The potential for artificial intelligence to transform industries is undeniable. From generative AI tools that supercharge productivity to predictive models that inform strategic decisions, AI holds enormous promise. McKinsey estimates that generative AI alone could add $2.6 to $4.4 trillion in annual value across industries, with use cases spanning customer support, marketing, software engineering, and more.
Yet despite this upside, most organizations are struggling to move beyond experimentation. In fact, while 92% of companies expect to increase their AI investment, only 13% have scaled AI initiatives across their business, and a mere 1% consider themselves “AI mature”. This dissonance indicates the growing problem – business leaders must develop sustainable AI implementation and end-user adoption strategies.
The long-term opportunity for GenAI is estimated to be $4.4 trillion in productivity gains for enterprise use cases. This potential can only be tapped by focusing on AI to complement human productivity. Adapting company culture and preparing employees is vital as business leaders find new ways to improve their businesses with AI.
The challenge isn’t the technology — it’s adoption. True enterprise transformation only happens when your people know how to use AI, trust it, and are supported through every step of the journey.
This article explores how to build an AI adoption strategy that centers your people, scales your implementation, and drives ROI.
Why Do Organizations Need a People-First AI Strategy?
AI adoption is as much a human transformation as a technological one. Leaders often fall into the trap of investing in the best models and tools, without preparing their workforce to use them. By aligning AI practices and governance with the needs of employees and other users, business leaders can promote sustainable growth while improving user experience across the board.
A recent study showed that 25% of companies have implemented GenAI technologies broadly across their offices – with another 25% having targeted plans to rollout GenAI in the coming months. Yet, only 10% of employees use these tools weekly, and only 15% believe their organization has a clear plan for successfully integrating GenAI into their business strategy. This leaves room for increased implementation and development of human-centered AI strategies.
As Soumitra Dutta, professor at the Cornell SC Johnson College of Business, says, “We have to use technology, not because technology exists, but because it helps us to become better individuals. When organizations deploy AI inside their work processes or systems, we must explicitly focus on putting people first.”
Maximizing the effectiveness of AI in the workplace depends on reframing the conversation around AI from the macro to the micro, from the robot taking over the workforce to a working companion that handles certain types of tasks.
For example, generative AI can automate tasks and analyze data, but it takes a human to process the products of AI and consider them within real-world contexts, experiences, and history. When developed thoughtfully, this partnership maximizes the benefits AI can bring to a business and employees’ careers.
Business leaders must structure teams and design processes that center humans as much as possible. This framing prioritizes human agency, which empowers employees to make better decisions, work more productively, and experiment with AI’s many possibilities.
When presented in this way, employees may be less hesitant to engage with AI tools. From there, it is up to managers and other business leaders to prepare their workforce to embrace artificial intelligence at work. Managers need to ensure that employees do not feel their value is in question or being undercut.
Securing full-scale AI adoption requires support through reskilling, upskilling, and disseminating information that can help employees adapt to new perspectives in work, productivity, and success.
Why Closing the AI Skills Gap Is a Business Priority
According to the latest Thomson Reuters Future of Professionals Report, 77% of professionals believe AI will greatly impact their careers. While that impact is mostly seen as positive, a critical gap exists between AI’s growing presence and employee readiness.
This AI skills gap encompasses things like a lack of foundational knowledge in AI and data literacy, inadequate familiarity with AI-powered tools and their use cases, a shortage of hands-on experience using AI in real-world scenarios, and persistent discomfort or resistance toward AI’s role in the workplace.
Failing to find actionable strategies to close this AI skills gap includes risks like:
- Failure to achieve ROI from AI investments: Organizations are investing heavily in AI, but without employee adoption, those tools collect dust and become failed AI investments. Upskilling ensures AI tools are used to their full potential, safeguarding ROI.
- Sluggish innovation: AI-powered decision-making, productivity, and automation unlock new levels of efficiency. But without reskilled teams, progress slows, and opportunities are lost.
- Widening workforce inequality: Neglecting AI upskilling will deepen digital divides and disadvantage underrepresented groups. Closing the gap fosters inclusion and reduces bias by ensuring AI tools reflect diverse perspectives.
- Low adoption, low impact: Employees won’t adopt what they don’t understand. Investing in personalized, role-specific training increases usage, boosts satisfaction, and amplifies the business value of AI.
How to Create a People-First AI Adoption Approach
Developing a human-centric approach to artificial intelligence requires prioritizing humans in every aspect of implementation and use. A people-first AI strategy ensures that:
- Employees understand why AI is being introduced.
- Adoption is aligned to real workflows and productivity goals.
- Change is introduced gradually, with support at every stage.
Ultimately, AI will only generate value if it’s understood, trusted, and consistently used by your workforce.
Here are a few ways in which organizational change leaders can drive AI adoption in the workplace:
Showcase contextual AI use cases on how it improves the quality of work
Generative AI is redefining workplace productivity. By automating tasks and helping people make data-driven decisions. This technology enables workers to accomplish tasks more efficiently and effectively. Start by identifying where AI can drive measurable improvements in productivity, accuracy, or decision-making — and start there.
By automating tasks that are more tedious or repetitive, AI gives employees more time to exercise their creativity and talent. As a result, this can lead to improved employee experience, increased confidence, and a renewed motivation to excel at work.
Here are some impressive ways AI is improving productivity in the workplace:
- Resolving routine support inquiries and providing links to relevant support resources.
- Automating business processes such as data entry, schedule optimization, and manufacturing workflows.
- Analyzing data to forecast trends and predict outcomes.
- Personalizing user interactions based on historical or behavioral data.
- Generating or improving content for marketing campaigns or internal announcements.
Notice that these use cases do not completely overlap with the core purpose of employee roles. Rather, they lay the foundation that empowers employees to do the work they are passionate about, and it gives them the time they need to refine their talents and produce exemplary work.
Create a frictionless UI for GenAI products
Optimal user experience is a key aspect of human-centered design. Employees already have their plates full, so when it comes to embracing AI tools, a smooth experience is essential. Ensure that AI interfaces are intuitive and easy to navigate. A frictionless UI eliminates frustration and keeps employees focused and in the moment.
Here are some essential aspects of people-first AI UI:
- A simple, uncluttered interface allows employees to move through AI workflows without thinking twice about their next move.
- Personalized experiences keep employees engaged and ensure that employees emerge from learning experiences feeling their time was well-spent.
- Embedded feedback mechanisms have double the usual benefits, helping users adjust behaviors in real time and empowering them to review the usefulness of various AI features within the flow of work.
By prioritizing user interface design, business leaders can ensure that employees can learn new AI tools with fewer obstacles and make the most of their new tools more quickly.
Provide hands-on AI training and role-based user onboarding
Experiential learning has become increasingly prevalent in recent years, and for good reason. People tend to learn more quickly and retain more information when they learn by doing. This principle also applies to AI adoption.
Many organizations use the 70-20-10 model to optimize workplace learning through a hybrid, but experience-heavy approach. With this model, employees absorb 70% of information from hands-on learning experiences, 20% from person-to-person interaction, and just 10% from traditional education activities.
Modern digital adoption platforms, like Whatfix, support employees in the flow of work through in-app guidance, proactive help, and contextual assistance. With Whatfix Mirror, teams can build interactive software environments for risk-free training activities and application experimentation. Tools like this empower users to learn in realistic environments without the stress of impacting live data and allow leaders to tailor training and workflows to the needs of different users.
Support end-users in the flow of work
By embedding user onboarding and training for new tools into application environments, leaders can help employees learn new tools and workflows on their own terms, without losing productivity. With a digital adoption platform like Whatfix, managers can enhance user adoption by delivering relevant messages and tutorials that guide learners through the process. These messages can be custom-tailored to meet individual user needs, delivering support for best practices. It can also help users understand significant risks and the importance of data security related to AI tools.
By providing in-depth and comprehensive support within the flow of work, leaders can ensure that employees have the support they need to feel prepared and confident as they begin using generative AI at work.
Collect feedback on how to improve AI to better meet user needs
People-first AI adoption keeps human experience at the forefront from start to finish. As employees navigate new tools, provide ample feedback opportunities. By collecting feedback and making improvements accordingly, leaders can demonstrate their commitment to employee support while further maximizing the potential impact of AI integration on business.
End-user feedback opportunities can come in many different forms, from post-training surveys to manager check-ins. Here are some questions to ask employees to provide a better AI adoption experience:
- How comfortable were you with AI before beginning this training, and has that comfort level changed?
- Did you find the messaging in this training engaging and relevant?
- Was anything about this experience confusing or frustrating?
- Are there any resources you wish you had before starting this training?
- How do you feel this AI tool will impact your work?
How to Accelerate AI Adoption Without Productivity Disruption
Rolling out AI tools often leads to temporary disruption — new workflows, changed responsibilities, and learning curves. But disruption doesn’t have to stall progress. Here are a few strategies to accelerate AI user adoption without hurting productivity:
- Pilot with Power Users First: Test with early adopters who can provide feedback and model usage for peers.
- Embed In-App Support: Use Digital Adoption Platforms like Whatfix to guide users within the application, reducing confusion and errors.
- Use Progressive Rollouts: Introduce AI gradually — one workflow, department, or use case at a time.
- Analyze Behavior: Track usage to identify where users struggle, then improve training and design.
This approach ensures you’re not just launching AI; you’re operationalizing it, smoothly and sustainably.
How to Track AI User Engagement & Adoption
Effectively tracking how your organization, its departments, and individual end-users engage with and adopt AI tools (such as copilots, assistants, or embedded ML workflows) is essential for attributing impact, realizing ROI, and driving continuous improvement. Organizations must go beyond surface-level AI usage and uncover deep behavioral insights to identify what’s working, what’s not, and where to optimize.
Here are three key areas to help you track AI adoption and measure its impact:
1. Monitor AI user engagement across teams
Understanding who engages with your embedded AI copilots and how frequently they’re being used is foundational for tracking adoption and measuring impact.
- Measure DAU and MAU trends across tools and teams to monitor adoption velocity and habitual use.
- Segment usage by role, department, and region to identify adoption gaps and high-performing user groups.
- Identify power users, engaged teams, and successful use cases driving measurable outcomes or innovation across the organization.
2. Map AI-integrated workflows
AI adoption is only meaningful when it enhances real work. That’s why it’s critical to understand how users are integrating AI into their day-to-day processes.
- Track where and when employees use AI tools in their workflows across apps, systems, and processes.
- Analyze AI interaction sequences to understand how tools are embedded into broader task journeys or decision-making flows.
- Pinpoint user friction or drop-off moments, such as where AI usage is abandoned mid-task or workflows fail to produce expected outputs.
3. Quantify the ROI of AI copilots and assistants
Organizations need clear attribution between AI usage and business outcomes to justify continued AI investment.
- Track core success metrics tied to AI objectives and business KPIs, such as productivity, data quality, and operational efficiency.
- Attribute AI interactions to outcomes like increased task throughput, faster service delivery, or improved customer satisfaction.
- Benchmark pre- and post-AI implementation metrics, such as time-to-completion, accuracy rates, or decision quality.
- Estimate ROI based on measurable impact, adjusting for tool cost, enablement effort, and the scale of deployment.
With Whatfix Product Analytics, organizations can easily track AI end-user engagement on any enterprise application. Segment end-users based on usage, department, or other cohort types. Map critical workflows to visualize them with Journeys and identify where dropoff is occurring with Funnels.
How to Identify and Overcome AI Adoption Barriers
Despite significant investments in AI, many organizations struggle to realize its full potential. Understanding and addressing the common barriers to AI adoption is crucial for successful implementation. Below are key challenges and strategies to overcome them:
1. Lack of clear AI strategy and governance
A well-defined AI strategy and robust governance framework are essential for aligning AI initiatives with business objectives. McKinsey’s research indicates that organizations with CEO-level oversight of AI governance are more likely to achieve significant bottom-line impact from their AI investments.
Action steps to define a outcome-based AI governance strategy include:
- Establish a cross-functional AI governance committee to oversee AI initiatives.
- Develop a clear roadmap that outlines AI objectives, timelines, and success metrics.
- Ensure executive sponsorship and regular communication about AI goals and progress.
2. Employee resistance and fear of change
Employee apprehension about AI, often stemming from fears of job displacement and mistrust in AI systems, can hinder adoption. A Pew Research Center survey found that 52% of U.S. workers are more worried than excited about the increasing use of AI in the workplace.
To overcome resistance to change, organization leaders should:
- Engage employees early in the AI adoption process through transparent communication.
- Provide reassurances about job security and highlight how AI can augment their roles.
- Offer forums for employees to express concerns and ask questions about AI initiatives.
3. Insufficient training and AI skills gap
A lack of adequate training and AI literacy among employees can impede effective adoption. Prosci’s research reveals that 38% of AI adoption challenges are due to insufficient training in AI tools.
Here are a few ways organizations can enable their employees:
- Implement comprehensive AI upskill training programs tailored to different roles within the organization.
- Encourage continuous learning through workshops, online courses, and hands-on projects.
- Leverage digital adoption platforms to provide in-app guidance and support.
4. Integration challenges with existing systems
Integrating AI solutions with existing IT infrastructure can be complex and resource-intensive. Prosci notes that 16% of AI adoption challenges stem from system integration issues. To overcome technology-related integration issues, AI product leaders and IT teams can:
- Conduct thorough assessments of current systems to identify integration points.
- Collaborate with IT teams to develop integration plans that minimize disruptions.
- Pilot AI solutions in controlled environments before full-scale deployment.
Examples of a People-First AI Strategy
Here are some examples of major brands implementing people-first AI strategies.
- Microsoft helped U.K. oncologists use machine learning to analyze MRI scans, freeing up precious time and energy to spend more time with patients and providing heartfelt support.
- Freshworks uses a people-first strategy to integrate AI into engineering processes, leading to 61% of its engineers saying they produce better code with fewer defects.
- McKinsey partnered with the global bank, ING, to launch an AI-powered chatbot to enhance customer service, expand support functions, and protect sensitive customer data.
- Canada has recently rolled out a human-centered AI strategy for its federal public sector to maximize the amount of time government employees can spend handling complex tasks that serve Canadians.
AI Adoption Clicks Better With Whatfix
The path to AI maturity isn’t about deploying the most advanced model — it’s about getting your people to embrace, trust, and use AI every day.
Successful AI adoption is a change management challenge, not just a technical one. It requires clear communication, real-time enablement, leadership alignment, and continuous iteration.
Whatfix helps large organizations accelerate AI adoption by:
- Delivering in-app guidance tailored to role, task, and behavior
- Supporting AI rollout inside CRM, ERP, HR, and analytics tools
- Enabling faster onboarding and training with contextual help
- Tracking behavior and identifying adoption gaps with analytics
- Reinforcing process change through task lists, Pop-Ups, and Smart Tips
To unlock the full ROI of your AI investments, enable your people where it matters most — in the flow of work.
Ready to learn more about Whatfix for AI Adoption? Request a demo today!
