Hot or Not: Where AI adoption is adding real value to Go-To-Market teams
- 1 day ago
- 9 min read
Artificial intelligence is changing how companies execute their go-to-market strategy and activities. From content creation and localisation to lead qualification, sales preparation and customer insight, AI-enabled tools are helping GTM teams move faster and do more with less.
But in our experience working with private-equity backed teams, the hype is running ahead of the evidence. The surge of capital into AI has created a wave of vendors claiming to have transformed marketing and sales, often with limited proof of impact. We have written about the hype around AI sales enablement tools and the traps to avoid.
In this article, we focus on where we have seen real world examples of AI creating real value today: nine practical use cases across the customer journey, from marketing through to sales, customer engagement and retention. You can use these to inspire your own adoption of AI in your Go To Market activities.
1) Website content generation and localisation
One of the earliest and most obvious areas where marketing teams have adopted AI is content creation.
In many businesses (especially B2B) organic content production is slow and dependent upon getting time with subject-matter experts, turning their knowledge into something readable, then editing, approving and publishing it.
AI speeds that process up. Rather than replacing expertise, AI is helping turn existing expertise into more usable formats. For example, a 20-minute conversation with a product specialist can become a blog post, a product explainer, a comparison page, a set of FAQs, or a localised version for other markets in a different language.
A successful approach we’ve observed has been less about producing generic blog content at scale and more about creating very specific answers to very specific buyer questions. In a world where buyers are increasingly asking ChatGPT, Gemini or other LLMs for recommendations and explanations, the content that wins is often the content that directly answers the question being asked.
A useful way to think about this is “content atomisation”. Instead of creating one large, broad piece of content and expecting it to cover every possible angle, businesses can break their content strategy down by buyer persona, funnel stage and question type. Each combination becomes an opportunity to publish a precise, useful answer.
A recent blog post by Coppett Hill explains in more detail how LLMs process specific questions in generating their responses.
2) Capture demand generation from AI search
Google’s AI Overviews (AIOs) and LLM queries are an increasingly large part of customers’ discovery journey, which means businesses need to treat them as marketing channels in their own right
This creates two challenges. First, AIOs can reduce the amount of organic traffic that reaches a company’s website by pushing organic results further down the search results page. Second, LLMs may influence buyer perception even when they do not generate a website visit at all. A brand may be recommended, ignored, misrepresented or compared unfavourably before the buyer ever speaks to a salesperson.
As a minimum, businesses should be regularly reviewing how often AIOs appear for their important search terms, whether their brand is cited, how competitors are represented, and how much traffic is coming from LLMs into their website.
For LLM visibility, the measurement challenge is harder. Tools like ChatGPT do not provide the same keyword-level data that Google does. Our suggested approach is to take existing paid and organic search data, isolate exploratory and comparative search terms, and use those as prompts to test how LLMs respond. Be wary of ‘LLM visibility tools’ which generate synthetic prompts without reference to how your target customers actually behave online.
3) Moving to AI-driven bidding in paid search
The core idea is simple: in a mature paid search account, there are too many variables for a person to manually optimise perfectly: keyword, audience, location, device, time of day, conversion likelihood, conversion value, and many more. AI-driven bidding can process those combinations and make more granular decisions about where to spend money.
Below is a client success story, where an AI driven bidding strategy was implemented in their Google Ads account, leading to a 12-month increase of 76% in their ROI through this channel.
The key requirement for successful implementation of AI-driven bidding is good data. AI bidding is only as useful as the conversion and value data being fed into media platforms. Businesses need to make sure they are not simply optimising for the easiest leads to generate, but for the leads that actually create the most commercial value.
4) ICPs: Building target account lists and classifying leads
A recurring challenge for marketing and sales teams is defining who they should really be targeting. Many businesses have a sense of which prospects are more valuable or more likely to convert, but struggle to operationalise that knowledge across marketing campaigns, outbound sales and lead scoring.
We have written extensively about how to define and operationalise your Ideal Customer Profiles (ICPs).
AI tools are making it easier to classify and prioritise accounts (at scale) which match the key ICP attributes of likability (how much we like the customer, from an LTV perspective) and likelihood (how likely they are to convert).
We’ve recently worked with a client using the GTM tool Clay to help with this very problem.
Clay provides company and individual level data in an interface that works like a supercharged spreadsheet. It brings together multiple data providers, AI research agents and workflow automation so sales and marketing teams can build, enrich, classify and act on prospect lists at scale. In practice, it’s used to take a list of companies or contacts, enrich it with third-party data, ask AI to research attributes that are not available in standard fields, and then use those outputs for ICP scoring, targeting, outbound workflows and CRM enrichment.
The client wanted to identify prospects with specific traits, such as international companies with a UK presence or UK-headquartered companies with overseas offices. These attributes were valuable, but not easy to find in a standard data field.
The first implementation of Clay, run by an external agency, performed poorly because the agency had not understood the business context: less than 10% of the results were usable.
Once we had advised on reworked logic, the business was able to identify leads worth twice as much as the average lead, restructure paid search activity, trial outbound sales and improve lead scoring.
This leads to a common-sense principal when adopting these AI tools: the tool is only as good as the implementation.
5) Email outreach and follow-up drafting
Email remains one of the most common ways customers and prospects interact with a business. Whether it is a sales team responding before a deal closes, or a customer success team handling questions after purchase, the speed and quality of email communication have a direct impact on customer experience.
AI is proving useful as a human assistant in this process. The goal here is not necessarily to fully automate customer communication, but to help people respond faster, more consistently and in the right tone of voice.
An online marketplace business we’ve worked with has implemented an AI tool within their CRM system to ensure consistency in the speed and quality of responses to customers during busy points in the year, when performance in metrics had historically dropped.
The tool helped teams maintain a more consistent tone and improve the speed of responses. Among users who adopted the tool, average email handling time fell by 18%, and first-contact resolution improved.
The important phrase is “among users who adopted the tool”. Adoption was not automatic. Some people used it well, while others did not. That became as much of a management challenge as a technology challenge.
For any business considering a similar tool, adoption measurement from the outset is fundamental. It is not enough to buy or build the tool. Leaders need to know who is using it, how often, for what kinds of interactions, and where adoption is lagging.
6) Meeting preparation
This is a familiar challenge for professional services, sales and business development teams. People want to go into a first meeting well prepared, but there is always a trade-off between research quality and time spent.
At Coppett Hill, we’ve taken our own medicine and designed a tool to help with our own business development efforts.
We built a custom GPT designed around the specific types of prospects we meet: private equity investors, value creation teams, and leadership teams at investor-backed businesses. The tool is not just a generic company research assistant. It is structured around Coppett Hill’s own view of go-to-market: how businesses find, win, keep and grow customers.
The custom GPT uses a detailed prompt to guide the model towards the right sources, the right questions and the right output structure. In under a minute, it can produce a useful briefing note or “crib sheet” for an upcoming conversation.
The value here is partly time saving, but also consistency. When different members of a team prepare in different ways, the quality of first meetings can vary. A well-designed AI assistant can create a more consistent baseline, helping everyone ask better questions and focus on the right commercial issues.
7) Sales enablement tools
Call recording and sales enablement tools are already widely implemented, particularly in B2B sales, but the most interesting applications go beyond simply recording calls or generating transcripts.
We recently worked with a SaaS platform with an international sales team. The business faced several common challenges: sellers spread across geographies and time zones, a new sales leader had just started, sales methodology was not being applied consistently, onboarding new sellers was difficult, and the handoff from sales to customer onboarding was patchy.
We’ve worked with clients that have used Jiminny and Gong to not just support sales coaching, but also provide broader insight into competitor differentiation, common win and loss themes, onboarding requirements, customer needs, existing tech stacks and product roadmap opportunities.
AI makes it much easier to extract and structure that information. It can support coaching, improve onboarding handovers, highlight product opportunities and give leadership a clearer view of what is happening in the market.
This can also be beneficial from an investor’s perspective. In our GTM due diligence work, access to structured sales call data can provide much richer evidence on pipeline quality, renewal risk, upsell potential and customer sentiment. That makes these tools valuable not only for operators, but also for private equity investors assessing a business.
8) Drafting RFP responses
For many B2B companies, RFPs are a major part of how they win work. They can also be time-consuming, repetitive and difficult to manage consistently. That makes them a natural use case for AI.
We’ve observed 3 key ways where AI can help:
Triage: Before a team commits time to a response, AI can help scan an RFP and flag whether it looks like a good fit, whether there are obvious red flags, or whether it appears to have been written with a specific provider already in mind.
Answer generation and answer-bank management: Many bid teams already maintain libraries of standard responses, but AI can make those more useful by tailoring them to the specific client, sector, tone of voice or proposal style.
Review and challenge: AI can be used to critique a draft proposal, identify weak sections, highlight unanswered questions, or point out where the response may not be sufficiently differentiated.
The technology does not change the fundamentals of good bid management, but it can make the process faster, more tailored and more consistent.
If a business does not have an official AI strategy for RFPs, there is a high probability that employees are already using AI informally. That creates risk if there are no guidelines, no quality control and no clear view of how AI-generated content is being used.
9) Surfacing themes and sentiment from reviews
Many businesses have more customer feedback than they can realistically process manually: support tickets, contact centre notes, NPS comments, Trustpilot reviews, G2 reviews, surveys, call transcripts and more.
AI is well suited to finding themes in this kind of unstructured qualitative data. Instead of relying on anecdotal feedback or manually reading a sample of comments, businesses can analyse large volumes of feedback and identify recurring issues, questions or opportunities.
A recent client use case was a travel business analysing contact centre tickets. By identifying the most common questions and issues, the business could improve website content, help guides and templated responses. That in turn reduced contact volume, sped up response times, and improved the customer experience.
The same approach can be used with NPS responses. For example, when detractors are asked what it would take for them to give a higher score, their answers can be fed into an AI model that identifies the most common themes. Those themes can then inform product, service and operational improvements.
This use case is also relevant in due diligence. Investors can now interrogate customer review histories in much more depth than before. For marketing and customer teams, that means it is better to understand the themes yourself before someone else does.
Summary
Across the examples we’ve encountered, the strongest AI use cases are not about replacing teams or handing over complex decision-making. They are much more practical: speeding up simple, high-volume processes where teams are dealing with repetitive work or large amounts of information with humans using AI tools and/or agents.
The tool itself is only part of the answer. Implementation matters just as much: teams need a clear use case, good data, training, and a way to measure whether the tool is actually being used.
At the same time, not every AI claim deserves the same attention. We are cautious about fully AI-led outreach, “AI SDRs” and broad platforms promising to automate entire sales or marketing workflows. So far, the evidence is stronger for focused point solutions than for end-to-end automation.
Lastly, businesses should not confuse automation with AI. Rules-based workflows can be very valuable, but they are different from AI. The priority should be choosing the right solution for the problem, rather than adopting AI for its own sake.
The businesses that get the most value will be those that start with specific commercial problems, implement carefully, measure impact, and focus on where AI can make their teams genuinely more effective.
If you’d like to discuss how to choose the right tools, implement them, and measure AI’s usage in your GTM function, contact us.
All views expressed in this post are the author's own and should not be relied upon for any reason. Clearly.
