How CIOs use AI agents to accelerate revenue growth

A wave of AI agents has recently emerged for sales and revenue teams, including Highspot’s Deal Agent, an agent to accelerate pipeline generation and conversion, and Qualified’s Piper for Demandbase, an AI sales development representative (SDR) agent. Salesforce’s AgentForce and many others are also rallying behind using agents for this use case as well, so much so that Gartner estimates for the remainder of the year, 40% of enterprise applications will feature task-specific AI agents. Further analysis in a Workato-sponsored 2025 survey by Harvard Business Review paints a similar picture, where 86% of 600 technology decision-makers plan to increase investment in agentic AI over the next two years, with a growing proportion to empower sales.

“Sellers are preparing faster, showing up with sharper points of view, being more strategic, and spending more meaningful time with customers,” says Kellie Romack, CDIO at ServiceNow. “We’ve already seen amazing early results with sellers cutting prep time from hours to minutes, almost a 95% improvement.”

Executives report that AI agents are accelerating sales teams by conducting prospect and customer research, reducing manual toil across the sales workflow. Yet, while the excitement is evident, it’ll take discipline to know where precisely to deploy AI agents, let alone how to operationalize, secure, and scale multiple agents throughout an entire sales organization.

“AI agents deliver the most value in sales workflows that are well-defined, rule-based, and involve high volumes of repetitive activity,” says Dan Shmitt, CIO at Salesforce. “These are areas where scale, consistency, and speed matter, but ultimately, where teams lack the capacity to engage at scale.”

Conducting customer research

AI agents are poised to accelerate sales teams in a few ways. One area is giving sales engineers better context. “AI agents can help make sales teams the most relevant communicators in front of their prospects or clients,” says Tiago Azevedo, CIO at AI development platform OutSystems.

Or take the use of AI agents in sales at fleet management company Samsara, for instance. CIO Stephen Franchetti, previously CIO at Slack, shares that AI has always been fundamental to Samsara’s platform to help automobile fleet operators optimize routes or check vehicle health. But more recently, they’ve applied AI knowledge agents internally throughout the company.

Samsara has created an internal model fine-tuned on Samsara data. This Samsara GPT, as he calls it, is trained on the company’s product knowledge base, specific customer data, and is connected to Salesforce and other systems, helping sales executives quickly become account-specific experts, answer customer questions faster, and accelerate onboarding processes.

“It’s the most successful rollout we’ve done,” says Franchetti. “It’s universally embraced by our sales organization and we’ve received amazingly positive feedback.” As a result, Samsara account development representatives (ADRs) are experiencing 16% better attainment using this internal GPT.

At ServiceNow, there are AI agents empowering sellers with highly relevant context. “We’re using an AI-powered coaching experience and sales hub to make our sellers more effective at preparing for meetings and navigating deals,” says ServiceNow’s Romack. “Our AI Sales Coach, built on Anthropic’s Claude, pulls together account data, research, and product intelligence into actionable guidance.”

Accelerating lead prospecting

That knowledge is also improving outbound strategies, helping ADRs synthesize prospect data and draft more personalized outreach. For example, Samsara’s pilot group has noticed a 300% increase in callbacks to emails using this strategy, says Franchetti.

In other organizations, internal experiments are still in pilot mode but showing strong signals. For example, Kate Prouty, CIO at Akamai, shares that the cloud, security, and content delivery network company has been piloting an internal AI-powered sales assistant it calls SaiLS Bot within its sales development representative (SDR) team. “SDR teams used SaiLS to accelerate prospect research with quick company understanding and impact analysis,” she says. This helps spin up account-tailored go-to-market plans, and surface cross-sell opportunities.

By using AI agents, SDRs at Akamai are able to quickly understand target companies, existing solutions, and potential cybersecurity impacts, dramatically shortening their prospecting cycles. “Quantitatively, in the first nine months of using SaiLS Bot, the sales team saved the equivalent of three full-time employees in labor,” says Prouty.

At ServiceNow, agents aid in how they prospect new leads, conduct post-sales follow-up, and query the status of deals in the pipeline more easily. For instance, a seller might prompt something about opportunities in a particular territory with a range of probability to close and stage details in order to gain highly targeted information.

Some leads naturally come from inbound sources, too. But human labor has historically struggled to qualify this interest at scale. “Our recent research shows only 25% of inbound sales leads require human outreach or follow-up, leaving roughly 75% of leads historically receiving no engagement at all,” says Salesforce’s Shmitt. “That gap represents a structural limitation of traditional sales models.”

At Salesforce, one remedy has been deploying its website agent, which answers product questions and qualifies inbound interest. Salesforce then uses a sales agent internally to inform sellers with quick access to account information, deal history, pricing, and executive briefing materials, helping shift initial interest into active conversations.

Enriching pipeline and follow-up

Sales teams are experiencing high pipeline growth using AI agents. “We’ve deployed AI agents across our sales operations at Workato,” says Carter Busse, CIO at the automation and integration platform. For instance, they recently generated $2.7 million worth of new sales opportunities using an internal agent that analyzes calls in Gong, an AI operating system for revenue teams, to determine what made closed-deals successful to better cater future outreach.

In total, Workato has built 28 sales agents for various autonomous sales processes, including handling opportunity enrichment, quote generation, approval routing, and meeting follow-ups. Agents also automatically update CRM fields with new data from client interactions, improving their data quality and reliability.

Busse grounds the results in efficiency savings. “Deals with agent-enriched data progress through pipeline stages up to 20% faster,” Busse adds. “We’ve also seen 40% faster quote turnaround, and five to seven hours per week back to sellers to focus on customer conversations.”

Salesforce’s Shmitt also sees big potential in using agents for repetitive workflows related to follow-up communication, expanding what human sellers can realistically respond to. “In sales, this typically includes processes like answering product questions, managing follow-up, re-engaging stalled interest, and determining when to route a lead to a human seller,” he says. The company deploys its tech internally, using its engagement agent to automate some of these lead engagement activities. “The agent delivers 24/7 personalized outreach, product Q&A retrieval, objection handling, stalled-lead re-engagement, meeting booking, and lead information collection, with defined escalation paths to humans,” he says.

OutSystems is similarly seeing gains from deploying agents to automate tedious tasks and accelerate revenue-facing workflows. “Agents are improving pipeline accuracy and reducing manual administrative work that slows deal cycles,” says Azevedo, who estimates they’ve saved 1,700 hours in manual administrative work using their contact agent. “By arming our team with the right proof points at the right moment, we validate value faster and shorten deal cycles.”

Generating sales enablement materials

AI agents can also generate customer journey references and case studies. For example, OutSystems is dogfooding its AI agent building tools to streamline sales operations, most notably using Agent Workbench. “We’ve built a multi-agent system that arms our sales team with key insights on prospects and their buying signals, relevant case studies, and points for successful pitches,” says Azevedo.

“We’ve enrolled all our sales team members in Deal Mate, one of our customer story agents, which has suggested 2,859 relevant stories to the sales team since October,” he adds. They’ve also created hundreds of automated sales decks using a CMS assistant, which has delivered significant time savings as well.

Overall, AI agents are giving sellers more firsthand, cross-industry insights into how customers are using their platform or services, along with business improvements they’re gaining. CIOs report that this automated research is contributing to productivity savings and improving funnel conversions.

Also, financial analysis can be expedited using agents. At Samsara, MCP servers are supercharging financial analysis that would’ve traditionally required considerable manual labor. “Technically, it’s much less of a lift,” says Franchetti. “Our financial language gets to our community in a much more natural way.” For this, they’ve used Workato One to take recipes and workflows, and expose them as MCP servers for their agentic processes.

Governance and controls required

Although early usage signs look promising for innovating sales pipelines, CIOs must implement governance controls for agentic AI in sales workflows, especially when agents have access to customer data or the ability to conduct autonomous actions.

“The guardrails matter just as much as the technology itself,” says Shmitt. To reduce risks, CIOs should clearly define roles for agents, limit permissions, and set human approvals for high-value actions. “Many organizations introduce agents into workflows where they support decisions rather than deploy fully autonomous agents,” he adds.

Access control, especially authentication and authorization, is a common priority for CIOs. “Our top concern is ensuring agents working on behalf of sales employees only access authorized data,” says Workato’s Busse. “Anything that modifies customer records or sends external communications requires approval.”

For Azevedo, it comes down to balancing innovation with safeguarding infrastructure and data. “Agentic AI requires organizations to invest on both ends of that mission, to ensure customer data and deal integrity remain intact while also automating core business processes throughout the enterprise.” To do so, he recommends unifying disparate data sources, and streamlining how agents discover and create that data.

He adds it’ll also require transparency into agentic behaviors. “Full observability into how agents make decisions is critical for CIOs to support or propel their organization into an age of intelligent hyper-automation based on trust.”

ServiceNow’s Romack adds that it’ll also take CIO leadership. “For users, we’re also here to guide and coach,” she says. “A great example is ensuring our sellers get best practices to create prompts like the right account and deal context, specific metrics, breaking down complex requests, and review and validation.”

In effect, Romack sees the CIO as playing a unifying role to build integrated experiences and curb possible tool sprawl. “The CIO should be the great unifier, bringing together sales, IT, legal, finance, and security to create powerful and responsible experiences,” she says. “That balance is what unlocks value across the organization.”

But it’s not just humans that need leadership and unification. Agents need training on institutional knowledge and policies as well. “We treat agents like employees,” says Busse. “They’re onboarded with curated knowledge about the business, trained on data governance policies, and monitored continuously.”

Plus, CIOs have a financial responsibility to consider. “AI agents should be treated as long-term systems, not short-term experiments,” says Akamai’s Prouty. As such, CIOs should collaborate with IT leadership to manage costs for AI agent rollouts. “Without oversight, costs can spin out of control for AI tools that aren’t delivering value to teams.”

Best practices for AI agents in sales

Before diving headfirst into agent-assisted revenue workflows, sales teams will need foundational capabilities in place. “Many organizations still lack a clear roadmap for how to start, scale, and define success,” says Shmitt. Preparing things like data, governance, and operating structures are key to responsible adoption, he says.

Success will lie in a strong data foundation consistently updated to avoid stale or static resources. “Enterprises that thrive will be those prioritizing a centralized foundation and a strong knowledge base of content and data, and collaborating with business champions across their agentic journey,” says Azevedo. The latter will necessitate a cultural shift, he says, which requires viewing agents as extensions of the workforce with clear ownership and operational structure. As such, he recommends assigning select team members to specific agents, monitoring performance, and flagging issues as necessary.

Next, start small with valuable use cases tuned to seller workflows. “Pick one or two high‑impact use cases where the value is obvious, like sales prep or deal coaching,” says Romack. To do so, she recommends working closely with sellers to understand their pain points to create experiences that function inside their existing workflows. “If they’re swiveling between systems with AI that doesn’t help them land their next meeting or close their next deal, it won’t stick.”

So start where conditions are already strong, adds Shmitt. “Agents deliver the most value in sales environments with clear documentation, structured data, and well-defined workflows,” he says. Testing agents in these areas will help validate accuracy and value, which can inform adoption.

Other CIOs agree organizations shouldn’t automate everything at once. Instead, Busse recommends starting small with high-pain, low risk workflows like an agent that proactively manages license optimization. “Pick something where you can measure impact quickly,” he says.

Past the experimentation stage

Executives are attempting to drive ROI from their AI investments, but returns have been murky. A report from PwC at Davos found that 56% of CEOs said they’ve seen no financial benefit from their AI investments, and only 12% reported both cost savings and revenue growth.

On the flip side, agentic AI in sales is showing more promising returns. While it’s still early days, executives report impressive gains from deploying AI agents within revenue workflows. They help sellers automate repetitive work across the sales lifecycle, from qualifying leads, refining messaging, and researching accounts, to gathering relevant customer insights, auto-updating CRM systems, and more.

“All these use cases and investments we’re making deliver ROI and key metrics from a business perspective,” says Samsara’s Franchetti. “We’ve moved past experimentation. Now it’s more about where the value is and how do we apply AI to meet that value.”

And rather than replacing sales teams wholesale, AI agents are complementing humans and allowing them to emphasize soft skills. “The efficiency gains alone justify the investment,” says Busse. “Reps are spending more time on actual customer conversations and less on administrative work.”

But enterprises don’t yet report closing deals entirely with AI agents. Instead, as Romack says, these tools elevate human potential, with AI agents doing the heavy lifting on context, synthesis, and next steps, while sellers bring the strategy, relationships, and outcomes.

“The value is immediate,” she adds. “You can jump right in and feel the results in your first meeting of the day. Over time, that compounds into better deal execution, like fewer stalled deals, stronger mutual plans, and more consistent value.”