developed, tested, deployed, and sustained,” he says. While he was commenting about federal government agencies, the advice can apply to any organization.

Reaching the necessary level of AI maturity to meet goals will be a multi-step process, Robbins says. “Focusing on both what worked and what barriers still exist will be vital to informing future efforts and guiding where to put resources to address potentially long-lead-time problems like policy or hiring,” he says.

Broad categories that should be included in a roadmap for AI maturity include strategy and resources; organization and workforce; technology enablers; data management; ethical, equitable, and responsible use; and performance and application, Robbins says.

Downplaying data management

Having high-quality data is vital for AI success. “Without solid data foundations, AI adoption becomes nearly impossible,” Genpact’s Menon says. A recent Genpact and HFS Research survey of 550 senior executives shows that 42% think a lack of data quality or strategy is the biggest barrier to AI adoption. Poor data hygiene undermines AI success, Menon says.

“Build a centralized data platform to organize and manage data coming from multiple sources,” Meno says. “This ensures high-quality, well-curated data to drive AI models successfully.”

Subpar and inaccurate data “doesn’t just threaten decision-making; it can lead to regulatory mishaps,” adds Souvik Das, chief product and technology officer at financial software firm Clearwater Analytics.

Companies need to establish a governance framework for data management. “Ad-hoc data management is out, and a structured framework is in, providing clarity and consistency in roles, responsibilities, and processes [such as] ensuring data is meticulously cleaned and access is controlled and compliant,” Das says.

Data governance can be as tricky as it is vital, with lots of pitfalls to avoid.

Assuming AI is a ‘set-it-and-forget-it’ solution

AI isn’t a one-time deployment. “It’s a living system that demands constant monitoring, adaptation, and optimization,” Searce’s Pallath says. “Yet, many organizations treat AI as a plug-and-play tool, only to watch it become obsolete. Without dedicated teams to maintain and refine models, AI quickly loses relevance, accuracy, and business impact.”

Market shifts, evolving customer behaviors, and regulatory changes can turn a once-powerful AI tool into a liability, Pallath says. Left unchecked, AI might produce outdated or even harmful results, eroding trust, revenue, and competitive edge, he says.

“Build dedicated teams to monitor AI performance, automate updates, and refine models continuously,” Pallath says. “Treat AI as a living system — one that thrives on iteration, learning, and proactive governance to deliver sustained value. Success isn’t just about deployment — it’s about long-term commitment to excellence.”

Ignoring responsible AI frameworks

One of the most dangerous oversights in AI implementation is neglecting to establish robust ethical frameworks, Pallath says. “Without clear guidelines for responsible AI use, organizations risk deploying biased algorithms, mishandling sensitive data, or pursuing problematic use cases that can trigger regulatory penalties and reputation damage,” he says.

Strong ethical frameworks aren’t constraints; they’re enablers that align AI initiatives with organizational values and stakeholder trust, Pallath says. “Build comprehensive responsible AI frameworks from day one,” he says. “Prioritize ethics, compliance, and transparency in every AI initiative. Responsible AI isn’t just about risk mitigation — it’s a competitive advantage that builds trust, credibility, and business resilience.”

Overlooking the risks

AI deployments, like any IT initiative, come with risks. Some of these involve cybersecurity and others relate to data integrity and privacy.

“The lack of standardized ethical AI considerations creates challenges in managing potential risks associated with AI, such as biased algorithms and security breaches,” Menon says. “The repercussions of these issues can be severe, resulting in reputational damage and legal liabilities.”

Enterprises need to take measures to protect AI data and ensure data privacy and integrity.

“By establishing guardrails early on in accordance with the responsible AI principles and the organization’s beliefs and strategies, organizations can mitigate risks, foster trust with customers, distinguish themselves from competitors, and pave the way for long-term innovation.”

Moving too quickly to deploy AI broadly

Blanketing an organization with AI use cases without first testing out concepts and applications in a few select areas could result in failure.

“Take a phased approach,” MITRE Labs’ Robbins says. “Start with simpler, low-intrusive applications and gradually advance to more complex and potentially intrusive uses. Early AI applications can assist in tasks like data analysis, real-time language translation, and administrative automation.”

Over time, more advanced uses such as identifying previously undetectable patterns can be adopted, provided the governance frameworks are in place, Robbins says.

Not taking existing processes into account

“As AI execution starts, it’s crucial to apply the same focus on rethinking processes on how the work gets done,” says Lan Guan, chief AI officer at IT and professional services firm Accenture.

“Business leaders have a heightened sense of urgency to ‘make things happen,’ but failing to look at processes can hurt efforts to scale gen AI over the long term. We must resist using AI to merely amplify what is already broken.”

Expect this issue to magnify with decision-oriented agentic AI increasingly entering the enterprise.

Not establishing demonstrable ROI

Buying up scads of AI solutions without regard for return on investment (ROI) of the purchases is a good way to doom AI strategies.

“Many organizations rushed AI implementation without aligning their strategies with clear business objectives, making it difficult to measure success,” Menon says. “This lack of alignment hinders long-term impact and resource optimization.”

Leadership must first define the expected benefits of AI, ensuring that the strategy supports long-term growth, Menon says. “AI is power-hungry. You can’t afford to just throw more resources at the problem and hope for the best. Instead, leaders should carefully examine the cost implications of every AI-driven workflow.’

Underestimating the importance of measuring outcomes

AI without measurement is AI without accountability, Pallath says. “A fundamental mistake organizations make is launching AI initiatives without clear success metrics,” he says. “Without robust measurement frameworks, it’s impossible to validate if AI systems are delivering real business value or just creating technical debt.”

The inability to quantify impact undermines both current performance and future investments, Pallath. Organizations need to establish clear metrics before deployment. “Track both technical performance and business impact,” he says. “Remember that what gets measured gets improved, and only measured AI success can be replicated and scaled.”