AI agents have left the demo stage. For the past two years, businesses have watched AI write emails, summarize meetings, draft content, and answer questions. The conversation is becoming more consequential, focusing on AI systems taking action inside real business workflows.
An agent connected to a company’s sales, marketing, or revenue stack can recommend accounts, enrich records, route leads, trigger follow-ups, and influence decisions across the business. Rather than relying on the model behind it, the agent’s usefulness depends on the quality of the data it is trusted to use. That is the infrastructure question behind the agentic age.
Morgan Stanley’s Midyear Economic Outlook noted that AI-related spending has become a major force in the current investment cycle, with data center infrastructure continuing to exceed expectations. The capital flowing into AI infrastructure reflects a broader reality: AI is moving from experimentation into the operating layer of the enterprise.
For go-to-market (GTM) teams, that operating layer starts with data.
This is the focus of Evolusha 2026, a live broadcast hosted by Lusha on May 27. The event will explore how data drives growth in the agentic age, with CEO and co-founder Yoni Tserruya presenting Lusha’s view on the role of verified data infrastructure in the next phase of AI adoption.
Lusha, a B2B data company used by more than 1 million professionals across sales, marketing, recruiting, and RevOps, is approaching the agentic era through a practical lens. Its platform includes more than 300 million verified contact profiles and 40 million company profiles, accessible through API, MCP, Claude, ChatGPT, N8N, Make, Clay, and the Lusha workspace. Tserruya’s argument is that companies adopting AI agents will need a strong data foundation to give those agents the context, accuracy, and structure required to support real business decisions.
From AI Assistance to AI Action
The first wave of generative AI adoption in business was largely centered on productivity. Teams used AI to write, research, and summarize faster. Those use cases created immediate value and helped companies comfortably experiment with the technology.
The next phase is operational. AI is being connected to workflows, systems, and business processes. For GTM teams, that means identifying promising accounts, enriching CRM records, detecting buying signals, prioritizing leads, generating outreach, and recommending the next best action.
When AI is used for assistance, a human reviews the output before acting. And when AI is embedded in workflows, the information feeding the system largely determines the quality of the outcome.
A sales team may use an agent to prioritize accounts, while a marketing team may use one to enrich inbound leads and segment campaigns. At the same time, the revenue operations team may rely on AI to route prospects, update records, or trigger follow-up sequences. In each case, the agent’s value depends on the accuracy and completeness of the data it can access.
The more AI moves into execution, the more businesses need confidence in the data layer beneath it.
Why Data Infrastructure Matters
For years, B2B data has been associated with prospecting and sales intelligence: finding contacts, verifying information, building lists, and supporting outbound activity. Those functions remain important, but the rise of AI agents is expanding the role of data across the business.
In an agentic environment, data becomes part of the operating layer. It helps AI systems understand who a company should engage, what context matters, which signals are relevant, and when teams should act. It also connects activity across sales, marketing, customer operations, and revenue systems.
That makes data infrastructure a strategic consideration. Clean, current, and verified information can help agents produce more relevant recommendations and support more consistent workflows. Fragmented or outdated information limits what those systems can do.
For GTM organizations where teams already work across multiple tools and data sources, the opportunities come from CRM platforms, sales engagement systems, marketing automation, enrichment providers, intent platforms, and internal spreadsheets. AI agents can help bring those workflows together when they have a reliable foundation to work from.
At Evolusha 2026, Lusha is expected to show three live workflows that demonstrate how verified data supports AI-powered execution across GTM teams.
The Business Case for Verified Data
Companies are now looking beyond experimentation toward measurable business impact and whether AI can help teams move faster or make better decisions.
For instance, sales can benefit from better data that helps teams identify the right prospects, understand account context, and focus outreach on the most relevant opportunities. In marketing, it supports stronger personalization and better lead qualification, while revenue operations gains improved routing, scoring, enrichment, and workflow automation.
These operational improvements also affect growth directly. With more reliable information, teams spend less time correcting records or hunting for context. They move faster and with more confidence, focusing their attention on customer conversations and strategy.
AI agents amplify that value by turning data into action. A system that understands company information, contact details, buying signals, and CRM context is able to make strong predictions and provide recommendations about where to focus and what to do next.
A More Practical View of AI Adoption
The agentic age is often discussed in broad terms, but the day-to-day implications are specific. A pipeline can be built from a defined ideal customer profile, enriched with verified data, scored by relevance, and updated as new signals appear. Job changes can trigger a timely outreach sequence, and a large lead list can be ranked and refreshed before a sales team starts its day.
These examples are less about futuristic automation and more about removing the manual steps that have long slowed revenue teams down. The value comes from connecting reliable data to workflows that already matter.
Trust and compliance are also becoming part of the infrastructure discussion. Lusha is certified for GDPR, CCPA, and ISO 27701 and independently audited by TrustArc and ePrivacyseal, details that stand out as data quality and governance grow increasingly intertwined.
What Evolusha 2026 Will Explore
Evolusha 2026 takes place on Wednesday, May 27, at 11:00 AM ET as a free live broadcast. Registration is open at lusha.com/lp/evolusha2026.
The event’s central theme, How Data Drives Growth in the Agentic Age, reflects a broader conversation across the technology market. As companies introduce AI agents into their operations, they are also examining the systems, data, and workflows those agents depend on.
Tserruya is expected to outline why data infrastructure is becoming a defining layer of AI adoption. He brings the perspective of a founder who scaled Lusha from a browser extension into a platform used by more than 1 million professionals, giving him a clear view into how sales and marketing teams are reshaping their workflows.
From testing agents inside workflows to enhancing copilots and productivity tools, the agentic age is still developing, and companies sit at different stages of readiness. As companies look beyond experimentation, measuring business impact and AI performance against real revenue targets inside live workflows will determine the next phase of agentic AI.
