Agentic workflows: Revolutionising AI-powered sales

Infusing agentic workflows into an already-in-motion go-to-market strategy sounds like a heavy lift, especially for CSOs and frontline sales managers who can’t afford to take their eye off the ball, as it relates to active opportunities and upcoming pipeline they need to close to hit critical targets.

Learning new tools, decoding new data, and swapping out intuition for insight? That’s a tall ask for anyone under constant pressure to hit numbers.

But make no mistake: This shift is coming for every GTM function, including marketing teams, enablement personnel, and sellers, the latter of whom want (and need) to realise continuous improvement with their sales prospecting, buyer engagement, and deal conversion rates.

The beauty of onboarding a built-for-purpose agentic go-to-market platform is you’re not layering on complexity to GTM. Rather, you’re removing it.

With a built-in, native AI and analytics engine guiding your entire go-to-market staff in their day-to-day, they can lean on native, intuitive AI agents crafted specifically to support sales, marketing, and enablement with clarity, not chaos.

So, given the ROI at stake—and plenty of examples that prove the upside of the emerging technology— the question is simple: Are you ready to implement more intelligent workflows and help your reps show up (much) smarter in deals?

How agentic AI workflows are (already) having a big impact on B2B sales

There’s something of a paradox facing C-suites and boards today:

• On the one hand, organisational leaders and investors (increasingly) understand artificial intelligence‘s impact on their business processes. Large projects and smaller sub tasks alike can be more easily dealt with. The ability to solve problems that long plagued their companies becomes far simpler. Output quality improves every department.
• On the other hand, these same C-level decision-makers know the substantial legwork and change management required to onboard generative and agentic solutions: technical support, human-oversight protocols, external services that may need to be contracted to aid with AI adoption and usage, and other core components that must be addressed.

“Business executives are grappling with the tension between their awe of AI’s potential and the complexity of integrating it meaningfully into their organisations,” EY Global Consulting AI Leader Dan Diasio recently noted.

“What’s next for leaders is to harness the combined strengths of AI and human ingenuity and channel cost savings into groundbreaking innovations,” he added.

The issue with this innovation investment inertia, though, is fairly straightforward: The longer C-suites wait to secure AI systems with advanced machine learning models and natural language processing that can streamline and strengthen teams’ work, the longer inefficiencies will remain.

Some forward-thinking execs, including enterprise sales leaders, are already ahead of the curve, as they’ve given their teams tech with AI agents that drive GTM performance and—more specifically—help their sellers, managers, and CSOs alike all perform tasks more effectively (and quickly):

• Many VPs of Sales have instituted agentic workflows in their daily work that helps them prioritise deals, delegate reviews, and spend leadership hours on coaching and strategic calls that shape territory planning for quarters ahead companywide.
• Managers with a dozen-plus balls in the air at any given moment use AI agents for sales to rebalance workloads, bring coaching themes forward, and keep weekly check-ins with reps grounded in current-deal context instead of spreadsheets.
• And SDRs turn to AI tools to better accomplish tasks that take them away from actual selling, like researching accounts, drafting outreach, and updating records, so time blocks open up for conversations with prospects during peak prospecting periods.

As it pertains to your sales organisation, getting support from autonomous agents (or at least semi-autonomous ones) to complete tasks that open up free time (potentially hours each week) for your staff to focus on bigger-picture work must be a focal point in months and years ahead to accelerate growth.

“To get real value from agentic AI, organisations must focus on enterprise productivity, rather than just individual task augmentation,” Gartner Sr. Director Analyst Anushree Verma noted. “They can start by using AI agents when decisions are needed, automation for routine workflows and assistants for simple retrieval.”

Taking full advantage of AI agentic workflows: 8 unlocks for sales reps

You’ve already seen the power of large language models for various business processes. You’ve had plenty of experience with traditional automation tools. You’ve even likely experimented with standalone AI sales assistants for your team.

Now, though, it’s time to leverage AI for sales in a brand new, game-changing way: implementing workflows that lead to faster, smarter, more informed decision-making—and less reliance on manual human feedback and insight.

Some common AI agentic workflows the top GTM team use today include:

1. Automating complex tasks like qualification routing to help sellers focus on live deals

• Why it works: Unclogs sales pipeline plumbing so sellers don’t waste prime selling hours on early-stage noise, and gets them working the deals that want to close
• Agentic workflow example: Automatically send prospects’ sales content interactions (above a set numeric threshold) to reps via email, then assign a templated playbook with collateral and messaging mapped to product line and persona.

Many sellers face a number of complex problems with each opp they work, and routing leads manually burns valuable cycles and delays response time.

An agentic workflow reduces delay by routing qualified buyers quickly so reps can shift to strategic outreach. By enabling AI agents to assess input data faster than reps ever could, teams reduce busywork and close loops that usually slow down speed-to-lead and handoffs from marketing.

2. Tackling repetitive tasks such as CRM updates so sellers spend hours fewer in admin

• Why it works: Unburdens reps from the monotonous copy-paste grind, and turns buyer signals turn into CRM context without stealing time from real revenue motion
• Agentic workflow example: Auto-log lead activity in your CRM, when account stakeholders (champions, economic and technical buyers, etc.) open digital sales rooms, and populate a daily digest of untouched opps in each rep’s task queue.

Too often, sales professionals waste time toggling between inputs and fields during daily status updates that were never built for revenue conversations.

Leading AI agents operate inside workflows to manage routine tasks such as record updates, notes, and contact tagging instantly. Streamlining operations for sellers starts here: with smarter automation of field inputs no one in GTM wants to do and everyone forgets when pressure stacks.

3. Retiring traditional automation rules in favour of adaptive workflows built for revenue work

• Why it works: Tosses brittle logic trees in the trash, and provides SDRs with playbooks that rewrite themselves when the field flips and new priorities emerge
• Agentic workflow example: Auto-assign a new sales onboarding path to recently hired reps that adapts weekly based on skills check-ins with managers, play usage, and pitch feedback from content engagement analytics and call summaries.

Outdated sales automation rules follow static, linear logic that breaks under pressure from modern buyers and large, complex buying teams. In short, today’s sellers need advanced tools that recommend instead of route, using intent signals to inform branching paths that adjust based on deal movement.

Adaptive, AI-driven workflows handle all of this using structured and unstructured data, reducing dead ends and skipping irrelevant paths built on assumptions.

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4. Driving task execution tied to active opportunities through contextual agentic triggers

• Why it works: Helps revenue teams move with immediacy, not inertia, and aligns tasks with high-value opportunities so sellers engage at the moment they’re most usable
• Agentic workflow example: Trigger auto-assigned tasks to SDRs when potential customers in their pipeline engage with key assets shared in DSRs and other touchpoints and no reply has been sent by the rep in question to the lead within 48 hours.

Cutting-edge AI agents operate off real-time cues from rep behaviour, B2B buying signals, and existing CRM data to queue up next-best steps immediately.

Instead of relying on managers to manually assign and triage priorities, the technology proactively recommends outreach. With this structure in place, sellers don’t need to guess which task comes next because the AI system already mapped the best course of action based on deal dynamics.

5. Embedding continuous learning into daily workflows to shorten ramp and raise rep quality

• Why it works: Flips onboarding from binge-and-forget to steady-state mastery, and makes learning part of the job, not some static course buried in a slide graveyard
• Agentic workflow example: Deliver short knowledge checks to sellers tied to their current pipeline, and push educational ‘refresher’ videos to them weekly based on questions they failed to ask on calls with leads or poor deal outcomes.

Traditional onboarding ends after ramp, but rep’s sales performance depends (heavily) on continuous exposure to relevant information in context.

Agentic workflows deliver ongoing micro-learning based on rep gaps, buyer feedback, and historical and real-time data. Thorough, ongoing prompt engineering ensures reps get the right answers and insights consistently, enabling them to sell smarter and not have to routinely log into static LMS tools.

6. Analysing how sellers use content to understand what content resonates with real buyers

• Why it works: Shines a light on what content closes (not what assets looks ‘nice’), ensures winning collateral ‘floats to the top,’ and eliminates dud materials altogether
• Agentic workflow example: Auto-tag GTM materials with buyer intent signals, and re-order asset visibility in your sales content management system based on the last 30 days of win rates, pitch performance, and asset-level deal influence.

Every interaction leaves behind usage data, but the hard part is connecting consumption to outcomes and sequencing those insights into workflows.

Key components like buyer view time, doc engagement, and follow-up behaviour matter more in context. Enabling AI agents to spot those patterns across multiple sources lets teams promote content that works and retire what gets ignored.

7. Adapting dynamically as buyer behaviour changes using AI-driven workflows and rep inputs

• Why it works: Flexes with every market twitch, understanding what worked last week might tank today, and keeps your revenue organisation from moving like molasses
• Agentic workflow example: When reps flag competitive intelligence shared by buyers during calls, launch a workflow that assigns an entirely new talk track, enables objection-handling assets, and adds peer-call examples and battle cards.

Sellers operate in unpredictable selling environments, and static workflows age quickly when buyer expectations change weekly. Core capabilities like adaptive messaging, deal pacing, and asset sequencing hinge on timely rep feedback.

Innovative AI agents for GTM operate by absorbing that context and pivoting fast, so SDRs aren’t stuck using yesterday’s play in a game that already moved on.

8. Eliminates the need for deep research since agentic assistants bring key context forward

• Why it works: Gives sales reps a sixth sense, meaning they don’t have to conduct any late-night slide spelunking, since everything’s stitched together, waiting for them
• Agentic workflow example: Instantly respond to queries from sales team members, like “How do I pitch this product and service to legal buyers?,” by auto-generating answers sourced from successful sales plays and recent deal data.

Sales reps no longer need to sift through tabs or Slack threads to find who owns what or which asset fits a buyer’s need. Agentic assistants condense multiple sources into short summaries with next-best steps included, cutting down hours of prep.

With informed decision-making wired directly into sellers’ daily workflows, your sales teams respond faster and make fewer errors under pressure.

Empowering your sales force with AI agents that work ‘behind the scenes’

Your reps carry the weight of your GTM world, so to speak, on their shoulders, as they’re the heartbeat of every deal cycle, juggling various stakeholders, navigating prospect pushback, and connecting dots as quickly as possible to ensure they maintain momentum with pipeline currently on their plate.

That’s why the opportunity to ease their workload isn’t a tech priority.

It’s a growth strategy.

With the right mix of agentic workflows in place, you can assign highly specific tasks for each sales team member, auto-prioritise daily to-dos with minimal human intervention, and free SDRs up to do what they do best: sell.

These behind-the-scenes AI agents don’t just automate processes tied to sales productivity but also unlock smarter feedback loops that improve every handoff, pitch, and plan—in turn, strengthening your overall, long-term sales strategy.

As long as you feed your AI that offers such workflows the data and context it needs to run, it can address several challenges your teams face every day.

But here’s the catch: Not all artificial intelligence tools are built the same, nor do they provide the desired ROI you and other organisational leaders seek.

A true, purpose-built, agentic revenue enablement platform with native AI models and tools ‘baked in’ (i.e., not bolted on) and connected to your other essential GTM tech is what will drive repeatable, predictable growth at scale.

Point solutions can help in a pinch—but they won’t transform your GTM motion.