AI Adoption by Sector: Who’s Leading and Lagging?

The new digital divide is here—and it’s powered by GenAI.

We’ve been here before. Email. Cloud. Mobile. Each transformative wave of technology drew a line between companies that adapted early and those that struggled to catch up.

Now, a new line is drawn—this time by generative AI.

Unlike previous innovations, GenAI isn’t just another productivity layer. It’s a capability multiplier that changes how organizations design work, deliver value, and compete. And while the technology is barely two years old, the divide between early adopters and the rest of the market is widening fast.

This digital transformation isn’t about who has the best tech but who is building the muscle to operationalize it. In some industries, GenAI is already reshaping product development, customer engagement, and employee enablement. In others, cultural inertia, lack of strategy, or fear of disruption create dangerous blind spots.

For digital leaders, the question isn’t “Should we adopt GenAI?”—it’s “How fast can we build the maturity to scale it across the business?”

This article explores the state of GenAI adoption across business functions and industry sectors—highlighting where momentum is building, friction remains, and what practical steps leaders can take to accelerate their GenAI journey.

What Is the Rate of GenAI Adoption?

In just over twenty-four months since ChatGPT’s public release, generative AI has moved from novelty to necessity. AI adoption is scaling at a pace few technologies have matched, outstripping the early trajectories of cloud computing, mobile apps, and even the Internet.

According to McKinsey’s Global Survey on AI adoption, 65% of professionals now report their organizations regularly use GenAI – up from 33% just over a year ago. In parallel, 72% of respondents said that their companies have implemented AI in at least one business function, and 50% use AI in two or more functions, signaling a shift from isolated pilots to broader functional integration.

At a personal level, GenAI adoption is growing as well. Every adoption bucket grew year over year when employees asked about the frequency of AI usage at work and in their professional lives. Regular work usage of GenAI grew from 8% to 13% between 2023 and 2024, and regular use for work and outside of work rose from 14% in 2023 to 26% in 2024.

What’s driving this growth?

Several converging factors, including:

• Accessibility: Tools like ChatGPT, Copilot, and Claude have brought GenAI into mainstream workflows with low barriers to entry.
• Pressure to transform: GenAI is no longer seen as a competitive edge in many sectors—it’s becoming a competitive baseline.
• Evolving capabilities: GenAI is moving beyond content generation into code writing, data synthesis, decision support, and agentic workflows.

Yet, behind the impressive adoption numbers lies a more complex reality: While many organizations are experimenting with GenAI, only a tiny fraction has reached the level of maturity needed to drive measurable ROI or sustainable transformation. The real differentiator is not experimentation—it’s execution.

What Business Functions Are Adopting AI the Fastest?

Generative AI is not being uniformly adopted across the enterprise. Instead, it is taking hold in functions where text, data, and repetitive processes dominate—and where the potential for productivity gains is both immediate and measurable. The McKinsey 2025 survey respondents found that marketing and sales use cases were the most common, followed by product and service development and IT functions.

Here is the full breakdown of how businesses are integrating AI into their different business functions when asked which functions are regularly using GenAI:

• Marketing & Sales: 34%
• Product & Service Development: 23%
• IT: 17%
• Service Ops: 16%
• Software Engineering: 13%
• HR: 12%
• Compliance, Risk, & Governance: 8%
• Corporate Finance: 7%
• Supply Chain & Inventory Management: 6%
• Manufacturing: 4%

Functional deep dive: Top GenAI use cases

Here is a closer look at how GenAI is applied within specific business functions—highlighting the enterprise’s most common and high-impact use cases.

• Marketing & Sales: Content creation (16%), personalized marketing (15%), and sales lead identification (8%).
• Product & Service Development: Design development (10%), scientific research and review (6%), and accelerated early testing (6%).
• IT: Help desk chatbot (7%), data management (7%), help desk AI assistant (6%).

What’s driving function-specific adoption?

These user adoption patterns are shaped by a mix of operational needs, resource constraints, and cultural readiness within each function:

• High-volume content workflows benefit from GenAI’s ability to automate drafting, summarization, and personalization.
• Functions under cost or talent pressure (like IT and HR) prioritize GenAI to do more with fewer resources.
• Digital-native teams (e.g., product and engineering) are more comfortable experimenting with emerging tech.

As GenAI capabilities expand—from copilots to autonomous agents—we’re seeing use cases multiply in less traditional areas, such as compliance, finance, and supply chain.

Factors Impacting AI Adoption

Generative AI adoption is accelerating—but not without user friction. Despite growing investment and experimentation, most organizations still grapple with moving from isolated pilots to scaled impact. Some of the most significant barriers are operational; others are deeply human.

Below are the most common factors slowing down GenAI adoption—viewed through either the business lens of strategic execution or the employee lens of lived workplace experience.

• Failed implementations and lack of ROI: 80% of AI implementation projects fail—nearly twice the rate of other software implementation projects. These failures are rarely due to the technology itself. Instead, they typically stem from foundational weaknesses: Unclear problem definitions, absence of KPIs, and a lack of scalable infrastructure. To succeed, GenAI must be treated not as a plug-in but as a shift in the operating model—one that requires process maturity and business alignment from the outset.
• Lack of documented use cases: Without documented and shared use cases, teams often operate in silos—leading to duplicated efforts, inefficiencies, and missed opportunities. Organizations can avoid reinventing the wheel and intelligently scale what works when GenAI wins are codified and circulated across departments. A good way to encourage cross-functional knowledge sharing is to develop an internal GenAI use case library or playbook.
• Reskilling on an entirely new technology:  For many employees, GenAI is unlike any tool they’ve used before. The initial learning curve isn’t just technical—it’s cognitive and behavioral. This steep adjustment is one of the root causes of many AI implementation projects failing. Without accessible upskilling, employees may feel overwhelmed or excluded from using the new technology.
• Guidance on when and when not to use AI: Workplace surveys show that 40% of employees lack awareness of what GenAI tools can do—or how they apply to their roles. This lack of guidance leads to underutilization or misapplication of available tools, resulting in missed productivity gains and confusion.
• Organizational culture and workforce demographics: GenAI adoption often mirrors organizational culture. Companies led by younger, more digitally fluent executives are more likely to explore, experiment with, and embed GenAI into daily operations. Startups and scale-ups—where process innovation is encouraged—are typically more agile and better positioned to restructure workflows for emerging technology. Research from MIT Sloan confirms that firms with digital-native leadership teams are more proactive in their AI strategies and faster to operationalize new tools.
• Resistance to change: The speed at which GenAI tools evolve can be disorienting. 50% of employees say they feel overwhelmed by the rate of AI-related updates. When tools change frequently—without clear user training or support—what should feel like empowerment starts to feel like disruption. Sustainable adoption requires consistency, psychological safety, and communication that frames change as an opportunity—not a threat.

What Industries Are Leading the AI Adoption Race?

Despite the headlines, GenAI adoption is far from being evenly distributed across sectors adoption-wise. While some industries are scaling use cases and reshaping their operating models, others are still in the exploratory stage—testing tools, assessing ROI, or grappling with talent and regulatory hurdles.

Understanding the AI adoption landscape helps to compare where different sectors stand today. Here is a comparative snapshot of adoption trends across key industries:

What sectors are early adopters of AI?

Industries like technology, media, finance, and professional services are leading the GenAI adoption curve. These sectors share a common foundation that accelerates their AI maturity:

• Deep digital infrastructure and cloud-native systems.
• High volumes of structured, accessible data.
• Work cultures that reward experimentation.
• Established pipelines of AI-literate talent.

For example, media companies embed GenAI in content production pipelines to automate editing and personalization. Professional services firms leverage AI for automated research, proposal generation, and workflow orchestration. Financial institutions are fine-tuning GenAI models to support regulatory compliance, customer insights, and fraud detection—often within secure, private environments.

These sectors consistently report above-average ROI and faster time-to-value from GenAI pilots, proving that digital maturity and strategic alignment drive scalable outcomes.

What sectors are AI technology laggards?

Sectors like construction, agriculture, the public sector, and healthcare remain in the early stages of GenAI exploration. The barriers here are often systemic and structural, including:

• Manual, paper-based workflows that resist automation.
• Legacy IT systems that limit integration and scalability.
• Regulatory restrictions that reduce flexibility for experimentation.
• Risk aversion cultures and long procurement cycles.

Still, pockets of innovation are emerging. Public health departments are piloting AI-powered triage and documentation tools. Some construction firms are testing AI for site planning, work coordination, and safety audits.

Industry-wide maturity is not a prerequisite for GenAI success. Organizations that invest early in digital infrastructure, workforce enablement, and AI governance can leapfrog peers—even in traditionally slow-moving sectors.

Breaking Down AI Adoption by Sector

While GenAI adoption is gaining traction across industries, implementation’s rate, maturity, and focus vary significantly. Sector-specific dynamics (from regulatory constraints to digital fluency) play a pivotal role in determining how deeply and rapidly GenAI takes hold.

This section examines ten major sectors, breaking down where they stand in their GenAI journey.

Remember – AI adoption isn’t uniform—even within the same sector, digital maturity varies widely. The most advanced organizations are often those that treat GenAI as a strategic enabler, not just a tech tool.

Banking and financial services

• Adoption status: Advanced.
• Top use cases: Fraud detection, risk modeling, AI-powered financial advisors, and customer service chatbots.
• Adoption enablers: Robust data infrastructure, analytics maturity, existing AI literacy.
• Primary barriers: High regulatory security, explainability requirements, and data privacy concerns.

Financial institutions are embedding GenAI into compliance, risk, and customer experience workflows. The sector’s long-standing data infrastructure and appetite for automation enable rapid scaling. However, trust and auditability remain core hurdles.

Thanks to decades of investment in digital infrastructure, rich datasets, and a competitive push for automation, the banking and financial services sector is among the most mature regarding AI adoption. AI is already delivering measurable value across key business functions. In fraud detection, machine learning algorithms can identify suspicious transactions in real time, dramatically reducing financial loss and improving response times. AI-powered risk models offer more precise credit scoring and market forecasting. At the same time, generative AI transforms customer engagement through hyper-personalized virtual assistants and advisory tools that simulate human interactions at scale.

Despite this momentum, implementation challenges remain. Financial institutions operate in some of the world’s most tightly regulated environments, where explainability and transparency of AI models are not just ideal — they’re legally required. This creates friction when adopting black-box models, especially in use cases like credit underwriting or compliance monitoring. To overcome this, institutions invest in “glass box” AI systems and explainable AI (XAI) frameworks that provide clear reasoning behind algorithmic decisions. Additionally, to manage sensitive data securely, many are shifting toward federated learning and other privacy-preserving techniques that allow model training without compromising customer data.

In the long term, AI offers financial services firms a path toward deeper efficiency, better risk mitigation, and new product innovation. Those that succeed will pair technical advancements with strong AI governance practices, cross-functional collaboration between data scientists and risk/compliance teams, and continuous upskilling across the workforce. As regulators offer clearer guidance on responsible AI use, expect adoption to accelerate — particularly in wealth management, regulatory compliance, and back-office operations where automation can significantly reduce overhead.

Education

• Adoption status: Emerging.
• Top use cases: Intelligent tutoring, content personalization, and administrative automation.
• Adoption enablers: Growing demand for personalized learning and interest in automation.
• Primary barriers: Budget limitations, fragmented IT infrastructure,  and need for pedagogical oversight.

AI adoption in education is beginning to take shape, especially in areas like adaptive learning platforms, intelligent tutoring systems, and personalized content delivery. These tools promise to close learning gaps by meeting students where they are — providing real-time feedback, differentiated instruction, and more inclusive support for neurodiverse learners. GenAI is also streamlining back-office tasks like grading, enrollment, and communication with students and parents, freeing educators to focus on teaching.

Still, adoption is uneven, especially among underfunded districts and higher education institutions facing tight budgets and legacy systems. Pedagogical oversight is also critical: AI-generated content must align with educational goals and standards, and instructors must remain central to the learning experience. To successfully implement AI, institutions need to upskill educators, administrators, and IT teams to use new tools and evaluate their efficacy and fairness. Additionally, student-facing applications must include clear, in-context guidance to ensure responsible and effective use.

In the long term, AI has the potential to transform learning outcomes and improve institutional efficiency, but it requires thoughtful investment in both technology and human capital. Forward-thinking institutions are beginning to integrate AI literacy into teacher training programs, create governance frameworks for responsible use, and explore how GenAI can support, rather than replace, traditional instruction.

Government and public sector

• Adoption status: Early.
• Top use cases: Document summarization, permit processing, and virtual agents for citizen services.
• Adoption enablers: High-volume documentation needs and long-standing digitization mandates.
• Primary barriers: Bureaucratic procurement cycles, regulatory constraints, and aging infrastructure.

Governments are cautiously piloting AI and GenAI to streamline paperwork-heavy processes, such as permit approvals, records management, and benefits administration. Intelligent virtual agents are emerging as valuable tools to improve citizen engagement, reduce wait times, and make services more accessible. AI can also assist with legislative research, policy drafting, and summarizing public comments—dramatically reducing manual effort and accelerating workflows.

However, the public sector faces unique hurdles. Procurement cycles are lengthy and risk-averse, often stalling innovation. Legacy infrastructure and siloed data systems make integration difficult, and any AI deployment must be evaluated through public accountability and transparency. These constraints demand rigorous governance and careful model selection to avoid bias, hallucinations, or misuse of citizen data.

To scale AI use responsibly, governments must invest in training their workforce — from frontline workers to policymakers — on how to interpret, apply, and audit AI outputs. To prevent confusion or misinformation, citizen-facing services must also contain built-in explanations and guardrails. Successful adoption hinges on change management: educating employees and the public, establishing trust in AI-driven systems, and demonstrating tangible improvements in service delivery.

Healthcare

• Adoption status: Early.
• Top use cases: Clinical note summarization, diagnostic decision support, and patient triage.
• Adoption enablers: Data-rich environments and potential for life-saving applications.
• Primary barriers: Regulatory compliance, risk of AI hallucination or bias, and data interoperability.

AI in healthcare holds enormous promise — from streamlining administrative workflows to supporting more accurate, data-informed diagnoses. GenAI tools can assist clinicians by summarizing medical records, generating SOAP notes, and flagging anomalies, enabling providers to spend more time with patients and less on paperwork. AI-assisted triage tools also improve how hospitals prioritize care, reducing wait times and improving patient outcomes.

Yet the stakes are high. Errors, biases, or misinterpretations in clinical contexts can have life-threatening consequences. Regulatory bodies demand rigorous validation, and many AI systems struggle with interoperability across EHR platforms. Healthcare organizations must also tread carefully with patient data, adhering to strict privacy laws like HIPAA while ensuring the ethical use of AI outputs.

Overcoming these challenges requires deep collaboration between clinicians, data scientists, and compliance teams — and a sustained investment in upskilling. Physicians and staff need to understand how AI systems work, when to trust them, and when to intervene. Equally important is patient education: AI-powered chatbots or virtual assistants must be equipped with clear, digestible information and escalation pathways to human care. The goal isn’t to replace providers but to augment their work with trustworthy, explainable AI support.

Hospitality

• Adoption status: Lagging.
• Top use cases: Guest personalization, AI-driven concierge bots, and operational optimization.
• Adoption enablers: Demand for personalized, self-service experiences.
• Primary barriers: Underinvestment in IT, fragmented systems, and lack of internal AI skills.

The hospitality industry has a natural fit for AI — delivering personalized, seamless guest experiences at scale. GenAI tools can power conversational booking agents, real-time concierge bots, and tailored recommendations that make guests feel known and valued. Behind the scenes, AI can optimize staffing, inventory, and energy consumption, reducing operational costs and improving sustainability.

Despite this potential, many hospitality companies lag in adoption due to aging tech infrastructure and limited investment in digital transformation. Fragmented property management systems and a lack of in-house AI expertise make it difficult to deploy AI holistically. Moreover, inconsistent training and change management mean that even when AI tools are introduced, employees often lack the skills or confidence to use them effectively.

For AI to deliver real value, hospitality leaders must prioritize workforce enablement — from front desk agents to marketing teams. Staff should be trained not only in tool usage but also in interpreting AI outputs and understanding guest preferences. In parallel, guests must be guided through AI-enabled interactions with clear, human-friendly prompts. The hospitality sector can evolve toward smarter, more responsive service models by embedding AI literacy into onboarding and investing in user-friendly, integrated platforms.

Insurance

• Adoption status: Maturing.
• Top use cases: Claims processing, fraud detection, and chatbot support for policyholders.
• Adoption enablers: Rich historical datasets and process automation opportunities.
• Primary barriers: Data privacy, regulatory compliance and model governance, and trust in AI outcomes.

AI is already reshaping the insurance sector, especially in high-volume, rules-based areas like claims triage, underwriting, and fraud detection. Generative AI takes this further by generating responses for customer support queries, explaining policy terms, and producing personalized customer documentation. These efficiencies not only lower costs but improve the customer experience through faster resolution and proactive communication.

That said, AI must be implemented with precision. Insurers handle highly sensitive data and operate under strict regulatory oversight. Errors in claims approval or risk modeling can have costly implications and damage trust. As a result, the sector is focusing on explainable AI, robust audit trails, and well-defined governance frameworks to ensure transparency and accountability.

To scale adoption, insurance firms must invest in upskilling underwriters, claims agents, and customer support reps — using AI tools and working alongside them. Training on reviewing AI outputs, overriding decisions when necessary, and communicating those choices clearly to policyholders is essential. Likewise, policyholders engaging with AI-powered self-service portals need intuitive guidance and escalation paths to human agents. The result is a hybrid model where humans and AI collaborate to deliver more accurate, personalized, and efficient coverage.

Manufacturing

• Adoption status: Moderate.
• Top use cases: Quality inspection, supply chain optimization, and predictive maintenance.
• Adoption enablers: IoT data integration, cost-saving potential, and automation culture.
• Primary barriers: Data silos and infrastructure integration with legacy machines.

AI is driving improvements in uptime, quality, and productivity in manufacturing. Computer vision systems powered by AI can inspect products for defects in real time. Predictive maintenance algorithms anticipate equipment failures before they happen, reducing costly downtime. GenAI is also emerging in product design and documentation, rapidly generating SOPs, safety protocols, and equipment guides from existing inputs.

Still, widespread adoption is held back by legacy systems, fragmented data sources, and inconsistent digital maturity across sites. Many factories operate with decades-old equipment that doesn’t readily connect to modern AI systems. Data may exist in silos — on spreadsheets, disconnected systems, or paper-making model training and integration difficult.

Manufacturers must invest in workforce upskilling at all levels to bridge this gap. Line workers need to trust and interact with AI-powered machines; engineers must know how to maintain and fine-tune AI systems; and managers need training in data governance and AI-driven decision-making. Equipping end users — whether field technicians or customers — with interactive guidance and documentation generated or enhanced by AI is just as important. Those who build a digitally fluent workforce will be best positioned to compete in the era of smart manufacturing.

Pharma

• Adoption status: Growing.
• Top use cases: Research synthesis, regulatory submission drafting, and trial recruitment.
• Adoption enablers: High-value use cases, data-rich R&D environments.
• Primary barriers: Regulatory scrutiny, data quality issues, and AI talent shortages.

Pharmaceutical companies are quickly discovering GenAI’s advantages in accelerating time-to-market. AI can ingest vast volumes of research to synthesize insights, draft regulatory documents, and even predict molecule interactions. Clinical trial recruitment—historically slow and expensive—is being improved with AI tools that match participants to eligibility criteria and automate outreach, speeding up the process significantly.

Yet challenges persist. Regulatory bodies like the FDA require detailed documentation and explanation of AI-assisted research or submissions. Data quality and availability vary across R&D pipelines, and many pharma companies struggle to attract or retain the AI talent needed to operationalize GenAI tools at scale.

Success in this space will require more than just technology. Scientific teams must be trained in AI fundamentals and encouraged to integrate these tools into their workflows. Regulatory teams need clarity on how to validate and submit AI-generated content. Customers—from trial participants to medical professionals—must be supported with clear, AI-generated materials that simplify complex concepts without sacrificing accuracy. Cross-functional collaboration and strong compliance partnerships will be the key to unlocking AI’s full potential in pharma.

Real Estate

• Adoption status: Emerging.
• Top use cases: Property description generation, valuation modeling, and AI-powered customer service.
• Adoption enablers: Document-heavy workflow and competitive pressure.
• Primary barriers: Legacy CRM systems, fragmented tools, and low AI maturity.

AI is starting to make inroads in the real estate sector, particularly among tech-forward brokerages looking to stand out in a competitive market. Generative AI can draft property listings, assist with valuation modeling, and power customer service bots that respond to real-time inquiries. These tools free up agents to spend more time building relationships and closing deals.

However, many real estate firms operate on outdated CRM platforms and lack the integration to support end-to-end AI workflows. The industry also suffers from uneven digital literacy among agents and clients, making adoption more difficult. Privacy concerns around client financial data further complicate AI deployment, especially in larger firms with more rigorous compliance needs.

To unlock AI’s benefits, brokerages must provide targeted training for agents and admins on how to use AI tools ethically and effectively. AI-generated content should be reviewed and tailored to fit local markets and client expectations. Homebuyers and sellers interacting with AI touchpoints—whether chatbots or recommendation engines—need transparent experiences and easy paths to human support. In a people-driven industry like real estate, AI must be positioned as a helpful assistant, not a replacement.

Retail

• Adoption status: Maturing.
• Top use cases: Personalized product recommendations, inventory optimization, and automated marketing content.
• Adoption enablers: Rich consumer data and digital-first business models.
• Primary barriers: Integration with legacy systems and workforce training gaps.

Retail has emerged as one of the most enthusiastic adopters of GenAI, especially in e-commerce. AI powers highly personalized product recommendations, optimizes supply chains in real-time, and automates the creation of ad copy, product descriptions, and promotional content. These use cases help brands drive conversion, reduce costs, and maintain a competitive edge in an increasingly crowded marketplace.

However, legacy systems still hamper AI deployment in some omnichannel and brick-and-mortar retailers. Workforce training is another hurdle — store associates, marketers, and merchandisers need support to understand and trust AI-generated suggestions. Without that human-in-the-loop validation, AI outputs risk being ignored or misused.

To scale AI’s impact, retailers must integrate continuous learning programs into their operations. Employees should be trained to use AI tools not just as passive dashboards but as collaborative assistants. And customers engaging with AI — whether through personalized emails or virtual try-ons — should be offered clear guidance and opt-outs to preserve trust. When implemented with both user groups in mind, AI can drive revenue growth while deepening customer loyalty.

What Can You Do to Accelerate AI Adoption?

Even in organizations with strong tech stacks, GenAI adoption ultimately succeeds—or stalls—based on people. Upskilling, enablement, and cultural alignment are the foundation for enterprise-wide AI success.

Leaders can implement four actionable strategies to accelerate GenAI adoption across their organizations.

1. Take a people-first approach to AI adoption

GenAI isn’t just a technical upgrade—it’s a new way of working. Leaders must frame adoption as a partnership between people and machines, not a zero-sum replacement. That means engaging teams early, listening to concerns, and co-creating workflows that elevate (rather than sideline) human contributions.

Start with employee personas. Identify how different roles interact with AI tools and design targeted onboarding journeys. Tailoring support to job function drives faster learning and more sustained usage.

2. Showcase how AI improves the quality of work

Many employees remain skeptical of GenAI’s practical value. Demonstrating how it increases creativity, reduces the admin burden, or supports decision-making can be more persuasive than top-down mandates.

Don’t just tell—show. Capture before-and-after examples from employees who are early tech adopters and turn them into internal case studies. Seeing peers succeed with GenAI builds trust faster than executive directives.

3. Provide hands-on AI upskilling

Formal training is essential—but so is experiential learning. Workshops, sandbox environments, and peer-led demos help employees build confidence with GenAI tools and start using them meaningfully in their day-to-day roles.

Layer structured learning with low-stakes experimentation. Setup “AI labs” or sandbox environments where employees can test tools without fear of failure. Track participation and follow up with targeted tips to reinforce learning.

With a tool like Whatfix Mirror, organizations can build replica sandbox applications to provide hands-on employee training, without the fear of risking live software usage or interacting with real customers.

4. Support employees in the flow of work

Even the most intuitive GenAI tools need contextual guidance. Embedding support—like smart tips, walkthroughs, and just-in-time help—into everyday GenAI-infused platforms reduces friction and ensures adoption isn’t just attempted but sustained.

Don’t wait for employees to ask for help. Use in-app tooltips and behavior-triggered guidance to surface AI support exactly when users hit a roadblock.

Digital adoption platforms like Whatfix DAP make delivering this kind of just-in-time enablement easy without overwhelming your teams. With Whatfix DAP, organizations can embed contextual, role-based support in the flow of work, guiding employees at key friction points and helping them to maximize AI usage and drive business outcomes.

AI Adoption Clicks Better With Whatfix

Successfully adopting GenAI is about more than just choosing the right tools. It’s about enabling employees, evolving workflows, and creating an infrastructure for continuous learning. That’s where Whatfix becomes your strategic advantage.

Whatfix digital adoption platform (DAP) empowers enterprises to accelerate GenAI adoption through:

• In-app guidance and just-in-time support: Equip employees with contextual help and GenAI usage tips right inside the applications they already use.
• Experiential learning at scale: Create hands-on AI walkthroughs, interactive demos, and self-service training modules—segmented and tailored to each role, function, and workflow.
• Sandbox environments with Whatfix Mirror: Simulate GenAI applications in a real-world context without risking live data. Whatfix Mirror lets teams experiment safely, practice new workflows, and build GenAI fluency in a risk-free setting.
• Behavioral analysis and adoption insights: Understand how users engage with AI-powered tools (with Whatfix Analytics), identify friction points, and optimize enablement strategies with data-driven precision.
• Frictionless change management: Whether rolling out a new AI tool or embedding GenAI into existing systems, Whatfix helps you deliver smoother transitions and higher adoption rates without disrupting business continuity.

With Whatfix, you can move beyond experimentation and turn AI intent into AI impact. Support your workforce in the flow of work, empower teams to build AI fluency, and drive measurable outcomes from your digital transformation efforts.

Let’s reimagine work—together. See how Whatfix is driving AI adoption now!