Key Takeaways
- Many Chief Innovation Officers at scaled B2B companies across industries are adopting a ‘build-and-buy’ approach to their AI technology stacks for go-to-market teams. This method combines internal engineering and development resources with outside AI solutions so firms avoid the full cost and risk of building everything from scratch alone.
- While general-purpose AI like ChatGPT and Claude are increasingly popular among B2B go-to-market functions, enterprise CIOs are now pairing these tools with dedicated systems designed to handle deal-specific work. That blended setup keeps daily execution grounded in accurate, constantly refreshed account context and pipeline data.
- By procuring ‘horizontal’ AI and agentic platforms purpose-built for go-to-market, B2B CIOs and other technology decision-makers can equip every customer-facing team with the capabilities needed to win more opportunities and grow revenue. Doing so helps large businesses scale faster while keeping their spend manageable.
Inside just about every large B2B enterprise organization today you’ll find Chief Innovation Officers and IT leaders working diligently behind the scenes.
Their goal: Establish and optimize a best-in-class AI stack for their go-to-market teams:
- The CIO runs AI evaluation as a security and infrastructure exercise, prioritizing governance, integration standards, and licensing terms. That protects the network, yet often leaves sales, marketing, enablement, and customer success functions holding tools nobody on the revenue side asked for.
- The IT director spends their time approving point solutions function by function: a chatbot here, a forecasting model there—whatever sellers or marketers requested next. Each usually passes a security review, but nothing in that process asks how the pieces would work as one, cohesive GTM system.
The venn diagram of their objectives overlaps a lot, but by working in siloes to assess the merits of building in-house machine learning models and investing in external AI applications, they inadvertently miss the forest for the trees.
Reconfiguring their shared AI tech stack rarely (if ever) calls for a complete overhaul.
Rather, it requires these forward-thinking business leaders to grasp what their C-level colleagues—notably, their CRO, CMO, and VP of Sales—need from their AI tooling and to let that list of must-haves guide every infrastructure choice.
Where too many innovation and IT executives go wrong, though, is believing said infrastructure must be built or bought instead of a combination of the two—an approach that can amplify go-to-market execution and increase their GTM maturity.
AI tech stack FAQs
Why must enterprise CIOs adopt a 'build-and-buy' approach to create a high-performing AI tech stack for go-to-market teams?
Blending homegrown AI development with outside platforms gives enterprise CIOs stronger go-to-market results than betting on one single lane. Purely internal builds struggle to match fast-moving AI models, and purchased tools alone often miss a firm’s sales history. This combination keeps proprietary differentiation intact and still delivers continuous updates from external experts.
How can CIOs ensure outside AI investments for go-to-market integrate seamlessly with other agentic and generative AI tools?
Smooth connections between outside AI purchases and go-to-market efforts start with enterprise CIOs demanding open integration standards before signature. Compatibility with autonomous agents and generative models already active inside the organization outweighs any single AI platform’s individual features. Vendors offering native connectors, documented APIs, and shared permission layers make that technical harmony achievable without custom engineering.
What are best practices for Chief Innovation Officers evaluating the external AI space to find ideal go-to-market solutions?
Approached as an ongoing discipline and not a single purchase event, outside AI evaluation gives enterprise CIOs the strongest outcomes. A recurring review of vendor roadmaps, security practices, and go-to-market fit keeps that AI assessment from growing stale. Peer references collected from other adopters and side-by-side output comparisons reveal which platforms merit future investment.
How should CIOs work with go-to-market and revenue leaders to build a concrete AI framework and strong GTM technology stack?
When shared AI priorities take shape from the earliest planning stages, enterprise CIOs partner most effectively with go-to-market and revenue leadership. Joint workshops mapping buyer journeys, seller workflows, and success metrics translate abstract AI goals into a workable technology roadmap. That collaboration produces a resilient operating foundation grounded in actual deal data, not assumptions.
What key components of horizontal and purpose-built AI systems must B2B CIOs and IT leaders factor into AI vendor research?
Integration depth, data grounding, and governance controls top the list enterprise CIOs weigh when choosing a go-to-market AI platform. Domain-specific tools bring built-in knowledge about deals and buyers, and all-purpose AI models offer broader reasoning without that context. Checking security certifications and published connection APIs separates dependable partners from short-lived point solutions.
How can CIOs at B2B enterprises determine the total cost of ownership of building AI solutions for go-to-market versus buying AI?
The sticker price for a go-to-market AI project tells enterprise CIOs little until engineering hours, maintenance, and security review get added in. Homegrown development multiplies that starting figure several times, and a purchased AI subscription keeps spending predictable and capped. Comparing both paths using identical cost categories shows which fits a firm’s budget best.
What are the benefits for B2B enterprises of combining purpose-built, agentic go-to-market technology with custom AI innovation?
Pairing autonomous go-to-market platforms with custom AI development gives enterprise CIOs faster time to value than either path alone. Ready-made intelligence handles the common selling motions, freeing engineering teams to focus AI resources on what sets a firm apart. That division of labor accelerates competitive differentiation and keeps proprietary logic firmly under company control.
What advantages do agentic go-to-market systems offer B2B enterprises that in-house AI models and horizontal AI tools don't?
Internally built models and general-purpose AI tools alone fail to deliver capabilities enterprise CIOs unlock for go-to-market execution. Direct connections to CRM records, meeting transcripts, and content performance let an AI layer reason about a specific active opportunity. That depth of context helps sellers act on the correct signal at the right moment, not generic advice.
How can B2B enterprise revenue teams blend general-purpose AI like ChatGPT and Claude with purpose-built AI for go-to-market?
When each tool has a distinct job, enterprise CIOs blend general AI like ChatGPT and Claude with specialized go-to-market platforms. Off-the-shelf AI handles broad drafting and brainstorming, and dedicated systems ground recommendations in a firm’s own deal data. That division frees revenue functions to move fast on routine tasks and trust harder calls to smarter technology.
Where should enterprise CIOs start with building an AI tech stack for their sales, marketing, enablement, and revenue teams?
The starting point for enterprise CIOs is mapping existing data sources and workflows spanning sales, marketing, enablement, and revenue functions before selecting any artificial intelligence platform. That inventory reveals where a unified go-to-market AI layer can replace five separate departmental purchases and keep content and coaching consistent no matter which group logs in.
Why enterprise CIOs and IT leaders must ditch the ‘build-vs.-buy’ mindset regarding AI for go-to-market
Despite significant investment in internal and external AI tools, only slightly more than half (53%) of enterprise revenue leaders report consistent execution outcomes, according to Highspot’s 2026 GTM Performance Gap Report.
The 2026 Gartner® research “How CIOs Build, Buy and Partner to Scale AI Capabilities” lands on a blunt but important point: “Treat AI sourcing as an operating model decision rather than a technology purchase. Acquire AI capabilities and partners that you can govern, orchestrate and evolve at scale, otherwise you will accumulate AI debt faster than business value.”
That research title is worth reading again: build, buy, and partner.
We see that Gartner treats partnering as its own path, distinct from simply securing software off a shelf—one where the vendor stays engaged to help your firm govern, integrate, and evolve its AI technology as it matures.
Most artificial intelligence investments fail for a simpler reason than a bad platform pick: Nobody charged with choosing AI builds the connective tissue that turns a tool into an actual working part of—and trusted partner for—go-to-market.
Acquiring tech your team has no path to run, adjust, or plug into everything else (buyer engagement platforms, marketing automation systems, etc.) racks up what that same research calls ‘AI debt‘ faster than any ROI ever shows up.
The way to avoid this dilemma is straightforward:
- Skip the false choice between building your own AI and machine learning muscle or handing the whole project to a vendor. Smart CIOs treat this as a blend, not a fight.
- Keep horizontal AI from OpenAI and/or Anthropic your sellers already open every morning to guide prospecting and active-opportunity interactions, and bring in a dedicated partner that speaks the language of pipeline, forecasting, training, and coaching. (That is, the stuff no general-purpose model was ever trained to prioritize.)
- Invest in and embed an agentic AI layer purpose-built for GTM teams like yours that can read deal signals, spot the right coaching moments, and update a digital room without anyone lifting a finger. Output quality jumps once guidance carries actual deal context.
That same layer should plug straight into your CRM, content library, and other crucial business systems your sellers, marketers, and enablement specialists depend on daily.
When this seamless integration takes hold, you avoid ending up with yet-another disconnected tool fighting for a go-to-market practitioner’s attention.
Land on fine-tuned models trained against your own account and deal history, lean on a partner’s ready-made AI that ‘speaks’ directly with your existing sales tech stack, and you can combine broad reasoning with judgment built specifically for go-to-market work.
TL;DR: The enterprise CIOs and IT leaders pulling ahead already know it’s not ‘build vs. buy’ but rather ‘build and buy.’ That pairing is what turns a pile of formerly standalone solutions into a high-performing B2B revenue engine.
10 action items for CIOs and IT leaders when building AI tech stacks for their GTM and revenue teams
The 2026 Gartner research found organizations that report successful AI initiatives invest up to four times more (as a percentage of revenue) in foundational areas, such as data quality, governance, AI-ready people and change management, compared to those that experience poor outcomes from AI.
That GTM performance gap explains why some CROs settle for soft targets while others blow past their revenue goals from one cycle to the next.
Getting there means you must move beyond exploratory artificial intelligence pilots and (finally) treat this groundwork as the whole game plan:
- Deploy advanced yet intuitive AI agents wired into your existing systems.
- Anchor everything to one agentic source of GTM truth every team trusts.
Here’s a helpful checklist that today’s CIOs and IT leaders can use to build proprietary AI models, invest in AI solutions tailor-made for GTM, and combine the power of both approaches to empower their customer-facing and revenue-generating teams:
1. Reconcile the true, multi-year cost of building, buying, and partnering on AI solutions
Every technology comparison, whether it leans on building, buying, or a blend of the two, looks tidy in a single column. The real picture emerges a year in, once maintenance, security review, and the ongoing tug of an engineering team toward other AI projects join the ledger.
- A pure build path routinely lands at several multiples of what a purpose-built platform costs per year, after you count what it takes to keep pace with new models, integrations, and the invariable need to add user seats and licenses.
- A pure buy path can look cheap, until fragmented point solutions with niche AI force you to purchase two or three additional tools to close the gaps left behind.
- A partner path splits the difference: It costs more than a bare subscription but less than a full build, since the vendor absorbs the work of keeping the system current, governed, and connected to whatever your GTM organization already runs.
Map all three paths for the whole life of the contract before signing anything.
2. Quantify the full cost of ownership with AI, not just the license or subscription fee
The figure on the vendor’s quote is the smallest line on the full bill.
Behind it sits rollout and training time, prompt and agent maintenance as underlying models change, security review, ongoing integration upkeep, and the support burden of fixing whatever breaks after launch.
Ask AI GTM technology providers for an itemized view of every one of those categories before you weigh a subscription fee against a build estimate.
Companies that neglect this step often discover their build path counts salaries alone while their buy path counts the invoice alone—an unfair comparison stacked against whichever option got the fuller accounting.
Price both options using the identical yardstick—rollout, maintenance, security, integration, and support—to reach a (relatively) apples-to-apples comparison.
When comparing horizontal AI, specifically, find a provider willing to do a hybrid pricing structure that blends fixed-base and usage-based costs, not a flat per-seat fee that keeps climbing regardless of whether the tool moves pipeline.
3. Verify accountability when an AI suggestion damages a client relationship or deal
Ask a pointed question before any artificial intelligence platform goes near an active account: “Who answers for it when a recommendation backfires, an account owner sends the wrong asset, or a forecast badly misses?”
Governance, permissions, and the ability to trace a decision back to its source have topped CIO priority lists for several years running, and for good reason.
In the Gartner note, we saw a fix: a standing AI governance council (an AI center of excellence) that pulls IT, legal, and revenue leaders into the same room to test every new use case against trust, risk, and security management standards before it touches a live account.
An AI go-to-market platform worth trusting keeps an audit trail explaining why it suggested what it suggested, respects role-based access, keeps anyone outside the right circle from acting on sensitive accounts, and builds in a human checkpoint before anything customer-facing goes out the door.
Disregard that checkpoint, and one bad AI-generated recommendation becomes a relationship you spend a long stretch repairing.
4. Examine exactly which data sources feed the AI model and how current they remain
Question vendors on exactly what feeds their respective AI model before you trust a single recommendation. Some tools lean on structured CRM fields alone—pipeline stage, deal amount, last seller outreach—and call that ‘intelligence.’
Meaningful signals live somewhere richer: call-recording transcripts, email threads with active opportunities, content engagement, seller training records, and buyer activity spanning every touchpoint a deal generates.
A sizable sales force carries out thousands of events—meetings, messages, and other digital and real-world interactions—routinely each month (sometimes each week).
An AI model limited to just your CRM misses many of them.
Push further, and ask vendors how current that data stays.
A system updating hourly gives you a living, breathing picture of GTM performance, whereas one refreshed on a lag hands you outdated info dressed up as a fresh answer.
5. Demand interoperability with agentic platforms and models your org already uses
Refuse to buy anything unable to talk to the rest of your stack.
Whatever GTM-specific layer you add should slot into the agent ecosystem you are already assembling, whether that is Copilot, Agentforce, or custom agents you built on Claude or ChatGPT, not another disconnected app fighting for a login.
Look for open APIs, native connectors, and support for emerging protocols like MCP servers that let GTM intelligence show up wherever a seller, marketer, enablement specialist, or RevOps analyst already works, not forcing them to open a separate tab.
The best test: Press any prospective vendor to name three specific tools they already connect to, and dig into how deep that connection runs in practice.
6. Determine who retrains, monitors, and retires an AI system as your business changes
Every AI system, however capable on day one, needs a dedicated, named owner for what happens in the days, weeks, and months after launch.
Someone has to handle model training as your market shifts, someone has to police access control as headcount grows and roles rotate, and someone has to catch it when the system falls short of optimal performance and needs recalibrating.
Bypass this before signing, and you’ll inherit an orphaned AI solution nobody wants to touch a year and a half down the line, still running on assumptions from a very different stage of your go-to-market operations.
The stakes run deeper than routine upkeep, too: Gartner projects, “Most agents built before 2028 will need replatforming or rebuilding by 2030, creating demand for partners that provide migration, rightsizing, and long-term ModelOps and governance services.”
So, any contract signed today must name who leads that maintenance and migration.
Select a full-time AI owner, retraining cadence, and retirement trigger as part of the vendor contract itself, not as an afterthought once something breaks.
7. Separate genuine GTM execution intelligence from low-context AI advice and insights
ChatGPT, Claude, and the like will happily draft a polished email or brainstorm a discovery question, and both deserve a permanent home on sellers’ desktops.
What neither chatbot can do without first-party data grounding pulled from your actual accounts, deals, and content performance is tell you why a given opportunity went cold or which asset swayed a similar buyer the last time one showed up.
That gap between clever wording and grounded advice is where deals fall apart unnoticed.
Ask any AI system—before you trust its output—whether the answer came from pattern matching drawn from the open internet or from your own history.
One sounds convincing. The other carries accountability for the outcome: It can point to the exact account, call, or email behind the suggestion.
8. Attribute pipeline and revenue directly to specific, AI-influenced actions and decisions
Your B2B revenue organization’s AI adoption rate alone proves nothing.
A dashboard showing a hundred sellers logged into a given tool in your AI technology stack tells you people showed up, not necessarily that a single deal moved as a result.
Actual proof means tracing a specific recommendation, content asset, or coaching nudge to a specific stage change, closed-won deal, or dollar of pipeline.
Getting this up-to-date intel depends on the underlying platform reading structured and unstructured data in tandem, pulling stage and amount from the CRM while also reading the call transcript that explains why the deal moved.
Do this right, and your whole GTM function starts to execute faster and smarter, with everyone able to see, in plain terms, what works and what’s noise.
9. Benchmark each AI tool’s built-in domain expertise against general-purpose AI
Never trust a slide deck to tell you which particular AI systems understand your sales motion the ‘most’ and which are merely guessing with confidence.
Build a short evaluation rubric covering accuracy, context awareness, and how actionable each answer is, then run comparative trials feeding the identical deal, account history, and question to a specialized tool and a general-purpose model side by side.
Then, turn the strongest AI contender into a bounded proof of value: a real GTM workflow, a fixed timeline, and success criteria agreed on before the pilot starts, not judged after the fact.
Whichever platform claims a spot in your GTM tech stack should win that comparison every single time, not simply look impressive in a canned demo.
10. Trace all AI guidance back to a permissioned, current, and approved content source
Every recommendation your system hands a seller should trace back to a single, identifiable, approved piece of content, never a stale deck somebody forgot to retire two years ago.
Making that possible rests on unglamorous plumbing: monitoring metadata lineage that shows where a GTM resource came from and when it was last approved.
In other words, keeping tabs on tags that categorize everything consistently enough for the system to reason about it and tracking role-based access control that keeps sensitive material out of the wrong eyes is vital.
Miss any one of those three and operational performance suffers in specific, painful ways.
An approved battlecard from a year ago could reach a seller who shouldn’t access it, or a new sales hire ends up leveraging a C-suite pitch deck they’re nowhere near ready to use.
Get this foundation right first, and AI advice that follows will be trustworthy.
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Gartner, How CIOs Build, Buy and Partner to Scale AI Capabilities, Stewart Buchanan, 28 April 2026.
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