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Why AI ROI Starts Depends On Governance And Training

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Jul 10, 2026, 08:45am EDT

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​Facing pressure to speed up AI deployment while also showing ROI on AI spend, many leaders are downplaying the need to prioritize policies and governance that guide the secure use of AI. Many also skip equipping employees with the training and know-how to confidently incorporate AI into daily workflows.

This is not a winning strategy and, unfortunately, the lack of governance and training is common.

A Thomson Reuters study highlights the gap between investment in AI and companies' AI objectives and outcomes. Despite 78% of organizations now using AI, only 38% of companies in North America published an AI policy. This absence of policies led 55% of respondents to turn to unapproved tools, also known as “shadow AI.”

In my experience, organizations underestimate the real threat to security that shadow AI poses, and how AI policies, guardrails and employee training can boost return on investment and long-term success of AI initiatives.

This is a critical juncture in the growth of AI because, while there have been significant investments made, there is also a disconnect between investment and outcome. According to a Gartner survey from December 2025, only 28% of AI use cases in infrastructure and operations meet ROI expectations, while 20% fail outright​.

Those failures become much more obvious when paired with the lack of governance described above and the fact that about half of tech professionals seek independent training because they feel their employers haven't adequately prepared them for AI adoption, according to a Randstad Digital study.​​

Without a strong foundation, AI projects may not be doomed to fail, but there’s a high chance they will not come close to achieving business objectives.

How Building A Firm Foundation Pays Off

AI is not just another collaboration tool or analytics software. Instead, it represents an entirely different way to use, process and leverage data. This is why it’s so important for business leaders to create guidelines, policies, governance and employee training processes to ensure it is deployed securely and safely.

The first steps are to understand the role these components play in setting the stage for successful AI outcomes:

Guidelines dictate AI usage and establish the rules for how AI tools are selected, used and evaluated. Without guidelines, AI adoption can become ungoverned and inconsistent.

In a recent Larridin survey, CIOs were asked how many AI tools are being used across the company. Their answers ranged from 60 to 70 . In reality, the number of unapproved tools being used is 200 to 300.

While guidelines may not eliminate shadow AI, they can effectively mitigate the threats it poses to data security, compliance and quality risks. Without guidance on approved tools and acceptable use, employees don't simply stop using AI, they find loopholes.

A 2026 Okta survey found that 52% of knowledge workers are using unsanctioned AI tools at work. The reason, in my experience, is that employees often feel they have been put in an impossible position: feeling pressure to use AI to meet rising expectations but with no clear boundaries around what is safe or sanctioned.

Governance and guidance work together to form the AI policy foundation.

While guidelines provide oversight on the vetting, selection and usage of AI tools, governance provides the structural oversight that keeps AI aligned with business objectives, ethical standards and regulatory requirements.

Guidance provides the accountability and oversight necessary to use AI responsibly and ethically, and provides a direct line to responsibility, including human review mechanisms and ownership, when things go wrong.

Strong governance and guidance generally require dedicated roles, such as AI ethics officers and review committees. Governance is also critical for risk management and compliance because it can provide proactive identification of AI-related risks (bias, security, data privacy, etc.) and the processes to monitor, audit and mitigate them.

Governance is also crucial because it clearly defines who in the organization is tasked with making sure the organization’s AI practices are aligned with ever-evolving AI regulations such as the EU AI Act and emerging state-level regulations and laws.​

Employee-centric AI begins with training.

AI training programs give employees the confidence to use AI and ensure they not only know how to use AI tools effectively but also understand their limitations. This lowers the risk of errors and the inadvertent misuse of sensitive data.​

Inconsistent or ineffective training can also severely impact the employee experience by increasing the chance of burnout , due to rework and lower confidence in outputs.

A recent Workday report notes a disconnect impeding employee upskilling: 66% of business leaders believe AI skill training is a top investment priority but only 37% of employees who use AI the most reported increased access to training.

Without effective AI training, employees may become stressed, causing their performance to suffer. Even worse, workers left to figure AI out on their own face disadvantages, including falling behind colleagues and experimenting without guardrails, exposing themselves and their organizations to data-security risks.

Investing In AI Success

AI governance and training are not checkpoint items but foundational investments in both business performance and top business assets: the people doing the work.

When organizations build in clear guidelines, guardrails and accountability from the start, they protect employees from the compounding effects of uncertainty, inequity and eroded trust.

Those that treat these elements as afterthoughts risk poor ROI, and they transfer the real cost of that decision onto their workforce. The organizations that get this right understand that responsible AI deployment and employee well-being aren't competing priorities. They are one and the same. ​​​​​​​​​​

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