Key Takeaways
- Conducting an AI readiness assessment can help your go-to-market (GTM) and revenue leadership align investments, set realistic priorities, and avoid scaling tools faster than teams and governance support.
- Implementing AI without grounding it in data and strategy slows innovation, while clear ownership, sequencing, and outcomes ensure leaders invest in initiatives that scale value instead of noise over time.
- Your GTM AI strategy succeeds when your tech and data infrastructure is ready to equip and guide sales, marketing, and enablement teams with systems built to scale, adapt, and succeed across growth cycles.
Every organization—from early-stage startups to established enterprises—is building out a new (or evolving their existing) AI plan. The harsh truth, though, is very few of these businesses are ready for it to deliver what they expect.
The technology is clearly cutting-edge. However, artificial intelligence only aids go-to-market teams when systems, people, and data are already dialed in.
“While AI has transformational potential, its success depends entirely on the readiness of the system it supports,” per Highspot’s GTM Performance Gap Report.
As our AI study indicated, when embedded into a “coherent, well-governed GTM model supported by clean data, clear ownership, and consistent reinforcement,” the use of AI for sales, marketing, and enablement “scales coaching, personalizes engagement, and surfaces risk before it becomes a loss.”
If your goal is to equip, guide, train, and coach your reps, marketers, enablement specialists, and RevOps analysts so they can capably take advantage of all AI has to offer and, in turn, collectively realize more transformative results from a GTM perspective, the first step is to assess your AI readiness level.
That includes aligning your GTM roadmap to the skills already in play, value your org delivers, and metrics that shape your strategic priorities.
A well-defined AI strategy works best when it maps cleanly to your core business objectives—and you know exactly where you’re starting from.
AI readiness FAQs
How do I accurately assess AI readiness across GTM?
Start with a structured evaluation across people, systems, and overall go-to-market operations. Review workflows, data sources, governance models, and role adoption patterns. Score consistency, scalability, and operational fit to establish a clear baseline before funding AI adoption and expansion.
What metrics best indicate AI readiness in sales orgs?
Look at AI adoption depth, go-to-market workflow coverage, data reliability, governance adherence, and time-to-value across different sales motions. Strong AI readiness shows up in repeatable usage patterns by GTM teams, clean inputs, and consistent application across roles and regions.
How does poor data impact overall AI readiness levels?
Weak data creates unreliable outputs, uneven adoption, and operational drag on your go-to-market teams’ day-to-day work. Incomplete CRM records, outdated lead tagging, or inconsistent fields reduce trust and limit scalability, forcing GTM teams to spend time fixing inputs instead of advancing execution.
Who should own AI readiness strategy in our GTM org?
Ownership belongs with a cross-functional leader who is empowered to align sales, marketing, enablement, and revenue operations. Central accountability ensures shared standards, consistent execution, and complete alignment between investment decisions and strategic business priorities.
How does AI readiness affect enablement program ROI?
How AI ready a go-to-market organization is determines whether revenue enablement efforts scale or stall. Strong AI foundations allow rep training, content, and coaching programs to extend reach and consistency, while weak readiness leads to underutilization of sales tools and erratic GTM execution.
What risks come with overestimating our AI readiness?
Overestimation leads to premature rollouts, low adoption, and wasted spend. Go-to-market teams end up facing workflow-related strain, data governance gaps, and trust erosion when their AI-powered systems outpace their operational maturity, in turn slowing long-term value realization.
How do we assess agentic AI readiness across GTM?
Evaluate whether AI-oriented workflows, permissions, and data structures support autonomous guidance across go-to-market roles. Agent readiness requires clear ownership, consistent inputs, defined boundaries, and operational acceptance across sales, marketing, enablement, and operations.
Why understanding your go-to-market org’s AI readiness ‘score’ is essential
Implementing AI is undoubtedly a strategic directive you and others overseeing go-to-market have from your business leaders (and perhaps board members).
But before you can concern yourself with researching best-in-class agentic and generative AI tech and eventual AI implementation, you need to take a close, hard look at where your GTM function stands today in terms of readiness level.
By doing so, you get insights that can inform future AI deployment:
- A clear index score gives GTM leaders a comprehensive view of readiness, which can help them prioritize the right people, programs, and systems without second-guessing what’s working and what needs a rework.
- Your go-to-market directors can identify gaps prior to implementation of new AI sales tools, avoiding wasted time, shelfware, or rollout delays caused by underlying issues in structure, skills, or adoption readiness.
- Scoring based on a readiness index reveals how teams’ AI use aligns with current and future business priorities, helping GTM ‘owners’ course-correct before investing more time, budget, or attention in disconnected initiatives.
- Evaluating AI readiness is an important component of any revenue enablement strategy, as it helps you tie existing and upcoming AI initiatives to training, content, workflows, and coaching moments that deliver measurable value.
- An AI readiness score helps you pinpoint potential AI opportunities with the most upside so your company isn’t distracted by every new feature and, instead, invests where it matters most across your go-to-market strategy.
- Figuring out how ‘AI ready’ you are provides GTM leaders with the knowledge to focus on what will accelerate revenue, adoption, and retention, not just react to the latest shift in AI technology or wait for a C-suite mandate.
As Accenture’s 2025 Pulse of Change report explained, the artificial intelligence ‘winners’ in the years ahead will be “those that pair bold innovation with thoughtful enablement,” ensuring every single go-to-market function is prepared to make the most of the emerging technology in their day-to-day work.
“Success requires recognizing AI transformation as fundamentally a human challenge—ensuring workforce development, clear communication and ethical leadership evolve at the same pace as technological capability,” per Accenture.
And your starting point for this path to transformation is taking an AI readiness assessment that clearly lays out your strengths and opportunity areas so you have a blueprint for what to change to GTM operations today so you thrive tomorrow.
What to look for when assessing your GTM teams’ AI readiness level
We get it. It’s enticing to skip most (or all) of your AI readiness assessment and just assume your go-to-market is fully ready to make the most of the tech. That’s especially true of B2B go-to-market leaders with orders from execs to move quickly to onboard and leverage AI for a variety of GTM use cases.
But this can lead to investment in point AI solutions.
“The pressure to adopt AI drives go-to-market teams to hastily procure tools in isolated silos, leaving them with fragmented capabilities and limited visibility into AI’s impact,” Forrester analysts recently wrote. “Without a strategic foundation, teams struggle to demonstrate value and scale AI with confidence.”
That’s why the importance of a comprehensive AI readiness audit can’t be overstated. By taking the time and energy to conduct one, you can effectively:
Evaluate your current AI infrastructure to spot gaps in scale, security, and GTM alignment
Before you load up on shiny new AI tools or overhaul your sales tech stack, zoom out:
- Do the AI technologies you already have in place play nice with one another?
- Can they scale without tripping over compliance or crumbling under pressure?
- Is your setup flexible enough to support future GTM shifts and new workflows?
- Are your security protocols aligned with how your org shares data internally?
A few duct-taped integrations and a half-baked pilot won’t cut it. Solid infrastructure is table stakes for successful AI adoption in the months ahead. If it’s fragile, fragmented, or built on sand, now’s your chance to fix it before you fund it.
Scrutinize your go-to-market data quality across sales, marketing, and enablement workflows
If your data’s out of date, misaligned, or filled with gaps, the AI models in your tools of choice won’t do what you hired them to do. Clean records and consistent taxonomy aren’t glamorous, but they’re the bedrock for every system you run.
The success of your sales workflow automation setup depends entirely on well-labeled content, aligned CRM fields, and solid handoffs from marketing to sales. Want predictive analytics that’s worth a damn? Start by making sure your inputs are tight. Then, keep them that way with processes built for scale, not patchwork.
Review your AI governance to ensure compliance, security, and responsible use at scale
Governance gets messy fast, especially once multiple teams start pulling from the same sales tech stack. If access isn’t well-defined, control systems aren’t enforced, and your databases are open season, problems are guaranteed.
Any enterprise that’s serious about responsible AI utilization needs a data governance model built for accountability. Decide now who gets to see what, who approves access, and who manages oversight. One shortcut here, and the whole thing starts to wobble.
Dedicated policy beats ad-hoc governance every time.
Audit how well artificial intelligence integrates with other business-critical GTM tools
No one needs another disconnected dashboard. Your AI integration without context becomes a distraction. The stack should be working harder, not multiplying the manual lift.
Ask whether your systems talk to each other—CRM, LMS, CMS, content management, training, reporting. If not, start there. The foundation matters more than the flash.
Key findings from your company’s AI readiness index assessment should help you streamline, consolidate, and—ideally—connect to a purpose-built agentic GTM platform like Highspot that doesn’t bolt things on, but builds them in.
Benchmark change readiness across GTM roles and functions, not just tech maturity
You’ve got sales enablement owning sprints, sales leads juggling forecasts, and marketing trying to predict next quarter. Everyone’s got different pressures and different thresholds for adopting new systems.
So, map each squad’s AI readiness.
Don’t assume operations = willing or reps = slow. Measure and score, then build out programs that meet people where they are. Continuous monitoring over time reveals how the entire GTM org evolves—and where the next unlock might be.
This isn’t just about tools. It’s also about how people—your sales, marketing, and enablement personnel— absorb and apply new, better ways of working.
Examine your GTM culture’s preparedness level to move from insights to action at scale
New technology alone won’t carry the load. Without shared goals, urgency, and rhythm across teams, even well-funded AI projects stall. Culture matters.
- Do you have a shared mindset and appetite for meaningful operational change?
- Are execs actively demonstrating adoption in workflows, not just announcing it?
- Have you built the right incentives to motivate teams to shift daily workflows?
- Is data being reviewed, discussed, and acted on, or buried in another dashboard?
Explore how strategic priorities are shared, big-picture decisions are made, and each team’s time gets spent. The importance of AI readiness isn’t theoretical. It’s the difference between progress and gridlock between launch and lift-off.
How to build an AI strategy that factors in your readiness and business goals
Successful AI adoption is, without a doubt, a collaborative effort across leaders of your customer-facing and revenue teams. Some proven best practices to abide by—notably, ones that can ensure you fully understand your current AI readiness and use that insight to map out your AI GTM strategy—include:
- Defining your AI maturity level before making investments that outpace your ambitions. Choose initiatives your teams are ready to absorb now—not next year—so your roadmap reflects readiness instead of wishful thinking and wasted budget.
- Tying every artificial intelligence investment directly to a measurable business outcome. Every dollar spent should map to pipeline, productivity, or retention—otherwise, it’s just another experiment waiting to be questioned in a budget review.
- Ensuring AI integration enhances, not complicates, GTM workflows. If it doesn’t embed directly into how SDRs sell or how product and content marketers work, it’s just more tech that looks smart but slows everything down behind the scenes.
- Developing a hiring and upskilling plan to close AI talent gaps across GTM. Don’t just train end users. Re-skill the builders, operators, and drivers of your AI solutions so the tech becomes part of how people work, not a layer simply bolted on top.
- Establishing a strong data collection model that fuels precise, personalized AI outputs. Decide which AI signals matter, where they’ll live, and who owns them. Then, create rules to ensure your AI models get smarter, not messier, over time.
- Framing GTM transformation as the start of your AI ‘era,’ not a solution rollout. Remember: You’re not buying features. Rather, you’re rewiring the way people plan, execute, and scale decisions that shape pipeline, productivity, and performance.
Arguably the most important step in your AI readiness journey is to invest in a single-source-of-truth agentic platform with a native AI and analytics engine.
Specifically, you need one that offers AI-powered GTM insights, AI sales role play tools, AI-generated conversation intelligence, and other AI-centric capabilities that can simplify your teams’ work and enable your reps to show up smarter, move faster, and close deals with greater efficiency and predictability.
