Digital adoption platform (DAP) providers have been at the forefront of integrating automation into workflows to streamline user effort and enhance software experiences. Today, many DAPs come equipped with native robotic process automation capabilities and are continuously developing more sophisticated automation use cases. Additionally, these providers are enthusiastic about incorporating AI into DAPs, offering copilots, builder assistants, and bots to drive personalization and contextualization. With the emergence of agentic AI, vendors are now exploring new use cases to further boost the performance of DAPs.

The Case For Agentic AI In DAPs

But what role do AI agents play within the context of DAPs? DAPs’ core use case is to improve the usage and adoption of software applications with personalized nudges and interactive support across journeys configured within the supported application. There, AI agents can automate processes or workflows that are designed to empower users — giving them more control, personalization, and “automated ease” — in how they interact with the supported applications and digital tools to perform their tasks. Agentic workflows, though still in their early stages, hold promise for enhancing platform capabilities by introducing both incremental and significant improvements to software experiences.

Individualized Learning Experiences

DAPs serve as guided learning and knowledge delivery tools that provide in-the-moment guidance, support, and engagement across systems such as customer relationship management, human capital management, and enterprise resource planning. These journeys and walkthroughs are typically configured for a user cohort that has similar adoption challenges. With AI agents, DAPs can now, with relative ease, configure and deliver:

  • Tailored learning paths. Tailoring the learning experience at a user level based on their role, experience, and past interactions with the application ensures that users are not overwhelmed with irrelevant information, making the learning process more efficient.
  • Adaptive support. This entails dynamically adjusting the support level provided based on the user’s proficiency and behavior patterns. This could mean offering more detailed guidance to a novice or streamlining prompts for an experienced user. Agentic workflows can also be programmed to integrate security best-practices training tailored to the user’s interactions with the platform.

Automated And Efficient Support

DAPs help deploy automation to improve user experience with reduced clicks for tasks, automated form fills, and automated service ticket management. Agentic frameworks give DAPs increased agility in discovering opportunities for automation and creating autonomous workflows for:

  • Task automation. This involves identifying repetitive tasks within workflows that can be fully or partially automated, thereby saving time for the user and allowing them to focus on more value-added activities.
  • Automated content management. Advanced DAPs have started to leverage generative AI to enable automated content creation by learning from user journeys and clicks. With agentic workflows, DAPs can configure AI assistants to generate, validate, and organize content for specific roles or modules within a content workflow, significantly reducing manual content management efforts.

Integration Management

The true power in DAPs is building journeys and walkthroughs that span different applications that are part of a workflow to enable continuous guidance and support for users navigating their systems of work. But most enterprises struggle to identify and build these connected journeys and lose momentum after the initial deployments. With AI agents, enterprises can vastly optimize the manual effort in solving the complexities of cross-application adoption management with:

  • Cross-platform support. AI agents can be leveraged to identify connected journeys and workflows during initial implementation to help build a roadmap for cross-application use cases and seamlessly scale from single-application use cases for digital adoption.
  • Integration and interoperability management. In environments where multiple digital tools need to work together seamlessly, AI agents can be leveraged to identify and resolve dependencies, as well as build specialized training sessions focused on the integration points and interoperability between different systems.

Security And Privacy Enforcement

Experimenting with AI requires enterprises to continuously build awareness of sound and ethical use of AI among user groups, particularly with bring-your-own-AI (BYOAI) frameworks. DAPs have begun helping enterprises do more with AI by improving user adoption of AI tools through contextual learning and guidance. With AI agents, they can also help establish robust governance policies to drive responsible usage of AI through:

  • User consent and shadow AI. Deploying AI agents that provide users with clear options regarding data collection, usage, and personalization improves enterprise trust in working with AI. DAP vendors have started to build agents to specifically identify and report on shadow AI usage and can now deploy agents to apply better controls and guardrails for BYOAI frameworks.
  • Bias mitigation. AI agents can help enterprises actively identify and reduce biases in AI algorithms and data sets to ensure fairness and inclusiveness in automated recommendations and decisions.

Deploying AI agents within DAPs represents a forward-thinking approach to enhancing digital adoption, user engagement, and overall productivity within organizations. With AI agents, DAPs can help organizations solve usage, adoption, and experience challenges with highly optimized human oversight.

If you’re a Forrester client, you can access The Forrester Wave™: Digital Adoption Platforms, Q4 2024. Visit my Forrester bio page and click “Follow” to receive notifications. You can also follow me on LinkedIn here. Forrester clients can also schedule an inquiry or guidance session with me to delve deeper into this topic.