AI efficiency in go-to-market: How to execute smarter

Highspot’s GTM Performance Gap Report 2026 estimates large B2B companies could generate 8.5% more revenue, if their teams execute more consistently.

Read that again: Eight and a half percent.

For a $500M firm, that’s $42.5 million in revenue sitting on the table.

For a $1B multi-national conglomerate? That figure doubles.

And the lever that moves it isn’t a bigger headcount or a flashier product. Rather, it’s the quality of how your revenue teams operate every single day.

Unfortunately, many of the CIOs and CTOs who have pored over their businesses’ AI spending in recent quarters have seen the investment line climb steeply. (And the returns are somewhere between “Not yet” and “Hard to say.”)

That’s not deterring funding for AI, though.

A 2026 Bain & Company survey found that, while 37% of global organizations missed their cost-reduction targets with artificial intelligence this year, 90% of those same firms are increasing their AI budgets for the years ahead.

The spend keeps rising, but the results can’t afford to remain murky.

In the middle of this doubling down on AI, the build-vs.-buy debate rages on, consuming boardroom hours that could go toward something more valuable: figuring out what existing AI investments are actually doing for workers in the field.

That’s the conversation worth having with your C-suite.

Because it turns out the origin of your AI go-to-market tools matters far less than whether your revenue-generating and customer-facing teams are genuinely using them, extracting real value from them, and translating that value into the consistent, measurable outcomes that appease your entire executive team.

TELUS’ sales and enablement teams use Highspot’s agentic AI platform to move faster, automate repetitive tasks, and build content and training in minutes, not months.

AI systems: Only a competitive advantage when your GTM maturity is high

A 2026 IBM Institute for Business Value report found that, while 80% of technical executives (CxOs) said they’d be given “CEO-driven AI transformation mandates,” just 11% of them think their organizations are “fully ready for the scale of AI agent deployment expected in the next year.”

That readiness gap has a name: low go-to-market maturity.

And for mid-market and enterprise CIOs and CTOs evaluating AI efficiency across their organizations, it’s the single-most-revealing diagnostic available.

The firms getting the most out of their AI investments aren’t necessarily the ones who spent the most or built the most sophisticated in-house models.

They’re the businesses whose revenue teams had the infrastructure to absorb those investments and put them to work (and quickly). Think trusted data, clear ownership, well-documented processes, and leaders who hold the line on adoption to ensure all team members hit the ground running on day one.

Without that crucial foundation, even the most capable AI tools end up operating on shaky ground. In a go-to-market context, that means these solutions:

• Produce output and suggestions that nobody acts on
• Automate sales processes that no seller questioned
• Generate insight that expires in a dashboard few open

Raising your AI readiness level is the prerequisite to realizing legitimate and lasting AI efficiency that, in turn, leads to better go-to-market execution daily (engaging buyers, optimizing campaigns, crafting content, revising training programs) and stronger GTM performance and revenue growth in the long run.

The hallmark of high go-to-market maturity at high-performing companies is:

• Trusted GTM information and data/content governance: Every revenue team operates from verified, consistently updated information that everyone across the organization knows is trustworthy and acts on.
• Consistent execution by all revenue-generating teams: Sales, marketing, and enablement follow shared playbooks and workflows with enough discipline that results become predictable from one quarter to the next.
• Artificial intelligence acting as a true force multiplier: AI solutions amplify what skilled practitioners already do well, rather than compensating for processes that were never sound to begin with.
• Rapid adaptation to changing external market conditions: Revenue teams can reprioritize accounts, adjust messaging, and shift program focus without waiting weeks for leadership sign-off or manual data reconciliation.
• Ability to predict customer demand and leads’ needs: Forward-looking intelligence lets go-to-market teams get ahead of buyer intent rather than perpetually reacting to signals that have already gone cold.
• Oversight by business leaders to track efficiency gains: Senior leaders have clear, real-time visibility into how AI investments are translating into time saved, effort reduced, and outcomes improved across every unit.
• Increasingly higher worker productivity across GTM: Each quarter, practitioners accomplish more with the same hours because routine, low-human-judgment tasks have been systematically removed from their plates.
• Scalable growth and measurable business outcomes: Revenue programs expand across new segments, regions, and channels without requiring proportional headcount increases to maintain execution quality.

The gap between where most mid-market and enterprise firms sit today and what’s described above is where the real AI adoption challenges live. Closing it requires more than a vendor contract or an ambitious internal build timeline.

The orgs that have elevated their AI maturity share one common trait: Senior leaders stopped treating AI as an IT initiative and started treating it as a strategic imperative that can drive stronger B2B revenue performance long term.

That shift in framing changes what gets resourced and measured in GTM and which assets and approaches get retired when they don’t produce results.

High-maturity organizations (those in the Connected or Strategic levels of Highspot’s GTM Maturity Model) know that the AI systems in their arsenal are only as valuable as the humans who understand how to direct them.

Capability without a clear mandate, structured adoption, and honest outcome measurement is capability sitting idle (and, frankly, doing nothing for your firm).

Why the ‘build-vs.-buy’ argument is the wrong question for GTM to ask

When your revenue teams are underutilizing AI tools you’ve bought for them, does it matter whether those tools were built in-house or bought off the shelf?

The answer, almost certainly, is no.

In fact, a number of large firms are already “breaking the pattern,” as it relates to ‘blending’ their build and buy approaches, per Bain, which noted “they are realizing the savings they targeted, deploying [AI] agents with genuine confidence, and funding the next wave from returns that actually materialized.”

These enterprises learned what went wrong by “treating data access, governance, and process redesign as CEO-level problems rather than IT problems,” Bain added. This enabled them to ditch ‘weak’ AI use cases and methods and, instead, take a top-down approach to applying the tech more intelligently.

That’s the orientation shift CIOs and CTOs at scaled B2B orgs must make.

Rather than cycling back to the build-vs.-buy discussion every budget season, the shrewder questions to ask your revenue operations director and AI literacy leads are, “Are our teams really using what we’ve given them? And, if so, is the tech moving the needle on anything that matters to the business?”

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Whether you opt to invest in a purpose-built, agentic go-to-market platform or implement a hybrid model using MCP servers and APIs that connect with other proven AI tools and LLMs like ChatGPT and Claude, your organization will only realize AI efficiency and productivity gains when you:

Identify gaps and opportunities tied to strategic and creative work for each GTM team

Start with an honest audit of where your sales, marketing, enablement, and RevOps practitioners spend the bulk of their time and, more to the point, where they should be spending it.

The AI in sales examples worth replicating all share the same origin story: A leader got specific about which high-value, judgment-intensive work their team wasn’t getting to because lower-order tasks kept filling the calendar.

Pinpointing those gaps is what separates companies that roll out artificial intelligence with purpose from those that deploy it merely with optimism.

Discover which routine tasks should be taken off human workers plates and ‘given’ to AI

Once the strategic gaps are mapped, the next move is cataloguing the repetitive, rules-based work that your practitioners are currently handling manually and that an AI agent could take on without breaking a sweat.

Think CRM updates, meeting recap drafts, content tagging, lead routing, and campaign performance summaries. For most mid-market and enterprise go-to-market teams, this category is far larger than leaders initially estimated.

Every hour recaptured from that pile is an hour a seller, marketer, or enablement manager can redirect toward strategic work that an algorithm still can’t replicate.

Focus on data-quality control, and ensure AI tools ‘speak’ with one another in real time

Sophisticated AI running on unreliable data produces confident, well-formatted, wrong answers. That’s an unfortunate pattern that revenue operations leaders at large B2B organizations encounter regularly when CRM hygiene has been deferred and cross-system integrations haven’t been pressure-tested.

For CIOs and CTOs, this is squarely an infrastructure responsibility.

The go-to-market and revenue teams feeding inputs into your AI tools need clean, current, connected, always-accurate data. What’s more, the platforms themselves need to operate from a shared source of truth rather than pulling from isolated repositories that haven’t been reconciled since last quarter.

Confirm complete AI adoption and ongoing usage by sales, marketing, and enablement

Purchasing an AI-forward solution is the easy part.

Ensuring that every seller, marketer, and enablement specialist on your payroll is using it consistently, using it correctly, and extracting verifiable value from it? That’s the work most tech investments never fully account for.

Your sales department’s AI-powered selling efforts only compound in value when adoption is broad and sustained and makes their work much easier.

Spot-checking utilization data quarterly isn’t enough.

Your innovation and technology executives have to hold the head of GTM operations accountable for ongoing AI solution adoption metrics, the same way your revenue leaders are held accountable for pipeline numbers, because the two are, in practice, directly tied to one another.

Leverage AI in a way that leads to consistent, ‘winning’ business and revenue outcomes

Efficiency gains are worth celebrating, but they’re not the ultimate goal.

The firms that cracked the AI-in-B2B-sales code know productivity improvements are a means to an end: more pipeline, higher win rates, shorter deal cycles, and programs that hit their targets with enough regularity that leadership stops asking whether AI is working and starts asking where to expand it next.

That requires tying every AI-centric use case back to a measurable business outcome from the start. Not after six months of deployment. Not at the annual planning off-site. From day one, so you can ensure you’re seeing tangible results.

What your company can realize with true AI efficiency across go-to-market

Pull back for a second and picture what all five of those practices look like operating in concert across your entire GTM and revenue function.

Every team working from the same verified data. Routine tasks handled without a human lifting a finger. Sellers, marketers, and enablement managers with substantial calendar space freed up for the work that requires their expertise.

And, at the center of it all, a leadership team that can see, in real time, which AI technologies are pulling their weight (and which warrant a hard conversation).

That’s what unquestionable, AI-powered precision looks like at the enterprise level. And the downstream effects on go-to-market strategy are substantial. Sustainable AI efficiency offers a number of benefits for scaled and growing orgs:

• Program, initiative, and customer acquisition cost reduction: Leaner, smarter workflows mean fewer wasted cycles and lower overhead costs tied to manual coordination across revenue teams.
• Stronger customer experience for B2B buying committees: Well-timed, well-informed outreach from sellers who know what a buying group cares about before the first conversation ever starts.
• Faster, smarter, AI-driven decision-making tied to pipeline: Revenue and GTM operations leaders act on current intelligence rather than waiting for reports that reflect last week’s reality.
• Greater ability to find instant answers to pressing questions: Sales, marketing, and enablement can get what they need without derailing a colleague or filing a request with a central team.
• Streamlined marketing, enablement, and sales data analysis: Cross-functional performance reviews take hours instead of days, when the underlying, easy-to-parse data lives in one place.
• Better understanding of AI agent use cases and capabilities: The more your teams use AI purposefully, the sharper their instincts get for where it adds the most business value.
• Comprehension of generative AI best practices tied to deals: Practitioners who work with well-configured AI tools on live opportunities develop a working fluency that compounds across deals.

None of this materializes on its own, of course.

The tech executives and senior ops directors who extract the most from their companies’ AI solutions tend to do two things their peers overlook:

1. They treat adoption as an ongoing discipline rather than a one-time rollout milestone.
2. They build a command center at the heart of their revenue enablement software environment that gives them a clear, continuous read on how teams across sales, marketing, and enablement are persistently engaging with the tools in place.

An agentic AI platform purpose-built for go-to-market teams can serve as exactly that: the center of your GTM universe, where cross-functional activity, performance data, and AI-driven guidance converge into one authoritative view your revenue leadership team can act from with confidence.