You have spent your career building enterprise software products for evolving markets. What made launching Aura different, and what did it reveal about where customer expectations for AI in enterprise software are headed?
Most enterprise product launches I have been part of were about proving a category: convincing customers that a new tool deserved a place in their technology stack. Aura was different because our customers were not asking for another AI tool. They were asking us to fundamentally rethink learning for the future of work.
That changed the entire design philosophy. Instead of building another application that sits alongside the systems where work happens, we built Aura to operate within them, grounded in each customer’s own content, data, and business context. The goal was to make learning part of the workflow, where a learning interaction and a business outcome become part of the same data trail instead of two separate reports someone reconciles after the fact.
That experience made one thing clear: customer expectations for AI have fundamentally shifted. Enterprise buyers are no longer evaluating AI as a feature layered on top of existing software. They are evaluating it against the operational metrics that run their business, such as ramp time, win rates, productivity, retention, and customer outcomes. They expect AI to improve those metrics, not simply generate more activity.
At the same time, employees are being asked to do more with broader responsibilities and leaner teams than ever before. AI in enterprise software has to help them perform better in the flow of work, not ask them to adopt yet another destination. That was the standard Aura had to meet before we considered it ready for beta, and I believe it is the standard the rest of the enterprise software industry will soon be measured against.
Absorb is positioning learning as a business performance system rather than just an LMS. What shift are you seeing in how enterprises now view learning's role inside the organization?
The biggest shift is that learning is no longer viewed as a content library. It is becoming operational infrastructure, woven directly into how work gets done. For years, the LMS was treated primarily as a compliance system with a course catalog attached. That model is breaking down under three converging forces.
The first is the pace of skills change. The World Economic Forum projects that 39% of today’s workforce skills will be transformed or become outdated by 2030, while 87% of organizations already report existing or anticipated skills gaps.
The second, and perhaps the most underappreciated, is the impact of AI on how work is organized. As AI increases productivity, people are expected to take on broader responsibilities, managers oversee larger teams, and entirely new roles emerge. Traditional models of learning through apprenticeship, shadowing, or a manager with time to coach simply do not scale. Increasingly, leaders, managers, and frontline employees are all learning at the same time, making continuous, AI-enabled learning a business necessity rather than an HR initiative.
The third is ownership. The most forward-looking organizations are shifting from HR-led training to business-led learning, with HR serving as the strategic enabler rather than the sole operator.
Together, these shifts change the question enterprises are asking. It is no longer, "Did employees complete the training?" It is, "Did learning improve performance, and can we prove it?"
That is why we built Aura to connect learning directly to skill development and measurable business outcomes, rather than stopping at course completion. Completion is simply a receipt. Business performance is the outcome that matters.
The market is flooded with disconnected AI tools and copilots. What gaps in that fragmented approach pushed Absorb to build a coordinated system of specialized AI agents?
Most AI-powered learning solutions today fall into one of two categories: a single chatbot bolted onto an LMS or a collection of disconnected AI tools that do not share context. One handles search, another creates content, another answers questions, and none of them knows what the others know. The result is a fragmented experience that limits AI’s ability to drive meaningful outcomes.
Three gaps pushed us to take a different approach.
The first is context. AI agents that cannot share context cannot build on each other’s work, so every interaction starts from scratch and the learner is left stitching the experience together.
The second is personalization. Learning is not a single interaction. It is a continuous journey of identifying skill gaps, recommending the right interventions, adapting to individual learning preferences, and measuring progress over time. That requires multiple specialized agents working together, not a single copilot answering isolated questions.
The third is governance. Standalone AI tools often encourage employees to move company knowledge into unmanaged environments, creating security, compliance, and shadow AI risks. Enterprises need AI that operates within their own data, permissions, and governance framework.
Aura’s answer is a coordinated system of specialized AI agents that share context, operate from a common customer-approved knowledge layer, respect the same permissions, and maintain complete tenant data isolation. Rather than a collection of disconnected copilots, we built an AI system where each agent has a specialized role but works as part of a coordinated whole, delivering a more intelligent, secure, and measurable learning experience.
Aura delivers knowledge inside workflows through Microsoft Teams, Chrome, and the learner experience itself. How does learning change when support becomes contextual and available exactly at the moment of need?
The research is clear: when employees need help, they turn to people, not learning systems. Nearly 75% seek out a colleague first because they need an answer that is immediate, relevant, and specific to the task at hand. In most organizations, the knowledge already exists. The challenge is making it available at the moment it is needed.
That is why Aura delivers support directly within tools like Microsoft Teams, Chrome, and the learner experience itself. Instead of interrupting their work to search a knowledge base, retake a course, or wait for a manager to respond, employees get the guidance they need without breaking their flow.
That fundamentally changes the role of learning. It is no longer a destination employees visit a few times a year. It becomes an always-on capability that helps people perform better every day.
It also reflects how adults actually learn. The most effective learning happens in context, through real work, conversations, and experience. Formal training remains important, but its greatest value comes when it is reinforced by timely guidance and practical application.
The future of learning is not about creating more places for employees to go. It is about bringing the right knowledge to them, in the right context, at exactly the right moment.
Enterprise buyers are hearing AI promises from every vendor right now. How important is it that what a company promises before the sale matches what customers actually experience after implementation, particularly with something as transformational as Aura?
It is absolutely critical. The more transformational the promise, the more important it is that customers realize value quickly and consistently. Enterprise buyers are hearing AI claims from every vendor. What differentiates successful companies is not the promise they make, but how reliably they deliver against it.
That starts with being clear about what AI can and cannot do. We focus on measurable business outcomes, not vague productivity claims. Whether the goal is reducing ramp time, improving sales performance, increasing learner engagement, or accelerating onboarding, customers need to understand what success looks like and how it will be measured.
It also requires tight alignment across the entire customer journey. Product, marketing, sales, customer success, and enablement all need to tell the same story and work toward the same outcomes. The value customers are sold should be the value they are enabled to achieve after implementation.
That philosophy is reflected in how we built Aura. Measurable outcomes are one of our core design principles, not just a marketing message. We validate success through customer results, continuously refine our approach based on what we learn, and ensure every team is accountable to the same definition of value.
Ultimately, pre-sale and post-sale alignment is not just good customer experience. It is what builds trust, drives adoption, and creates the long-term partnerships that enterprise software depends on.
During the Aura beta phase, what customer reactions or use cases most strongly validated Absorb's vision of connecting learning, skills, and operational performance into a continuous feedback loop?
The biggest validation was that customers evaluated Aura the way we hoped they would. They did not ask whether the AI could answer questions. They asked whether it could improve performance while meeting enterprise standards for security, governance, and reliability. That reinforced our belief that enterprise AI is ultimately judged by business outcomes, not demonstrations.
The beta results supported that vision. Across all skill areas, Aura delivered useful responses to 95% of learner queries and resolved 94% of sessions with a single response. That matters because trust in the flow of work is hard to earn. If employees have to ask twice or verify every answer, they quickly revert to interrupting a colleague or searching elsewhere.
Equally important, Aura achieved 100% enterprise-compliant responses across our governance and safety testing, giving customers confidence that they could deploy it responsibly at scale. On the administrator side, Admin Assist deflected 40–60% of routine support requests, freeing administrators to focus on higher-value work rather than repetitive tasks.
Together, those results validated our broader vision. Aura can identify a need, deliver the right intervention in the flow of work, and generate outcomes that organizations can measure using the metrics that already matter to their business. Activity tells you a system was used. Business impact tells you it worked.
Leading Cross-Functional Growth Strategy, What do you think is the biggest mindset shift enterprise leaders need to make if they want AI investments to translate into real workforce impact?
The biggest mindset shift is to stop thinking of AI as a way to help people do the same work faster, and start seeing it as the first technology that can deliver truly personalized learning at enterprise scale.
For decades, we’ve known how people learn best. Benjamin Bloom’s research on the “Two Sigma Problem” showed that students receiving one-to-one tutoring dramatically outperformed those in traditional classrooms. The challenge was never proving the model. It was that there were never enough teachers. Every innovation in learning, from classrooms to online courses to the LMS, traded personalization for scale.
AI changes that equation. For the first time, organizations can provide every employee with a personalized learning companion that understands their role, their skill gaps, their business context, and the work they’re trying to accomplish. Instead of waiting for scheduled training or searching through content, employees receive guidance tailored to them, in the moment they need it.
That matters now more than ever. As AI increases productivity, people are taking on broader responsibilities, managers are leading larger teams, and new skills are emerging faster than organizations can teach them through traditional approaches. The old model, where the most experienced person teaches everyone else, simply doesn’t scale.
The organizations that succeed will recognize that AI is not just another productivity tool. It is the first opportunity to make personalized development available to every employee, every day, in the flow of work.
Making that vision a reality requires two things. First, AI must be built on a strong foundation of clearly defined roles, skills, knowledge, and business context, because AI amplifies what already exists. Second, leaders need to bring their workforce along with transparency, clear expectations, and a phased approach to adoption.
For centuries, organizations have had to choose between scale and personalization in learning. AI is the first technology that makes both possible. I believe that will prove to be its most profound impact on the workforce.
