Unlocking Potential: AI's Impact on Software Adoption

Khadim Batti
Whatfix

DAP-er Things to Come: The Future of AI-Driven Software Adoption

Imagine a future where software, once a complex obstacle, becomes a natural extension of daily workflow — an intuitive, seamless experience that maximizes productivity and efficiency. This future is no longer a distant vision but a reality being crafted by the transformative power of Artificial Intelligence (AI). As AI advances, it is poised to redefine how we interact with technology, ushering in a new era of digital adoption that empowers enterprises to thrive.

The reality today is far from this ideal. Organizations are grappling with a deluge of software applications, each adding layers of complexity to the digital landscape. Employees, navigating a maze of applications and features, frequently encounter digital fatigue — a substantial barrier to business growth, innovation, and employee satisfaction.

According to Gartner Research, the average employee relies on 11 applications daily to perform their tasks. Over a third (36%) need proficiency with 11 to 25 applications, while power users (5%) manage 26 applications or more. This surge in application use contributes to "digital friction," with two-thirds (66%) of employees reporting moderate to high levels of friction while working with their software tools.

This complexity underlines a critical need for AI-driven solutions that not only streamline user experiences but also fuel digital transformation, reduce digital friction, and foster seamless, efficient workflows that propel businesses forward.

Reimagining DAP with AI

Digital Adoption Platforms (DAPs) have emerged as essential tools in helping users navigate complex software systems, offering in-app guidance, onboarding support, and ongoing assistance. By reducing the cognitive load of managing numerous applications, DAPs make software more accessible for employees across roles and departments.

The rise of AI is driving a transformative shift in the future of Digital Adoption Platforms (DAPs), paving the way for groundbreaking advancements in user experience. While GenAI agents and assistants promise to revolutionize user experiences, their successful adoption often hinges on effective onboarding and ongoing support. Organizations may introduce these tools without sufficient guidance, leaving employees unsure of how to leverage their full potential. AI-powered DAPs can bridge this gap by providing clear instructions, prompts and best practices for interacting with co-pilots and further extending the impact of each GenAI agent.

By empowering employees to understand and utilize these tools effectively, DAPs can significantly enhance user adoption and productivity. For example, generating reports on a CRM can be a tedious task and one that users often can get wrong, requiring repeated rework. When a user hovers over the "Reports" section of the CRM, a DAP can identify the user's role and their current context, and provide suggestions on the type of report the user may want to generate. One mouse click later, the DAP fires up the GenAI Assistant and feeds it the relevant prompt. The GenAI assistant then takes over and executes the requisite steps to deliver the report to the user. What would often take a user many minutes and rework is now reduced to mere seconds and a single click.

This is a simple illustration of how a DAP and GenAI assistant working in tandem can achieve higher user productivity for the organization while reducing user effort and digital friction.

AI-driven DAPs play a strategic role in digital transformation by aligning technology with business objectives, fostering a digital-first culture, and providing continuous data-driven insights. They enable employees to navigate software easily, ensuring that digital adoption efforts support key business outcomes, such as boosting supply chain agility to cut costs and meet market demands.

By adapting to individual user needs, DAPs encourage a culture of ongoing digital learning and readiness for new technology. With real-time analysis of user behavior and software usage, AI-powered DAPs highlight areas for improvement, allowing organizations to optimize user experiences, enhance feature adoption, and make informed workflow adjustments — ultimately increasing productivity and maximizing the value of digital tools.

The Future of Software Adoption

The journey toward a seamless, intuitive digital future has already begun. With AI transforming DAPs into intelligent, adaptive tools, we are closer than ever to a reality where software serves as an enabler, free from the complexities that hold businesses back. By empowering users with predictive guidance, automating tedious workflows, and offering real-time insights, AI-powered DAPs are bridging the gap between the technology we have today and the vision we aspire to achieve. This is more than just a transformation; it's a pivotal shift toward an empowered, productive workforce, driving innovation and growth on the path to an AI-enhanced digital landscape.

Khadim Batti is Co-founder and CEO of Whatfix

Related Links

Hot Topics

The Latest

Closing the Gap in Modern Tech and the Tools Meant to Monitor Them

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

MEAN TIME TO INSIGHT Podcast - Episode 23: NetOps Labor Shortage

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Why FinOps Rewrote Its Mission and What It Signals for Technology Management

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Clearing the Path to AI: Why Vendor Consolidation Matters Now

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Cold Data, Hot Problem: Why AI Is Rewriting Enterprise Storage Strategy

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

Observability at a Crossroads: AI, Economics, Complexity and the Enduring Power of Open Source

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

Explainability Is the New Battleground in AI-Powered Observability

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

Empowering the Human Side of the Autonomous IT Age

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI Scale Is Outpacing Infrastructure - and IT Leaders Are Running Out of Time

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

No Escalations ≠ No Work: Why Visibility in DevOps Matters More Now That AI Is Accelerating Everything

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...

Unlocking Potential: AI's Impact on Software Adoption

Khadim Batti
Whatfix

DAP-er Things to Come: The Future of AI-Driven Software Adoption

Imagine a future where software, once a complex obstacle, becomes a natural extension of daily workflow — an intuitive, seamless experience that maximizes productivity and efficiency. This future is no longer a distant vision but a reality being crafted by the transformative power of Artificial Intelligence (AI). As AI advances, it is poised to redefine how we interact with technology, ushering in a new era of digital adoption that empowers enterprises to thrive.

The reality today is far from this ideal. Organizations are grappling with a deluge of software applications, each adding layers of complexity to the digital landscape. Employees, navigating a maze of applications and features, frequently encounter digital fatigue — a substantial barrier to business growth, innovation, and employee satisfaction.

According to Gartner Research, the average employee relies on 11 applications daily to perform their tasks. Over a third (36%) need proficiency with 11 to 25 applications, while power users (5%) manage 26 applications or more. This surge in application use contributes to "digital friction," with two-thirds (66%) of employees reporting moderate to high levels of friction while working with their software tools.

This complexity underlines a critical need for AI-driven solutions that not only streamline user experiences but also fuel digital transformation, reduce digital friction, and foster seamless, efficient workflows that propel businesses forward.

Reimagining DAP with AI

Digital Adoption Platforms (DAPs) have emerged as essential tools in helping users navigate complex software systems, offering in-app guidance, onboarding support, and ongoing assistance. By reducing the cognitive load of managing numerous applications, DAPs make software more accessible for employees across roles and departments.

The rise of AI is driving a transformative shift in the future of Digital Adoption Platforms (DAPs), paving the way for groundbreaking advancements in user experience. While GenAI agents and assistants promise to revolutionize user experiences, their successful adoption often hinges on effective onboarding and ongoing support. Organizations may introduce these tools without sufficient guidance, leaving employees unsure of how to leverage their full potential. AI-powered DAPs can bridge this gap by providing clear instructions, prompts and best practices for interacting with co-pilots and further extending the impact of each GenAI agent.

By empowering employees to understand and utilize these tools effectively, DAPs can significantly enhance user adoption and productivity. For example, generating reports on a CRM can be a tedious task and one that users often can get wrong, requiring repeated rework. When a user hovers over the "Reports" section of the CRM, a DAP can identify the user's role and their current context, and provide suggestions on the type of report the user may want to generate. One mouse click later, the DAP fires up the GenAI Assistant and feeds it the relevant prompt. The GenAI assistant then takes over and executes the requisite steps to deliver the report to the user. What would often take a user many minutes and rework is now reduced to mere seconds and a single click.

This is a simple illustration of how a DAP and GenAI assistant working in tandem can achieve higher user productivity for the organization while reducing user effort and digital friction.

AI-driven DAPs play a strategic role in digital transformation by aligning technology with business objectives, fostering a digital-first culture, and providing continuous data-driven insights. They enable employees to navigate software easily, ensuring that digital adoption efforts support key business outcomes, such as boosting supply chain agility to cut costs and meet market demands.

By adapting to individual user needs, DAPs encourage a culture of ongoing digital learning and readiness for new technology. With real-time analysis of user behavior and software usage, AI-powered DAPs highlight areas for improvement, allowing organizations to optimize user experiences, enhance feature adoption, and make informed workflow adjustments — ultimately increasing productivity and maximizing the value of digital tools.

The Future of Software Adoption

The journey toward a seamless, intuitive digital future has already begun. With AI transforming DAPs into intelligent, adaptive tools, we are closer than ever to a reality where software serves as an enabler, free from the complexities that hold businesses back. By empowering users with predictive guidance, automating tedious workflows, and offering real-time insights, AI-powered DAPs are bridging the gap between the technology we have today and the vision we aspire to achieve. This is more than just a transformation; it's a pivotal shift toward an empowered, productive workforce, driving innovation and growth on the path to an AI-enhanced digital landscape.

Khadim Batti is Co-founder and CEO of Whatfix

Related Links

Hot Topics

The Latest

Closing the Gap in Modern Tech and the Tools Meant to Monitor Them

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

MEAN TIME TO INSIGHT Podcast - Episode 23: NetOps Labor Shortage

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Why FinOps Rewrote Its Mission and What It Signals for Technology Management

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Clearing the Path to AI: Why Vendor Consolidation Matters Now

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Cold Data, Hot Problem: Why AI Is Rewriting Enterprise Storage Strategy

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

Observability at a Crossroads: AI, Economics, Complexity and the Enduring Power of Open Source

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

Explainability Is the New Battleground in AI-Powered Observability

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

Empowering the Human Side of the Autonomous IT Age

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI Scale Is Outpacing Infrastructure - and IT Leaders Are Running Out of Time

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

No Escalations ≠ No Work: Why Visibility in DevOps Matters More Now That AI Is Accelerating Everything

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...