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The Rise of Work AI and Why It Requires a Platform Mindset
Jun 18, 2026 • 5 Minute Read • Tracey Barber, VP, Corporate Marketing
Organizations have spent the last several years experimenting with AI. The challenge now is turning that experimentation into operational value.
As AI adoption matures, the conversation is shifting away from standalone assistants and isolated use cases toward a broader question: how can organizations connect knowledge, workflows, automation, and decision-making across the business?
That shift is driving interest in a new category often referred to as Work AI.
While the market is still evolving, the direction is becoming clearer. Analysts increasingly point to the importance of AI-powered knowledge management, enterprise search, workflow automation, and agentic systems that help employees find information, generate outputs, and complete work within the organizational context.
The strongest Work AI platforms bring these capabilities together into a unified layer that helps people access knowledge, automate routine tasks, and act with greater context across the systems they use every day.
That's why Work AI should be evaluated as a platform decision, not simply another AI tool.
What Work AI Means
At its best, a Work AI platform combines enterprise search, knowledge management, workplace automation, and agentic orchestration into a single layer that helps people work with more context and less switching.
In practical terms, that means a good Work AI platform should help teams:
- Find relevant information across systems quickly
- Understand how content, people, and projects relate to one another
- Build enterprise context graphs that connect people, content, projects, workflows, and systems, creating a learning loop that improves relevance and agent effectiveness over time
- Generate useful outputs grounded in company context
- Automate repeatable workflows
- Create agents that support role-specific work
That's a fundamentally different proposition from adding a lightweight AI feature to an existing app, which is why Work AI should be treated as a platform decision.
What the Market is Converging Toward
As companies experiment with AI, the pattern is often uneven.
A few power users create momentum, while everyone else is left piecing together disconnected tools, unclear guidance, and limited context. That leads to fragmentation, duplicated effort, and a lot of activity that feels promising but never quite becomes operational value.
The organizations making real progress are moving beyond ad hoc usage and standardizing parts of the stack, defining guardrails, building repeatable workflows, and connecting adoption to measurable outcomes.
In other words, they're shifting from tool usage to operating model design.
At the same time, leading platforms are moving beyond simple search and retrieval toward building enterprise context graphs that connect information, relationships, and work patterns across the organization. This accumulated context becomes a strategic asset, helping AI systems deliver more relevant and personalized experiences over time.
5 Elements for a Successful Work AI Rollout
Organizations often focus on selecting the right AI platform. In practice, implementation matters just as much as technology.
The organizations seeing the greatest returns from AI are embedding it into existing workflows, aligning it with business processes, and creating the governance structures needed to support adoption at scale.
Successful Work AI rollouts typically include a few common elements:
1. A Real Use Case Pipeline
Before rollout, teams should identify where knowledge retrieval, internal support, workflow acceleration, or agent-driven automation can create immediate value. That work is much easier when it is grounded in actual department needs instead of abstract brainstorming.
2. Phased Technical Onboarding
Implementation needs to address source systems, identity alignment, permissions, QA, and data sensitivity. If a platform does not reflect how access and content work in the real business, trust breaks quickly.
3. Champions and Enablement
Adoption tends to grow faster when local champions can model how the platform helps in real workflows. Training also needs to match the audience, from foundational onboarding to more advanced builder and administrator paths.
4. Governance that Scales
As agents and workflows multiply, moderation, review, and guardrails become more important, not less. Governance is what allows organizations to scale adoption without creating noise, confusion, or unnecessary risk.
5. Measurement Beyond Logins
Usage matters, but usage alone is not enough. Organizations should also look at time saved, workflow adoption, agent utilization, and impact on operational KPIs over time.
5 Evaluation Questions
A useful evaluation framework should go beyond surface-level demos and ask harder questions.
1. Can it connect to the systems that matter most?
The right platform should fit the real information architecture of the business, not an idealized one. That includes tools like knowledge hubs, collaboration platforms, CRM, ticketing, file storage, and communication systems.
2. Does it preserve permissions and support governance?
Security, SSO alignment, permission-aware access, and sensitivity controls are foundational. Without them, scale becomes a liability.
3. Can it support both search and action?
A search box is helpful. A search and action layer is more transformative. The strongest platforms help teams not only find information, but also turn it into output, workflows, and approved next steps.
4. Can non-technical teams participate?
If agent creation and workflow setup are limited to highly technical users, adoption will bottleneck. Work AI should support both power builders and non-technical contributors who understand the process pain points best.
5. Does it get smarter as your organization uses it?
Many platforms can connect systems and retrieve information. Fewer can build and continuously improve an enterprise context graph that captures relationships between people, content, projects, workflows, and organizational knowledge.
The platform should create a learning loop where interactions improve relevance, recommendations, workflow automation, and agent effectiveness over time.
The bottom line is to evaluate not only what a platform can do on day one, but how its understanding of the business evolves as adoption grows. Those insights help leaders make informed decisions about scale, governance, and continued investment.
From Strategy to Execution
Organizations evaluating Work AI platforms will encounter a growing ecosystem of vendors that address different challenges.
Platforms like Glean, along with others in the enterprise AI and knowledge management space, reflect the industry's movement toward unified experiences that combine search, context, automation, and agent capabilities. The broader landscape also includes players such as Claude from Anthropic, which are helping shape enterprise expectations around AI agents, reusable skills, and more coworker-like ways of working.
The most important question is whether the platform can support how people work while providing the governance, security, and scalability required for enterprise adoption.
We decided to take this approach ourselves. As part of our own AI strategy, Verndale implemented Glean internally to unify knowledge and support AI-powered workflows across the business. See how we approached the rollout in our Glean case study.
Your Opportunity: Go Beyond AI Tools
Work AI gives companies a chance to reduce time spent searching, improve reuse of knowledge, onboard more effectively, and create more consistent ways of working across teams. But that only happens when the implementation is tied to real workflows, supported by governance, and treated as part of a broader operating model rather than an isolated AI pilot.
That's the opportunity in front of enterprise teams right now. Not just to add AI to work, but to redesign how work gets done.
Ready to evaluate Work AI for your organization? Start with a conversation about your workflows, knowledge ecosystem, and AI goals.
This article was developed in partnership with Glean. Verndale uses Glean as its Work AI platform to help employees access knowledge, automate workflows, and work more effectively across systems.