How to build a hype-free AI culture

To create a truly effective and sustainable enterprise AI strategy, organizations must create a hype-free culture around AI. Instead of discussing AI in extremes – such as that AI will replace jobs or be a fix-all for productivity – keep AI narratives neutral.

According to a Workday survey, 27% of business leaders view agentic AI designed to replace existing workplace functions as overhyped. Positioning AI as an enabler of productivity and workflows, rather than a replacement for human workers and current operating models, ensures the organization can foster a culture that embraces AI without overexaggerating its uses or fearing its effect on job displacement.

A hype-free AI culture should focus on business outcomes instead of models and technology architecture. Instead of focusing on the latest AI model or state-of-the-art AI technology, CIOs should look at their business needs and desired outcomes, as well as what success looks like.

When organizations focus more on models than outcomes, they can easily get lost in the hype of AI and diminish the value and business outcomes. In contrast, an outcome-first mindset promotes sustainability, reliability and scalability.

CIOs and other leaders are accountable for organization-wide AI messaging. Leaders set the tone for how the workforce views and interacts with AI tools. Creating a structured, aligned communication plan is essential to creating a healthy, hype-free AI culture within the organization.

"Accountability comes from observability. CIOs should always know where AI is running, what it is doing, and the value or risk it is producing, making AI dependable rather than controversial," said Larissa Schneider, chief operating officer and co-founder at Unframe AI. "The most effective CIOs earn trust through design. Fear inevitably arises when AI feels abstract and opaque, while confidence grows when it is specific, bounded, and directly embedded into real work."

Leaders have a responsibility to position AI as a tool and give the workforce a balanced, realistic view of how AI can be used within the organization. When leaders exaggerate the capabilities or detriments of AI, those feelings can trickle down in the workforce and negatively affect AI integration across the organization.

"The fastest way to create fear is to talk about AI as a substitute for people instead of a tool that changes how work gets done. CIOs need to be explicit that AI replaces tasks, not people, and then prove it through how they deploy it," said Jesse Todd, CEO at EncompaaS. "Employees don't need reassurance slogans; they need clarity. That means explaining where AI will remove friction from work, where human judgment still matters, and how roles will evolve as a result. When leaders avoid those conversations, people are left to make their own assumptions."

Before integrating AI into the workforce, executives must be aligned on realistic timelines, use cases and expectations for how employees interact with AI tools. A clear governance hierarchy should also be established to ensure consistent oversight of AI.

Leaders must also ensure that AI messaging reflects the organization's needs and does not rely on vendor-driven narratives that often amplify AI hype and exaggerate its usefulness and capabilities of AI.

"In many organizations, the real problem lies in mixed messages. The CIO plays a critical role in setting expectations about what AI is for, what it's not for, and how digital value will be measured," said Vittesh Sahni, senior director of AI engineering at Coherent Solutions. "That includes defining guardrails, being honest about risks, and stopping random pilots that don't tie back to strategy. When the messaging is grounded and consistent, teams trust the work and move faster."

One of the main appeals of AI is its ability to scale and grow over time, but when organizations implement AI without a proper foundation, scaling can disrupt the organization and harm it rather than help.

"A strong AI foundation starts with data, not models. If you don't understand what data you have, where it lives, how sensitive it is, and its value to the business, scaling AI will amplify existing problems rather than solve them," said Todd.

CIOs should prioritize data quality, AI governance and security when implementing any responsible AI strategy. "AI amplifies whatever system it enters, so if workflows are fragmented, data is inconsistent, or ownership is unclear, scaling it only accelerates dysfunction rather than creating value," said Schneider. "Before scaling, enterprises need consistent deployment across business units so that each use case strengthens the next, turning AI into a strategic asset rather than a cost center."

A solid AI foundation also requires infrastructure readiness, including cloud architecture, integration, and observability, to ensure AI can grow and scale without downtime or disruption. AI infrastructure also needs a solid, risk-aware compliance foundation, with model governance basics.

To create a truly hype-free AI culture, AI needs to be normalized as part of everyday work and treated like any other technology that enables productivity.

Agentic AI, copilots and "digital workers" can help improve productivity and output without increasing head count. However, these tools must be closely monitored and used responsibly and ethically, including clear governance and oversight and a comprehensive strategy for how digital workers interact with and complement human employees and their workflows.

"When organizations frame AI as a replacement story, they slow down adoption and replace trust with fear about job security. The strongest results still come from hybrid systems, where AI does the heavy lifting and humans stay in control of outcomes," said Sahni.

CIOs should also focus on identifying roles that AI may take over, redefining their job duties to adapt to AI use, rather than replacing them. "The mistake many organizations make is redefining job titles instead of workflows," said Todd. "CIOs should look at where time is actually spent today and ask how AI can improve throughput and quality without breaking accountability."

Being transparent with employees and avoiding fear-based narratives ensures that, once AI technologies are implemented, employees feel empowered and confident to use them in day-to-day work.

"Over time, roles evolve from task execution to system stewardship, where employees own outcomes, refine workflows, and manage ambiguity," said Schneider. "This shift creates stronger, more leveraged roles, allowing employees to work with systems rather than being trapped inside them, making the transformation evolutionary rather than disruptive."

A hype-free AI culture relies on metrics that make a true impact to measure and track success, rather than vanity metrics that indicate how much AI is being used by the organization rather than its impact on the bottom line.

"Too many AI initiatives are celebrated simply because something was built, rather than because it actually improved something," said Sahni. "A no-hype approach begins when teams start small, measure early, and scale only what creates real digital value. One strong KPI in production will tell you more than ten pilots running in parallel."

CIOs should implement key performance indicators (KPIs) that demonstrate true value and focus on ROI, productivity gains and risk reduction, such as revenue impact, time-to-value and cost reduction.

CIOs should also engage in a continuous improvement loop by assessing KPIs and comparing them over time and optimizing the AI strategy based on results. "Ultimately, the best AI systems feel obvious in hindsight," said Schneider. "When AI fades into the background and becomes part of how work gets done, that's when it delivers its greatest value."

Scaling AI is the goal for most organizations. However, doing it too soon can cause operational disruption and organizational harm. Scaling should only occur when business value has been proven, technology stacks are consistently operational and maintained, and there is a clear hierarchy of governance and ownership.

"Scaling too early creates a credibility problem," said Todd. "Once users see inconsistent or incorrect results, trust is lost and adoption stalls. Recovering from that is far harder than taking the time to get the foundations right in the first place."

An effective AI governance strategy should enable the organization to further innovation within reasonable limits, rather than limiting or blocking innovation to ensure long-term cultural maturity and AI growth.

Alison Roller is a freelance writer with experience in tech, HR and marketing.