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Nearly 90% of Fortune 100 companies now use Google's Gemini Enterprise — adoption that's become one of the biggest drivers behind Alphabet's cloud growth, with Google Cloud revenue up 82% year-over-year and its backlog reaching $514 billion in Q2 2026 alone.
That scale of adoption reflects a broader shift already underway. Fortune 500 companies are moving fast from AI assistants to AI agents, and Gartner predicts 40% of enterprise applications will feature task-specific AI agents by the end of 2026, up from less than 5% in 2025. Google's Gemini Enterprise is one of the platforms leading that shift — giving enterprises a way to build, orchestrate and govern agents that reason across information, connect to business systems and execute multi-step workflows on a company's behalf.
Signal AI intelligence can now become part of that agentic layer. Through Model Context Protocol (MCP), Signal AI connects directly into Gemini Enterprise, bringing external risk and reputation intelligence into the agentic workflows the world's largest organisations are building today.
What Fortune enterprises are solving for
Enterprise agents are only as good as what they can see. Gemini Enterprise already connects agents to the systems a business runs on — Google Workspace, core enterprise applications, internal data. That answers the question of what an organisation knows about itself.
It doesn't answer a different, harder question: what is changing outside the walls of the business, right now, that should change the decision an agent is helping to make?
For a Fortune 500 company, that outside world moves constantly and at scale a single team can't track manually:
- A regulator shifts its position in a market the business operates in.
- A geopolitical event opens up exposure somewhere in a global supply chain.
- A competitor makes a move that changes the competitive picture overnight.
- A narrative starts building momentum across global media before it reaches the boardroom.
- A reputational issue surfaces in one market days before it reaches another.
None of that lives in a CRM, an ERP, or a Workspace doc. It lives in millions of news articles, broadcast transcripts and social posts published every day, in every language, across every market a Fortune enterprise operates in. That is precisely the intelligence layer Signal AI has built: more than 5.5 million articles from premium, trusted sources are ingested and labelled by Signal AI every day — enriched with entities, topics, sentiment and significance — and it's what was missing from the agentic stack until now.
What connecting Signal AI to Gemini Enterprise actually changes
With Signal AI connected through MCP, a Gemini Enterprise agent can pull relevant external risk and reputation signals directly into its reasoning and workflows — not as a separate lookup a person runs afterward, but as part of the same agentic process that already understands the company's internal context.
In practice, that means different functions in many enterprises get a materially different starting point:
Risk teams get agents that can investigate emerging threats, track regulatory developments, and flag changes affecting key entities and markets — as part of the same workflow used to assess a deal, a market entry, or a third-party relationship.
Corporate Affairs and Reputation teams get agents that can surface changing narratives, stakeholder attention and issues developing across global media before they escalate — feeding directly into briefing and response workflows instead of a separate monitoring tool.
Executives get the potential for AI-assisted decisions that combine what the organisation knows about itself with what is changing around it, in the same workflow, at the speed agentic AI is designed to operate.
That is a meaningfully different proposition than plugging in another data source. It's giving the agent a second sense — an external one — to reason with alongside the internal knowledge Gemini Enterprise already gives it.
Part of a bigger shift in how enterprise AI gets its facts
Signal AI isn't alone in bringing trusted, external intelligence into Gemini Enterprise this way. Financial data providers including LSEG, FactSet, and Morningstar and PitchBook have already connected their own data into Gemini Enterprise through MCP, each grounding agents in a domain of external truth a business can't generate internally. The pattern is the same across all of them: enterprises want an open agentic ecosystem, not a walled one, and the value of any agent is capped by the quality of the intelligence it can reach — not just the model underneath it.
The pace behind this shift is striking. Enterprises are moving beyond experimenting with AI agents to deploying and scaling them across the business.
The question is increasingly not simply whether to adopt agentic AI, but how well-informed those agents will be.
Signal AI’s role in that ecosystem is external risk and reputation intelligence — giving enterprise agents a view of the world outside the organisation. The external context that Risk, Corporate Affairs and executive teams rely on can now become part of the agentic workflows enterprises are building with Gemini Enterprise.
Your enterprise AI knows your business. Now give it the intelligence to understand what's happening around it.
Ready to connect?
Set up your custom MCP server connection in Gemini Enterprise and bring Signal AI’s external risk and reputation intelligence into your agentic workflows.