Service area 5 of 5
AI-enabled GTM transformation
AI expenditure and experimentation are failing to translate into reliable commercial performance. Companies run many pilots and can show little measurable revenue impact. Tools are bought without a data foundation, so output is generic or wrong. Sellers do not adopt the tools. There are no clear policies on AI-generated content or on customer data in AI systems. Job roles are unchanged while AI changes the work. Brands are invisible in the AI assistants buyers now use to research. AI products are priced like traditional software, so revenue grows while gross margin falls.
The evidence in my databank points to the same gap. Gartner expects AI agents to outnumber sellers ten to one by 2028, yet fewer than 40 percent of sellers say agents improved their productivity, and finds chief sales officers who overhaul data, automation and user experience five times more likely to gain a return on AI. McKinsey research cited in the databank reports that 62 percent of organisations are experimenting with AI agents while only 39 percent report measurable EBIT impact. Gartner also predicts that by 2028, 90 percent of B2B buying will be AI-agent intermediated.
The gap between AI deployment and AI value in GTM is a workflow, data, role and governance problem. That is where I work.
I start from the revenue problem, fix the data and workflows underneath, redesign the roles around the new work and measure the result in pipeline, win rate and cost of sale.
What I do
Who this is for
Economic buyers: CEO; CRO or CSO; CMO; CFO; Chief AI Officer, CIO or CDO; VP Revenue Operations; Chief Customer Officer; CHRO (role redesign); General Counsel or DPO (governance); founders and product leaders of AI companies.
Typical situations
- A CRO or CIO with AI tools in place but no data foundation and no measured value, who needs a prioritised use-case agenda the CFO will fund.
- An enterprise commercial leader who owns the number while AI changes how it is produced, and needs a costed plan for the roles of people and agents that the CFO and board can approve.
- A CMO whose content operation is a bottleneck and whose brand does not appear in the AI assistants buyers use to research the category.
- A Chief Customer Officer whose customer success coverage is too thin for the long tail of accounts and whose churn signals are missed.
- A General Counsel or DPO who needs GTM-specific rules on customer data, AI-generated claims and regulatory exposure (EU AI Act, GDPR, India's DPDP Act) that sit between legal advice and tool configuration.
- A CEO or Head of Digital whose offers, pricing and buying process are not usable by AI buyer agents.
- A founder, CPO or CRO of an AI-native company, or a software company adding AI features, with urgent pricing, packaging and margin questions.
- A global capability centre taking on AI-enabled revenue operations for headquarters.
How this connects to the other areas
AI-enabled GTM transformation applies AI where it improves the commercial system built in the other four areas. The business case sits with Business strategy & performance. Buyer-agent readiness and AI product pricing extend the choices made in GTM strategy & growth. The revenue data model and cadences from GTM execution excellence are the foundation every agentic workflow depends on. The role, capacity and compensation redesign that AI requires is delivered with GTM organisation, talent & enablement.
Most engagements in this area begin with a readiness and data assessment rather than a tool decision, because the value of the tool is determined by what sits underneath it.
Engagement formats
- Executive briefing (single session): AI in the revenue engine for a leadership team or board, with the questions to put to vendors and internal teams.
- AI readiness and opportunity scan (typically four to eight weeks): maturity assessment, data readiness and a prioritised use-case agenda with value cases.
- Pilot and proof-of-value programme (typically eight to sixteen weeks): one to three use cases piloted with agreed success criteria and measured results.
- Multi-use-case agentic rollout (typically twelve to twenty-four weeks): workflow redesign, context layer, implementation oversight, governance and role redesign across two or more revenue processes.
- Fractional AI leadership a senior GTM AI leader on a part-time basis while the organisation builds its own capability.
- Retainers for AI search visibility and GTM engineering where ongoing operation is preferred to a one-off project.
Packaged offers that start in this area
- AI-ready revenue engine (CRO / CIO): prioritised AI use cases on a working data foundation, with measured pilot results.
- Human and agent revenue team redesign (CEO / CFO / CHRO): redesigned processes, roles, capacity and pay plans with a costed transition.
- Agentic commerce readiness (CEO / CMO / Head of Digital): machine-readable offers and pricing; agent-ready buying process piloted.
Outcomes the economic buyer can expect
- Lower cost of sale and more pipeline per seller.
- More seller time with buyers; faster cycles; better forecast accuracy.
- Visibility where buyers now research, including AI-assisted search and buyer agents.
- Governed, compliant AI use in customer-facing work, with faster approval of new use cases.
- Revenue teams designed for work shared between people and AI agents.
- AI products and features that earn revenue in proportion to the value they create.
Executive briefing
Start a strategy conversation
We can discuss where I can help and what an initial engagement could cover.
- I begin with a focused discussion of your business priorities.
- Diagnose what is stalling growth.
- Identify the constraint across all or any of the five areas.
- Design a strategy blueprint.
- And align the changes that matter.
What to expect
- 01An initial assessment of the challenge and its commercial implications.
- 02A proposed scope of engagement where there is a clear fit.
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