- Enterprise AI is transitioning from standalone models to unified platforms that securely integrate with company data and existing workflows to solve real operational challenges
- The primary barrier to effective AI adoption is the lack of specific company context, highlighting the need for systems that understand unique business terminology and relationships
- Successful organizations are redesigning actual workflows with built-in governance and utilizing tools like Glean to turn siloed structured data into reliable and actionable insights
At Google Cloud Next 2026, Google made its direction clear: enterprise AI is moving beyond chat interfaces and standalone copilots toward a broader platform model built for agents, data access, security, and getting work done at scale.
That matters because the biggest constraint in enterprise AI has changed. For the last two years, much of the market focused on model quality, access, and new features. Those things still matter, but they’re no longer the main question facing most organizations. The harder challenge now is operational. Enterprise leaders need to know whether AI can:
- work securely across business systems
- understand company context
- support real workflows
- earn trust over time
Google’s announcements reflected that shift. New investments across its Gemini Enterprise Agent Platform, Gemini Enterprise app, Agentic Data Cloud, security capabilities, and tighter Workspace integrations point to a market moving toward more unified AI platforms rather than disconnected tools.
That’s where enterprise buying behavior is heading, too. Companies don’t need more standalone AI experiences that create new silos. They need systems that can connect models to company knowledge, structured data, permissions, and the tools employees already use.
For teams evaluating what comes next, the takeaway is straightforward: the race is no longer only about who has the strongest model. It’s about who can make AI useful inside the real complexity of the enterprise.
That shift validates what many enterprises are already learning firsthand. Lasting value doesn’t come from a model alone. It comes from grounding AI in trusted company context, embedding it into workflows people already rely on, and delivering outcomes teams can measure.
Why enterprise AI is becoming a platform decision
Google Cloud Next pointed to a broader market shift. Across Google’s announcements, customer examples, and partner sessions, four themes stood out.
1. The context gap is now the biggest barrier to useful AI
Many enterprise AI failures aren’t caused by weak models. They happen when AI lacks the context needed to operate inside a real business.
During his panel session at Next, Zubin Irani, VP of Partnerships at Glean, shared a simple example: even a term like “PR” can mean different things depending on the team, user, or workflow. For a marketer, it may mean press release. For an engineer, it may mean pull request. Without business context, the same request can lead to the wrong answer or the wrong action.
That’s why enterprise AI needs more than generic reasoning. It needs access to company knowledge, systems, relationships, conversations, and permissions. A meeting prep agent is only useful if it can pull current account history, recent decisions, relevant documents, and the latest customer context. A campaign workflow is only useful if it reflects how a team actually creates, reviews, and reuses content.
As models improve, this challenge becomes more visible. Intelligence alone isn’t enough. Context is what turns AI into something dependable at work.
2. Winning teams are redesigning workflows, not adding more tools
The organizations moving beyond pilot mode aren’t treating AI as a side experiment. They’re starting with workflows that are frequent, measurable, and high-friction, then rebuilding them with clear ownership, guardrails, and success metrics.
That’s an important shift for any enterprise team evaluating AI in 2026. Instead of asking where AI can be added, stronger teams are asking which workflows would benefit from better context, faster execution, and more consistent output.
Glean customer Databricks offers a strong example. Non-technical marketers used Glean to build an agent called Briefbot that generates roughly 80% of a campaign brief in about five minutes. What once took a senior marketer half a day became a faster review-and-edit process, while also creating reusable context for future work. The full webinar goes deeper into Briefbot and the other agents Databricks built using the same workflow-first approach.
That’s what practical enterprise AI looks like. It’s not another destination tool, but systems that improve how work already gets done.
3. Trust, governance, and adoption can’t be bolted on later
As AI takes on more meaningful work, trust becomes operational, not theoretical. If a system is helping with customer support, reviewing code, summarizing financial information, or preparing customer-facing materials, leaders need confidence that outputs meet the right quality bar and remain reliable over time.
That starts with governance built into the design. Permissions, auditability, controls, and evaluation frameworks need to be present from day one.
4. The market is moving toward platforms that can operationalize AI
Enterprises are looking beyond standalone copilots and narrow point solutions toward platforms that can unify models, data, workflows, governance, and user experience.
Once organizations move past experimentation, fragmented tools create more complexity than value. What buyers need are systems that can connect AI to structured data, company knowledge, existing permissions, and the applications employees already use.
You can see the same trend in Glean’s recent announcements.
Making structured data easier to use
Glean’s new Google BigQuery integration extends secure, grounded AI experiences into structured enterprise data. By connecting Glean to BigQuery through MCP, employees can ask questions in natural language, get answers grounded in trusted data, and take action without needing deep SQL expertise.
As Emrecan Dogan, Head of Product at Glean, put it, the integration gives “every employee, not just SQL and data experts, the ability to ask questions of their company’s data in plain language and act on what they find.” He called it “a great example of how interoperable AI can securely make enterprise data more accessible, more grounded and intuitive to work with,” helping people move “from finding answers to getting work done.”
From prompt to polished presentation
Glean also introduced its new slide generation capability that turns a conversation in Glean Assistant into a draft presentation teams can refine using approved company templates. The result is a fully editable .pptx file that can be uploaded into Google Slides, helping teams move from idea to first draft faster while keeping people in control of the final narrative.
Both announcements reflect the same larger direction — enterprise AI is becoming less about isolated prompts and more about helping people complete real work inside trusted systems.
Why AI deployments still fail inside organizations
Even as models improve and platforms mature, many enterprise AI initiatives still stall for a simpler reason: organizations aren’t ready to absorb change at scale. In her session at Google Cloud Next, Rebecca Hinds, Head of the Work AI Institute and Thought Leadership at Glean, highlighted several patterns leaders should pay attention to.
1. AI gets layered onto broken workflows
If ownership is unclear, handoffs are messy, or the process itself is inefficient, AI often accelerates confusion instead of fixing it.
The strongest deployments begin with workflow clarity. Teams need to understand how work moves today, where bottlenecks exist, which decisions need human review, and where automation can create real value.
2. The barrier is often organizational, not technical
Programs struggle when employees see AI as imposed, threatening, abstract, or disconnected from their real work.
That’s why frontline involvement matters. The people closest to the work usually know where friction lives, where exceptions happen, and where a workflow is most likely to break.
3. Teams aren’t given room to learn
AI fluency doesn’t happen through a launch announcement. It’s built through experimentation, iteration, feedback, and clearer judgment around where human oversight still matters.
The strongest organizations create space for teams to test, compare outputs, refine instructions, and share what works.
These takeaways were front-and-center during a Work AI Institute panel with Julia Dhar (Behavioral Scientist and Managing Director and Partner at BCG), Aishwarya Srinivasan (AI Developer Relations Lead at Nebius and AI Educator), and Diana Wu David (Director of Futures at ServiceNow and #1 Global Futurist). Together, they emphasized that organizations that treat AI adoption as an ongoing practice, not a one-time rollout, will move faster and capture more value.
What matters most now for enterprise AI buyers
The biggest story from Google Cloud Next wasn’t any single launch. It was what the launches, customer conversations, and partner discussions revealed about where the market is headed.
The new buying criteria
Enterprise AI is entering a more demanding phase. Buyers are no longer judging platforms only by model performance or feature lists. They’re asking harder questions:
- Can this system understand our business?
- Can it connect securely across our tools and data?
- Can it support real workflows, not just isolated prompts?
- Can we measure whether it’s improving outcomes?
- Can people trust it enough to use it in meaningful work?
That shift will reshape how companies evaluate AI investments over the next several years. Many standalone tools will struggle to prove lasting value if they can’t integrate deeply into the enterprise environments where real work happens. Platforms that combine intelligence with context, governance, and execution will be in a stronger position.
If your team is evaluating what it takes to operationalize AI across the business, now is the time to look beyond model access and ask what will actually make AI work inside your business.
Want to see what it takes to move from AI pilots to measurable impact? Get a demo of Glean.
