Feb. 5, 2026 · Authored by Jessica Drexler, Kelsey Engbrecht
Many organizations approach AI as an implementation effort, focusing on deploying tools, integrating systems and managing timelines. But succeeding with AI requires more than simply putting the technology in place. AI is a transformative initiative that reshapes how work is done, how decisions are made and how value is created. Treating AI like a traditional technology rollout often leads to low adoption, stalled value realization and growing resistance from the workforce.
True AI readiness requires shifting from implementation thinking to transformation thinking — and that shift starts with people. AI initiatives succeed only when employees see, understand and trust the real value AI can bring to their day-to-day work. Change management provides the structure to support this transformation, but only when it is grounded in a human-centered approach from the very beginning.
A human-centered foundation for change
Before defining what is changing, organizations must first emphasize with who is changing. AI transformation is experienced at the human level long before it appears in systems, metrics or dashboards. Employees are the ones adapting workflows, trusting AI-enabled outputs and ultimately deciding whether AI becomes embedded in everyday work or quietly ignored.
A human-centered approach requires engaging people early, before decisions are finalized, to understand real pain points, constraints and opportunities. This effort is not about socializing decisions after the fact; it is about enlisting frontline workers to shape or co-create direction. When employees are meaningfully involved, change shifts from something being done to them to something being built with them.
This early involvement is foundational to effective change management and essential to driving AI adoption. It creates shared ownership, surfaces practical insight and sets the stage for defining what is truly changing across the organization.
Defining what is changing: From implementation to transformation
When AI is treated as an implementation, future states are often designed based on assumptions rather than lived experience. The result is solutions that look good on paper but fail to align with how work actually gets done.
Successful AI transformation begins with clarity. Organizations must define their current state and articulate a realistic future state that reflects how work, decision-making and roles should function for maximum benefit. This comparison helps leaders understand the scope, scale and impacts of change; creating the foundation for identifying where AI can genuinely add value beyond system deployment.
Involving employees in defining AI use cases through structured co-creation methods — such as discovery workshops, frontline interviews, journey mapping and cross-functional working sessions — helps ensure that future-state designs address real business problems and create meaningful value. This collaborative approach also reveals hidden dependencies, skill gaps and cultural barriers early when they are far easier to address. This process is most effective when we focus on the work and opportunities for advancement rather than the technology or tools that will be implemented.
Co-creation does not mean crowdsourcing ideas without structure. When done well, it is intentional, focused and guided — allowing organizations to benefit from workforce insight without slowing progress or creating confusion. This balance is critical to sustaining momentum while building readiness. It’s the difference between posing a question that engages your staff and one that they perceive as a waste of time. Asking these questions early ensures that future solutions are grounded in real workflows and user goals, setting the foundation for faster design, stronger adoption and more durable impact.
Assessing AI readiness across 5 key dimensions
A comprehensive AI readiness approach considers multiple interconnected dimensions:
Strategy: Ensure AI use cases align with business objectives
Data: Confirm data is clean, trusted, and understood
Technology: Enable productivity without adding complexity
Risk: Apply appropriate policies and controls
Adoption: Evaluate organizational and individual readiness
By assessing readiness across these dimensions, organizations can prioritize efforts, focus on investments and align people and technology to drive measurable value.
Understanding individual impact and readiness
While AI transformation is organizational in scope, its impact is personal. Employees want to understand how AI will affect their work, responsibilities and long-term value. When these questions go unanswered, uncertainty fills the gap, which most often leads to resistance.
Assessing individual readiness helps organizations understand where confidence exists, where skepticism remains and where additional support is needed. This is best accomplished through intentional listening mechanisms – such as targeted surveys, focus groups and manager-led conversations – that surface how different roles and teams perceive change. Addressing the “what’s in it for me” question directly builds trust and reinforces that AI is intended to augment or support, not replace human contribution.
Clear, consistent communication is essential. Employees need to understand not only what is changing, but why it matters, how it aligns to broader goals and what support will be available as changes unfold. Insights gathered at the team and individual level should be used to tailor messages, sequence communications and clarify expectations by role. Exploration into impact at the team and individual level is necessary to create communications that resonate with employees, answer their most pertinent questions and address their fears.
AI fluency as the critical skill for AI adoption
AI fluency is the single most critical capability for successful AI adoption. Without it, even strong strategies and well-managed change efforts will fall short. AI fluency goes beyond awareness — it includes understanding what AI can and cannot do, how to use it responsibly and how to apply it effectively within specific roles.
Fluency builds trust. Trust drives utilization. Utilization drives value. When employees lack confidence or understanding, they hesitate to engage or disengage entirely.
Building AI fluency requires intentional investment, including:
Role-based education tailored to how people perform work
Transparency around how AI is used, governed and supported
Ongoing learning as tools, risks and use cases evolve
Organizations that prioritize AI fluency early create a workforce that is more adaptable, more confident and better equipped to realize value over time.
Enabling adoption through communication and training
Awareness alone does not drive adoption. Employees must feel capable and confident using AI-enabled tools. Effective communication and training strategies reinforce understanding, build confidence and support sustained utilization.
Key approaches include:
Reinforcing core messages over time using language that resonates with impacted audiences
Delivering role-based, practical training focused on real work scenarios
Activating internal champions who model new behaviors and support peers
These champions often become power users and trusted resources, extending adoption well beyond formal training efforts and providing essential support to users.
Aligning roles, expectations and performance
As AI changes how work gets done, organizational alignment must evolve. Operating models, job descriptions and performance systems should reflect new expectations and ways of working.
Employees need clarity on:
How their roles may shift
What skills are required
How success will be measured
When expectations are clear and supported by the right tools, employees are far more likely to embrace change and contribute to positive outcomes.
Sustaining momentum through continuous readiness
AI adoption is not a one-time event. Initial enthusiasm can fade as new tools are introduced, or challenges emerge. Without ongoing attention, utilization and value realization may decline.
Continuous readiness assessments help organizations:
Monitor engagement and adoption trends
Identify friction points early
Recalibrate communication and training strategies
Sustaining momentum requires reinforcement, visible leadership support and a willingness to adapt as needs evolve.
How we can help
We help organizations move beyond AI implementation toward true AI transformation by focusing on the human side of change. Our digital team’s approach is grounded in the belief that adoption starts with people, and that meaningful involvement early in the journey is essential.
We support organizations in engaging their workforce to identify real problems, shape meaningful AI use cases and build readiness for change — without slowing progress or overburdening teams. Through AI readiness assessments, impact analysis, AI fluency enablement and structured change management, we help leaders build trust, align stakeholders and sustain adoption.
