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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

The Latest

I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...

Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...

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

The Latest

I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...

Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...