How GTM Teams Use AI as a Teammate

The common thread is problem-solving. Filmmaking combines technology with storytelling. Technical support, IT administration, sales engineering, partner work and Salesforce administration each reveal how an organisation operates. Moving through those functions at Expedient gave Nick a broader understanding of the company and its departments.

That breadth helps operations professionals understand how moving parts fit together and enable other teams to work effectively. Nick also values tinkering: people should try tools, learn how they behave and fix what they break. This practical curiosity supports problem-solving and offers a distinctive view of GTM challenges.

Although AI has become an overused label, Nick sees value in generative AI. It can process larger datasets and respond to well-framed questions. In B2B, it can identify trends and activities that people struggle to connect manually.

A spreadsheet illustrates the shift. Previously, someone needed to understand a CSV, inspect its data and build charts before drawing a conclusion. A generative AI tool can chart the file or surface less visible patterns. Users still need to understand the task. However, the tool reduces the work needed to turn raw information into a starting point for analysis and action.

A CRM can contain demographic details, correspondence, LinkedIn information, interests and enrichment data about a prospect or customer. AI can combine those inputs with a defined message to produce a tailored first draft. Users then revise its structure, language and tone instead of starting with a blank document.

Operations can consider how to scale that process. Salesforce and ZoomInfo data can inform a ChatGPT draft returned to a marketing or sales engagement platform. Nick still describes the output as a draft for people to workshop. This approach preserves personal judgement while reducing the time spent creating relevant outreach.

Nick sees uses before and after a deal closes. Historical product utilisation can support predictive modelling of future resource needs, including seasonal or daily patterns. A cloud service provider can examine customers' resource use over time. The model can then inform conversations about expected usage instead of relying on broad assumptions.

AI can also assess correspondence for sentiment, tone and interest, then suggest next steps. Modern platforms can combine behavioural indicators into predictive scores with less manual configuration than earlier lead-scoring methods. For an ABM programme, teams must distinguish demographic fit from behaviour that suggests meaningful intent.

Nick says GTM teams should not fully automate decisions. People working in a market have context that a model lacks, including why a signal appeared. For example, universities showing interest in cloud services can reflect students researching projects, not institutions considering purchases. Acting without interpretation would mistake activity for qualified intent.

An orchestrator must therefore retain control. Automation can aggregate data and trigger predefined workflows, but someone must decide which conditions justify action. Customer-facing communication carries greater risk because a false assumption or poorly judged message can damage the relationship. Technology should support decisions, not silently make and execute each one.

Dense technical documentation is hard to search when users lack the exact term or page. Nick describes an internal pilot that turned a large technical knowledge base into a conversational chatbot. Users can ask how to complete a task instead of searching manually or calling support. At the time, he said the team was moving the approach towards existing-client use.

Sales teams can use a similar approach to create draft emails from intent data, conversation history and account personas. Nick cautions against full automation because of security concerns and established selling habits. Adoption improves when representatives receive rough drafts to review. The technology earns trust by helping users while leaving them in control.

Conversational reporting can speed data access. Instead of waiting for operations to build each dashboard, users can request contacts, new pipeline or forecast deals through chat. Nick sees the appeal of tools that turn natural-language requests into reports or visualisations.

However, easier access does not ensure correct interpretation. Established organisations often have technical debt, exclusions and internal definitions that affect reported numbers. A closed-revenue request can omit valid company rules unless the model receives that context. Users already create independent reports and challenge official figures without understanding the definitions. AI can speed reporting, but governance and data literacy remain necessary.

Operations leaders must separate useful innovation from superficial AI positioning. As vendors add AI features, leaders need to identify distinctive capabilities and basic connections to existing models. They must also introduce tools without overwhelming sales teams, making change management as significant as technical evaluation.

Data quality becomes more central. AI systems need accessible historical information that is clean and grounded in the organisation's reality. Automation can reduce overhead and help teams act faster. Nick sees the focus shifting from workflow execution to choosing which actions and interactions matter. GTM Operations therefore moves towards orchestration: selecting technology, preparing trustworthy data and designing activity that helps customers reach successful outcomes.