Industry executives and experts share their predictions for 2026. Read them in this 18th annual VMblog.com series exclusive.
By Khadim Batti, Co-Founder & CEO, Whatfix
Digital transformation has accelerated beyond incremental software upgrades into a fundamental reshaping of how enterprises operate, make decisions, and compete for business. In this shift, AI has moved from a supporting feature to the primary execution layer of the enterprise, functioning less like an add-on and more like an operating system for work itself.
This acceleration has exposed critical gaps. Teams are struggling with AI saturation and adoption at the user level, while employees face mounting pressure to operate within increasingly complex digital environments where AI agents execute decisions, workflows, and exceptions in real time. The gap between technological capability and human readiness has never been wider, and that gap now directly constrains productivity, trust, and ROI.
As a result, enterprises are no longer deciding whether to adopt AI, but how to build the enabling infrastructure that allows people and AI to work together effectively. The organizations that will thrive recognize a fundamental truth: enterprise-wide AI success depends on systems that translate AI capability into confident human execution.
As AI becomes operational infrastructure, enablement systems are forced to evolve into operational infrastructure as well.
DAPs Will Become the Central Nervous Systems in Guiding AI Adoption
2025 revealed that the biggest barrier to AI and automation ROI was enterprise-wide adoption. With the exponential growth in AI tools available in the market, the struggle was driving its adoption. This catapulted the need for Digital Adoption Platforms (DAPs) in AI to create continuous, contextual guidance embedded directly into users’ workflows.
Enterprises are using DAP for rapid content creation, conversational support, and real-time behaviour insights, helping them keep pace with evolving applications and increasingly complex digital processes. This foundation set the stage for a major shift. In 2026, DAPs will become the cornerstone of digital transformation across the enterprise, focused on application as well as AI adoption and acceleration, as they move from guidance engines to intelligent orchestrators. With AI, they will be able to predict and resolve friction, automate routine actions, and collaborate with both employees and AI agents to optimize productivity across complex digital environments. Resulting in a stronger model of human-AI symbiosis inside the enterprise.
As enterprises continue to invest more in AI transformation, DAPs will become the central nervous systems that help them efficiently train employees, drive productivity with adoption, and uncover problem areas with intelligent analytics.
AI Strategies Will Shift Focus from Features to Operational Through Lines
AI enters 2026 as the operating system of the enterprise, shifting from peripheral use cases to the core layers that shape workflows, decisions, and strategy execution. The traditional stack anchored in applications, data, and cloud is giving way to a new architecture defined by context, agents, and orchestration.
AI now functions as the logic layer that interprets intent, automates multi-step processes across systems, and delivers outcomes without requiring employees to navigate individual applications. Work itself is transforming, from “click to complete” to “state intent and let the system execute, reshaping how enterprises define productivity, governance, and employee experience.
DAPs and enablement systems have become the connective tissue of this AI-native environment. Their role is expanding from guidance to intelligent regulation monitoring, AI-driven decisions, explaining system actions, reinforcing compliance, and building employee confidence as AI agents assume greater operational responsibility. The new success metric will no longer be tool deployment but how effectively humans and AI co-deliver outcomes.
Whatfix has already laid the groundwork for this shift with its portfolio of products and AI technology. ScreenSense, its proprietary AI engine, interprets user context and intent to deliver real-time guidance, knowledge, nudges, and automation directly in the flow of work. This intelligence powers the broader product suite: Digital Adoption accelerates proficiency, Product Analytics uncovers friction and adoption gaps, and Mirror introduces the world’s first GenAI-powered simulation and roleplay platform, enabling employees to practice both workflow execution and conversational scenarios in a safe, dynamically generated environment. Alongside this, Whatfix is building its AI-first product – Seek. Recently, Seek for Salesforce, an AI-native assistant, became the first AI to complete every Salesforce Trailhead Admin challenge. Capable of performing real admin and developer tasks, it provides unified, conversational search across tools and knowledge silos.
These innovations reflect Whatfix’s commitment to building enterprise-ready AI teammates that enhance productivity, governance, and ROI. The company is closing the gap between rapid technological change and human enablement, ensuring AI becomes usable, trusted, and outcome-driven for every employee. Whatfix is not just simplifying software; it is redefining how AI works for people.
Enterprises that succeed in 2026 will treat AI not as a feature, but as the operating fabric of their business. Systems will interpret, act, and learn continuously. Employees will collaborate with AI agents to deliver faster, more accurate outcomes. Leaders will focus on orchestrating human-AI symbiosis, embedding safeguards, and cultivating trust in autonomous decision-making. AI will become both invisible and indispensable, shaping a new era of measurable, adaptive, and intent-driven productivity.
Product Analytics Will Enable Empathetic Understanding of AI-driven Optimization
2026 marks the shift from descriptive dashboards to empathetic intelligence, where analytics systems no longer just track what users do; they understand the “why” behind their behavior. The role of analytics moves from measurement to meaning, interpreting behavior as insight into human experience.
A key shift is the consolidation of insights and action within a single platform, where the system not only surfaces understanding but also triggers the right interventions with a single command. Whatfix Product Analytics embodies this shift by translating raw interactions into intent, diagnosing friction in real time, and recommending precise in-app actions that accelerate adoption. It evolves from a reporting tool into an active intelligence layer that helps teams understand not just what happened, but what to do next, with a simple human command.
Analytics becomes the continuous learning layer of every product, sensing friction, reasoning about intent, and improving experiences without manual intervention. This evolution reframes data literacy into data empathy, the ability to treat patterns as human signals rather than abstract metrics. Product leaders will increasingly ask what their product is trying to tell them about user struggles, motivations, and emotional states. Empathetic interpretation ensures AI-driven optimization remains human-centered, maintaining a balance between efficiency and experience.
Product analytics and AI begin to converge into cognitive systems capable of reasoning and acting, not just reporting. The result is a more adaptive and intuitive generation of products, shaped as much by human understanding as by data.
Every Business Change Will Be Simulated Before Full-Scale Deployment
Simulation becomes essential in 2026 because enterprises can no longer afford to discover user friction, workflow errors, or process failures after go-live. Organizations are adopting a “simulate-before-ship” approach, meaning every new workflow or system change is rehearsed in a safe, risk-free environment before it is released to real employees or customers. This enables teams to learn how the work will happen, enhance guidance based on where users need support, and ensure employees are prepared from day one of the rollout. It is the enterprise equivalent of a flight simulator: practice first, then deploy with confidence.
This shift is driving a major market expansion. Simulation requirements are spreading beyond regulated industries into mainstream business apps, and Gartner expects simulations to plug directly into AI-powered, agentic workflows so that telemetry from practice environments feeds predictive models and DAP guidance.
As digital change accelerates, simulation-first enablement will move from niche to standard practice. By 2028, IDC forecasts 55% of G2000 technical training will be AI-personalized, cutting time-to-proficiency in half and making simulation the fastest, safest path to skill-building. DAP-linked simulations such as Whatfix Mirror will merge with predictive analytics to create continuous learning loops-predict ? simulate ? deploy ? measure-that surface friction early, reduce rollout risk, and strengthen user confidence. Enterprises should embed simulation directly into their change-management lifecycle and route telemetry back into DAP analytics to reduce rework before go-live.
Three macro shifts explain why simulation is rising so quickly. AI is forcing a permanent transition from one-time onboarding to continuous re-skilling, since employees now handle the complex exceptions AI cannot solve. Traditional IT sandboxes are being replaced by Simulation Layers, eliminating cost, security risk, and IT dependency by providing 24/7, zero-risk training environments. “Proficiency under pressure” is becoming the new gold standard, as organizations recognize that true readiness means performing complex workflows while navigating high-stress customer interactions.
Simulation is becoming indispensable because modern enterprises require certainty before change, mastery before deployment, and performance readiness before real-world exposure.
Digital Transformation Reframed
Digital transformation in 2026 will be defined not by new technology rollouts, but by how effectively enterprises integrate AI capability, human readiness, and execution into a single system of work. Transformation is shifting from isolated initiatives to an integrated operating model where AI, analytics, enablement, and simulation operate as a continuous loop.
As AI becomes the enterprise logic layer, DAPs translate intelligence into execution, analytics deliver empathetic understanding, and simulation validates change before deployment. Together, these systems turn digital transformation from a technology effort into a performance discipline, where advantage comes from orchestrating human-AI collaboration at scale.
