Modern selling was built around tradeoffs that once felt unavoidable. Over time, those tradeoffs became the working reality for revenue teams, shaped by real structural constraints. Speed or quality. Coverage or capacity. Preparation or presence. At the same time, the seller role expanded. Research, CRM updates, coordination, follow through. Complexity compounded. Decision-making slowed.
AI Assist improved efficiency and introduced intelligent synthesis into the flow of work. It reduced friction and elevated execution. Agentic AI builds on that foundation. Intelligence support now operates inside the workflow, preserving momentum, strengthening judgment in the moments that matter, and scaling execution beyond the limits of individual capacity.
The result is more than incremental productivity. It is scalable capability across the revenue engine.
This was the focus of a recent webinar I hosted with IDC Research Manager Michelle Morgan, one of the leading voices in revenue operations research. I’m Theresa Piasta, VP of AI Value Strategy at Outreach, and in that conversation we walked through the structural shift underway in revenue operations and what the rise of agentic AI means for modern selling.
Below is a closer look at the key themes from that discussion.
Why Selling Feels Harder (And Why AI is the Answer Now)
One of the first major themes we explored during the webinar was persistent friction in revenue operations. There are a lot of causes for that friction: forecasts fluctuate, deals stall, and sellers are bogged down by admin work. According to IDC’s assessment of the sales landscape, salespeople spend 30–50% of their time on non-selling work.
And as we’re seeing across all industries, AI is helping automate several of those tedious, time-consuming tasks. Michelle highlighted that 40% of organization are now scaling AI initiatives because they’ve seen so much success. If you’re not investing in AI at this stage, you’re falling behind in both speed and precision she further shared.
As Michelle noted, better forecasting isn’t driven only by more CRM inputs. It’s driven by systems that identify and surface real signals (consistently, and with greater completeness) reducing human bias in the process.
A consistent theme throughout our conversation was the importance of being intentional and decisive about reimagining workflows across departments. The idea is how to better leverage agentic AI to reduce friction across the revenue engine.
Leading organizations are strengthening handoffs by absorbing work that pulls sellers away from selling and reducing cognitive drain in the moments that matter most. And in doing so, they’re creating capacity for net new opportunity.
This is where the conversation evolves.
It’s no longer just about efficiency. It’s about unlocking what becomes possible when friction is removed. What if we could sell more? What if momentum didn’t stop when people logged off?
Real Strategies, Real Voices: Agentic AI in Action
Don’t settle for surface-level insights. Watch the hosted conversation between myself and IDC’s Michelle Morgan to hear firsthand how revenue leaders are navigating the shift from human-initiated assistants to AI agents that drive real outcomes. You'll get perspectives and practical frameworks we simply can't capture in a single article.
The Shift: From AI Assist to Agentic AI
Our discussion focused on the broader reimagination of work and what it means as intelligent systems become embedded into operating models.
Most revenue teams are familiar with AI Assist: intelligent support within the workflow that surfaces insight, synthesizes context, and recommends next steps, while humans remain firmly in control of judgment and decisions.
AI Agents take that foundation further.
While Assist elevates how work is done, AI Agents complete work on behalf of the seller. Once configured with objectives and governance guardrails, agents operate autonomously within defined domains — identifying relevant conditions, initiating actions, and carrying multi-step workflows through to completion.
They don’t simply recommend the next step. They execute it.
Research is conducted. Records are updated. Sequences are activated. Follow-ups are personalized and within established organizational guardrails.
Driving Outcomes: Clarity, Capacity, and Scale
Efficiency is only part of the value story.
The first advantage is clarity. Conversation Intelligence captures and connects customer interactions in real time, preserving context and revealing patterns early enough to act — while momentum remains intact. AI Assist builds on that foundation, accelerating speed to clarity. Insight is synthesized, next steps are guided, and sellers, leaders, and executives move from signal to confident decision-making faster.
AI Agents extend the impact further. Operating within company context, agents identify meaningful conditions and autonomously advance work across the revenue engine within established guardrails, driving measurable capacity, capability, and scalability gains.
Capacity expands as administrative lift and research are absorbed in the background, allowing teams to stay focused on high-impact work. Capability rises as execution becomes more consistent and context carries forward across interactions, elevating the average level of performance across the organization.
Scalability improves as precise execution and winning behaviors extend across teams — not just within isolated individuals. The result is clear: clarity accelerates decision-making, execution advances with reduced friction, and performance compounds across the revenue engine.
“The best sales teams don’t just move fast. They move with precision. Agentic AI gives you the clarity to know exactly where to apply that pressure.”
Enterprise Adoption: Designing Agentic AI for Scale
The companies making real progress aren’t just starting with technology—they’re starting with friction. They identify where revenue stalls, where opportunities are missing, and where workflows break down across departments. Then they redesign those workflows intentionally, following a phased roadmap that accelerates time to value and scales with discipline.
AI agents amplify the systems they operate within.
Organizations scaling successfully focus on three areas:
Data foundation: Clean, connected, decision-ready data directly impacts agent performance. Fragmented or inconsistent data degrades performance and limits impact.
Governance and oversight: Clear guardrails, defined decision boundaries, security controls, and oversight mechanisms provide accountability at enterprise scale.
Leadership alignment: Because agentic AI represents an operating model shift, change management and cross-functional collaboration are essential to drive alignment and adoption.
The Future of the Seller
The future of the seller is defined by elevation.
As AI-powered support becomes embedded into revenue workflows, the role evolves. Administrative lift, synthesis, and personalization at scale are absorbed in the background — allowing sellers to focus on what drives the greatest impact: judgment, strategy, negotiation, and trust.
This shift increases the strategic importance of the seller. With intelligent support operating alongside them, sellers engage more intentionally in the moments that matter. Managers spend more time coaching instead of inspecting pipelines. Customers experience seamless, context-rich continuity across every interaction, preserving momentum throughout their journey.
The result is amplified performance across the revenue organization. Focus improves. Execution becomes more consistent. And impact compounds over time.
Building Your Competitive Advantage Today
If there’s one takeaway from our session, it’s this: the opportunity is real, and the shift is already underway. The organizations pulling ahead recognize AI adoption as a strategic transformation — one that reshapes how they operate, compete, and grow.
Companies are gaining clarity, elevated effectiveness, and measurable growth.
That’s the competitive advantage unlocked when AI-powered support is embedded into daily workflows — driving clarity, consistent execution, and compounding growth.
If there’s one takeaway from our on-demand session, it’s this: the opportunity is clear, but the window is closing. The teams pulling ahead see the adoption of AI as a strategic shift that’s reshaping how they win.
Clarity. Consistency. Measurable growth. That’s what Agentic AI unlocks for leaders who are ready.
Agentic AI: The Next Competitive Advantage
Ready to move beyond the hype? Watch the full session to learn how to prepare your data, your teams, and your strategy for the age of autonomous revenue orchestration.
FAQs
1. What is the difference between AI assistants and Agentic AI?
AI Assist typically provides guidance and synthesis. AI Agents, by contrast, operate autonomously, grounded in business context, to execute tasks and workflows.
2. How does Agentic AI improve sales forecasting?
By automating data capture and analyzing signals across the buyer journey, Agentic AI removes bias and guesswork. It provides leaders with an objective, data-driven view of deal health and pipeline reality.
3. What are the prerequisites for adopting Agentic AI in revenue operations?
Successful adoption requires a unified data foundation (data integrity), robust governance and security protocols, and strong leadership buy-in to manage organizational change and build trust.
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