Key Takeaways
- Highspot’s 2026 GTM Performance Gap Report found 42% of go-to-market and revenue leaders at mid-market and enterprise B2B organisations believe fragmented, disconnected technologies are a direct cause of their GTM execution breakdowns.
- The best go-to-market platforms with native and intuitive AI capabilities empower GTM teams to connect buyer intent, content usage, pipeline movement, and seller workflows in one place. That combination helps sales, marketing, enablement, and RevOps teams act on shared data instead of reconciling disconnected tools.
- Not all AI-powered go-to-market tools are ideal for large B2B companies. The strongest options unify account and deal intelligence, automation, GTM collateral, coaching and training, and analytics so leaders can support complex buying journeys, improve seller productivity, and measure which programmes influence pipeline, revenue, and retention.
Your go-to-market function isn’t behind because you haven’t invested in technology. Odds are you, like leaders at countless other large organisations, have allocated plenty of budget to new solutions (AI and otherwise) in recent years.
The persistent problem most enterprise executives in revenue roles quietly wrestle with today is a surplus of the wrong systems for their GTM staff.
Notably, ones that were stitched together in ways that looked reasonable at the time but now feel like a Frankenstack nobody fully understands or knows how to take full advantage of for their respective needs.
Highspot’s GTM Performance Gap Report 2026 found 42% of B2B go-to-market leaders said fragmented, disconnected software was a direct cause of execution breakdown. (Based on our chats with revenue teams, that tracks.)
Before you can simplify your stack or move faster with AI for sales, marketing, enablement, and RevOps teams that actually shifts outcomes, you need to take an honest look at what you’ve built: where the gaps are, where the redundancies live, and what your teams genuinely rely on day to day.
From there, the path forward points to one clear answer: Secure an agentic AI command centre that coordinates all things associated with GTM.
Go-to-market platform FAQs
How do AI-powered platforms help B2B go-to-market leaders close the gap between GTM strategy design and field execution?
Unified platforms help go-to-market leaders bridge strategy and field execution by giving sellers, managers, and enablement teams a shared, real-time view of account activity and deal context. Both AI agents and agentic AI act on that context automatically, triggering next steps and coordinating across functions without requiring manual input from any individual team.
What makes AI platforms built for go-to-market leaders more effective at connecting GTM insights to measurable outcomes?
The best tools for go-to-market leaders connect behavioural signals, pipeline progression, and content engagement directly to outcomes like win rates, deal velocity, and cycle length. An effective GTM strategy requires systems that translate those insights into clear, timely actions for sellers rather than delivering reports after the window to act has already closed.
How can I merge digital marketing data and sales intelligence into one AI go-to-market system to improve GTM execution?
Centralised systems bring together go-to-market intelligence from buyer intent data, lead engagement signals, and CRM system records into a unified model that informs decisions across functions in real time. Successful GTM strategies are built on this kind of integrated data layer, which reduces the lag between what buyers do and how coordinated teams choose to respond.
Should we execute our account-based marketing and sales approaches from one AI go-to-market system or multiple tools?
Consolidated solutions that coordinate go-to-market operations embed sales and marketing automation at the foundation so account targeting, outreach timing, and content delivery all run from the same data layer. That removes the friction of parallel motions run without a shared sales process, replacing manual handoffs between functions with coordinated execution from a single source of truth.
Can AI go-to-market software help us streamline potential- and existing-customer analysis and boost GTM performance?
Purpose-built software for go-to-market teams evaluates potential customers and existing accounts by signal strength, engagement history, and fit against your clearly defined target audience. That structured segmentation helps teams direct outreach and budget toward accounts with the highest likelihood of advancing, rather than spreading resources across contacts unlikely to convert or expand.
How should we use data-driven insights tied to our sales engagement efforts to improve how we connect with our prospects?
Strong platforms equip go-to-market teams to track how prospects move through the B2B customer journey by aggregating recent and real-time buying signals across channels into a single, reliable view. Data accuracy across those sources is what determines whether your team acts on real intent or on outdated records that no longer reflect where a given lead actually stands.
What should my operations and revenue teams revenue ask AI go-to-market technology providers in RFPs and intro calls?
Evaluating tools for go-to-market teams means pressure-testing data quality commitments, asking how conflicting signals are resolved, and confirming that outputs align with how your team actually operates. When the underlying data is poor, even well-designed B2B sales strategies will underperform because the models surface the wrong priorities and erode seller confidence in the guidance.
Which enablement, RevOps, marketing, and sales team stakeholders should we loop into AI GTM tech vendor discussions?
Cross-functional decisions about systems that span go-to-market operations should include marketing teams, enablement leads, frontline managers, and operations owners who each carry distinct adoption responsibilities. A comprehensive plan for vendor evaluation captures those stakeholder requirements early, defines success criteria by function, and establishes governance before any contract is signed or budget committed.
How can we tell if an AI go-to-market platform can help us achieve our business objectives and hit key B2B revenue targets?
The right solutions connect big-picture go-to-market objectives to measurable outputs by showing how lead and customer data flows from first touch through close and into retention. Ask prospective vendors to demonstrate that connection using accounts comparable to yours in complexity, deal structure, and team size rather than showing idealised proof points from outlier customers.
What features, functionalities, capabilities, and services do the best go-to-market software vendors provide B2B GTM teams?
The strongest software vendors for go-to-market teams combine native analytics, workflow coordination, content guidance, and integrations that work within your existing tech environment without requiring heavy configuration. A strong GTM strategy depends on vendors who invest in measurable outcomes alongside feature development and can clearly show how their product evolves as your needs change.
Why AI go-to-market platform investment is high (and continually rising)
The rapid rise of agentic AI adoption is occurring for good reason: The technology is a proven force multiplier for go-to-market team productivity. (Not to mention it increases their companies’ overall GTM maturity as well.)
But investment and implementation is just the starting line of the value-add marathon. Execs and department heads at thriving B2B businesses recognise onboarding a new platform also requires process-related changes.
Leading enterprises today “redesign the work, not just the tooling,” as Forrester principal analysts recently wrote. “Agents bolted onto human-paced legacy workflows produce task savings, not step-change value. Pick a few high-friction workflows and rebuild the roles and approvals around autonomy.”
Enterprises make this mindset shift when bringing on agentic AI because:
Salespeople must adopt signal-based selling approaches that require AI-powered intel
Sellers relying on static contact lists and gut-feel prioritisation are bringing a compass to a GPS race: Accounts worth pursuing signal well before your team knows they’re evaluating vendors.
Platforms with embedded B2B buyer intent detection continuously scan engagement patterns and behavioural triggers so sales teams arrive at every conversation knowing what the account cares about and why now.
Buyer behaviour continues to evolve, requiring B2B sellers to keep up with AI assistants
Your average enterprise buyer has done the research, looped in a half-dozen colleagues, and already built a shortlist before your team even knows they’re in-market.
Keeping pace with that level of self-directed evaluation demands real-time visibility into sales engagement across channels and touchpoints, not quarterly pipeline reviews built on aging CRM data and secondhand salesperson recollections.
Tedious sales workflows take precious selling time away from reps with lofty quotas
Most sales professionals spend the majority of their working hours on admin tasks that have nothing to do with selling. Think logging CRM updates, formatting sales pitch decks, and chasing approvals.
All activities an intelligent workflow layer could handle automatically.
Every work hour recaptured from that overhead is time a seller can reinvest in a high-potential account and start planning future calls.
Leaders want a single system of intelligence to replace disparate GTM point solutions
Maintaining separate tools for sales forecasting, content, coaching, training and pipeline management only fractures your GTM performance picture.
Executives at large organisations increasingly demand one, authoritative intelligence layer that synthesises data across functions, surfaces guidance in context, and replaces the manual reconciliation that masquerades as cross-functional alignment.
Where old-school go-to-market tools fall short for modern GTM teams
“From escalating maintenance costs and security vulnerabilities, to missed opportunities in automation and data integration, outdated technology erodes enterprise growth,” Forbes Technology Council’s Guy Yehiav recently wrote.
You probably don’t need any convincing of the merits of sunsetting inefficient tools, given you can see firsthand whether they’re pulling their weight or wreaking havoc on your sales, enablement, and marketing efforts.
That said, it’s worth highlighting the common ways in which dated and ineffective platforms can have on your go-to-market strategy and daily work:
- Legacy B2B buyer engagement platforms keep lead activity trapped in static snapshots, leaving leaders squinting at half-told account stories while sellers walk into important conversations with yesterday’s signals and a lot of avoidable uncertainty.
- Dated marketing and sales automation tools turn simple coordination into an awkward baton pass, so campaign timing slips, seller follow-through gets spotty, and teams spend too much energy babysitting workflows that should run cleanly on their own.
- Traditional account-based marketing solutions tend to freeze account strategy in place, making it harder to adjust around live prospect interest, shifting priorities, or fresh stakeholders who suddenly appear and change the shape of the deal.
- Feature-limited sales call recording software captures conversations but leaves the meaning buried, which forces managers to scrub through hours of footage just to piece together objections, buying signals, and the handful of moments that truly mattered.
- Inefficient lead-routing and -scoring systems send promising accounts into the wrong hands or the wrong queue, creating a slow leak in sales pipeline quality that revenue leaders eventually spot in conversion rates and seller frustration.
- Antiquated sales training and development platforms make reinforcement feel detached from the work itself, so sellers complete lessons in one place and face live buyer pressure somewhere else with very little connective tissue between the two.
- Old-school seller training and development tech usually measures completion better than readiness, which leaves enablement leaders staring at tidy participation numbers while frontline performance still swings wildly from team to team.
- Static enterprise content management systems let outdated collateral hang around far too long, so marketing keeps publishing fresh material while sellers keep reaching for familiar files that should have retired three product updates ago.
None of these issues arrive as fireworks.
It’s always a successive series of problems that pile up: An upload fails, a connector sulks, a seller opens three repositories and still ends up forwarding Q3 collateral from a laptop folder, and a manager pieces together pipeline status from screenshots, forwarded threads, and corridor chatter.
Marketing publishes fresh material, while circulation still favors whatever someone saved six months ago. Enablement rolls out a major initiative, while adoption varies wildly from team to team, office to office, and manager to manager.
After a while, the entire revenue org spends its energy compensating for software while forfeiting its help. Buyer-facing work remains farther from the lead than any executive would ever tolerate, with crucial files parked in personal folders and institutional know-how parked in people’s heads. (Not great.)
Smart go-to-market professionals with a lot on their plates end up spending hours translating, reconciling, and double-entering. Leadership receives partial numbers, stories, and accountability. Ordinary work becomes heavier than it should. Strategic programmes arrive seller-side already winded.
In short, past-their-prime sales tools corrode organisational trust by inches.
What the best go-to-market platforms with AI offer GTM teams: 8 use cases
Collecting a plethora of lead and customer insights comes with territory for GTM teams. But what if you’re forced to manually comb through that data to get intel that can inform your marketing plans, sales strategies, and enablement approaches?
Taking action on that info becomes near-impossible.
‘Flipping on’ the agentic AI ‘switch’ is your answer.
With artificial intelligence guiding your go-to-market teams, you can ensure previously laborious activities can get put on autopilot. Tasks like:
1. Recognising which content, messaging, and plays resonates with high-value accounts
Inside an AI-driven go-to-market hub, sales content analytics can sort assets by buyer appetite and reveal which materials earn a second look.
That gives sales leaders a firmer way to trim the filler and back target-account selling with collateral that prospects clearly express an affinity for, based on engagement data (think digital sales room opens and document clicks).
Under the hood, agentic AI learns from every share, enabling it to auto-suggest the most applicable resources for sellers at the most opportune times.
2. Assessing where active opportunities tend to slow and accelerate in their buyer’s journey
A winding B2B buying journey can turn even healthy opportunities into head-scratchers. Thankfully, agentic AI can reveal which accounts are losing steam and what next-best action from a seller can likely help nudge committees along in their process (without coming across as insensitive or overbearing).
That gives managers a steadier view for coaching and reps a workable route toward deal acceleration. Meanwhile, agentic AI keeps reading lead participation in calls and account movement at large so the story stays current and the next discussion with a prospect always feels grounded and pertinent.
3. Capitalising on insights using a CRM integration and direct connections to other GTM tools
A go-to-market platform with native AI proves its worth once CRM records, email trails, buyer interest data, and collateral usage intel are connected. Revenue leaders want actionable insights at the ready for all GTM teams at all times.
That only occurs when there are seamless syncs between a single source of AI truth and other go-to-market apps. With such a setup, sales reps spend their time advancing accounts instead of translating strange data dialects.
4. Blending historical and predictive analytics to get an exhaustive view on key accounts
Inside an agentic go-to-market platform, AI can combine revenue intelligence with predictive sales analytics so account histories are weighed against fresh buyer movement and weak opportunities are exposed earlier.
Sales leaders get a fuller account picture. Meanwhile, sellers focus on pursuits that still have room to mature instead of flattering pipeline mirages ahead.
5. Automating outreach to contacts at cold prospects who could turn into qualified leads
An agentic GTM platform can shape outreach for B2B sales professionals using intelligence tied to recent buying-group curiosity and recent account history so cold prospecting stops sounding copied from the same tired template.
Prospects get context that feels considered, while VPs of Sales and frontline managers alike can easily discern which openings from salespeople earn replies and which introductions keep dying on arrival across new account lists.
6. Developing product launch plans that factor in data and lessons learned from past initiatives
An AI-powered go-to-market initiative scorecard in an agentic revenue enablement platform like Highspot can show you which particular GTM programmes pulled prospect interest, were adopted by sellers so future planning rests on evidence.
Revenue leaders and RevOps analysts can see what hit the mark (think launches of net-new offerings or a product upgrade), and reps can reuse assets, training lessons, and timing choices that held up in earlier rollouts for future initiatives.
7. Determining which value propositions shared in calls and meetings land best with leads
Within leading agentic GTM platforms, sales team members can access AI-generated meeting intelligence to determine which claims made by a given rep sparked interest among prospects and which talking points faded in lead conversations.
At the same time, sales managers get coaching material that can help them ensure all sellers are cognizant of what lands and misses in deal talks so they don’t fall into similar patterns that will only keep them spinning their wheels with opportunities unlikely to convert any time soon (if at all).
8. Crafting customer retention and churn prevention plans using recent client attrition data
Agentic AI can bring account health, product usage, and customer enablement data into one view so renewal planning led by customer success teams rests on fuller context and empowers support personnel to anticipate and act on churn signals before a client indicates they intend to move on.
Account management leaders pinpoint wavering customers sooner, and CSMs can approach expansions with a richer sense of what each one needs based on their most recent interactions with support staff and utilisation of your offerings.
