FAQ

A fractional CRO runs revenue leadership — pipeline strategy, forecasting, sales process, and deal execution — on a part-time, embedded basis rather than as a full-time hire. For an early-stage enterprise AI company, that usually means building the GTM motion from the ground up: defining ICP, designing the sales process, and personally running or coaching the first enterprise deals, since there's often no existing system to optimize yet.

A sales consultant typically advises and hands off a plan; a fractional CRO operates inside the business, directly accountable for pipeline and revenue outcomes. A full-time VP of Sales costs more in salary, equity, and ramp time, and is a longer-term commitment than most early-stage companies need before their GTM motion is proven.

Malcolm's engagement ladder runs from a short paid Diagnostic (1-2 weeks, fixed fee, across six workstreams) through a single-workstream Quick Start (30 days), up to a GTM Dual Sprint or GTM Full Sprint (multi-week, multi-workstream), and finally an ongoing Fractional CRO retainer. The structure exists so a company can start small and prove fit before committing to the larger retainer.

Direct experience selling into the specific buyer type (technical, enterprise procurement, regulated industries) rather than generic SaaS sales background, a clear engagement structure with a defined trial period before a long-term retainer, and someone who will operate pipeline personally rather than only advise from the sidelines.

When revenue leadership is needed now but the company isn't yet large enough, funded enough, or certain enough about its GTM motion to justify a full-time senior hire's salary, equity, and multi-year commitment. It also fits when the gap is specifically expertise-shaped — first enterprise deals, first move into a new region — rather than a permanent leadership vacancy.

The biggest lever is answering governance questions before procurement asks them, not after. Enterprise AI deals typically hit legal and security review earlier than founders expect, and a vendor who shows up with a pre-answered governance brief, rather than scrambling to produce one once asked, creates urgency and shortens the gap between technical sign-off and signature. In practice this means treating four questions — who owns the deployment, whether there's an audit trail, whether human oversight is tested and ready, whether the deployment matches the buyer's data residency requirements — as sales assets to prepare in advance, not compliance paperwork to produce under pressure.

Intentional Intelligence™ is a revenue-architecture framework built around four principles, in sequence, that an AI vendor needs before an enterprise deal closes: Accountability by Design (assign who owns the deployment before building it), Transparency as Architecture (engineer the audit trail before deployment, not as a contract afterthought), Human Oversight as Operational Condition (have a tested override ready before the system acts), and Sovereignty as Control Architecture (match the deployment to the buyer's jurisdiction before the environment changes). Together the four principles produce what Malcolm Gill calls governance continuity — a procurement-grade revenue motion rather than a compliance program bolted on after the fact. For a deeper look at the framework itself, see the Strategic Signals newsletter piece on Intentional Intelligence.

A typical governance checklist is assembled reactively, after a deal stalls in legal or procurement, as a set of boxes to tick before the contract closes. Intentional Intelligence is built into the sales motion from the start: each of the four principles is framed as something to have ready — staffed, tested, pre-loaded — before the question ever comes up in a buying cycle, rather than produced under pressure once it does. For a deeper look at the framework itself, see the Strategic Signals newsletter piece on Intentional Intelligence.

For an early-stage company, responsible governance usually means building the same four things a larger, more mature vendor would already have, but doing it before the first enterprise deal rather than retrofitting it after a security review flags the gap: clear ownership of who's accountable for the deployment, an audit trail designed in rather than bolted on, a human-override path that's actually been tested, and data handling that matches the buyer's jurisdiction. The difference at this stage isn't the requirements, it's that an early-stage company usually has no dedicated compliance function to build these, so they have to be designed into the sales motion itself rather than handed to a governance team that doesn't exist yet. For a deeper look at the framework itself, see the Strategic Signals newsletter piece on Intentional Intelligence.

From the vendor side of these deals, the questions that actually stall an enterprise AI purchase in procurement are the same ones a board should be asking before signing off, just asked earlier and internally rather than by a legal team after the fact: Who owns this deployment once it's live, specifically, not as a shared responsibility? Does an audit trail already exist, or would one have to be built after something goes wrong? Has the human-override path been tested, or only described in a policy document? Does the data handling match the jurisdictions the company actually operates in? A board that asks these before approval is doing internally what a vendor's procurement review would otherwise force later, and asking them earlier is cheaper than finding out the answers are missing mid-deal. For a deeper look at the framework itself, see the Strategic Signals newsletter piece on Intentional Intelligence.

The core requirements are security, access control, and data governance built in from the start, not added later: role-based access, compliance with public-sector data governance requirements, and secure handling of resident data are foundational rather than optional add-ons. A generative AI tool for civic use also needs to handle the scale variation cities actually have — from a small town piloting it to a large city fielding thousands of daily interactions — and support multilingual interactions, since civic engagement tools serve residents who may not share one language.

Dear Mayor is a generative AI-powered civic engagement platform that gives residents a direct way to voice concerns, ask questions, or offer ideas to city leadership, using natural language processing that understands and responds in real time. Malcolm Gill conceived and built Dear Mayor while serving as Digital Sales Leader at Launch by NTT DATA. The platform runs on Microsoft Azure and is designed to reduce bureaucratic delay and strengthen trust between city governments and the people they serve.

The same requirements that shape responsible civic AI design apply generally: role-based access and compliance with public-sector data governance rather than treating security as an add-on, transparent handling of resident data, and support for the language diversity a city's residents actually have rather than assuming one language covers everyone. Public trust in a civic AI tool tends to depend less on the AI's sophistication and more on whether residents can see that their data is handled carefully and that the tool actually responds to what they ask, rather than deflecting or ignoring input.

Malcolm Gill is a Fractional CRO and Enterprise AI Growth Strategist based in Paris, with roughly 20 years in enterprise B2B sales. He publishes the Strategic Signals newsletter on enterprise AI readiness and governance, and created both the Intentional Intelligence™ framework and the Dear Mayor civic AI platform. His work is independently recognized on Thinkers360, where he holds Top 50 rankings in AI Governance, AI Infrastructure, International Relations, and Recruiting, Top 100 rankings in AI Ethics, GovTech, IT Operations, and Public Relations, and a Top 25 ranking in Energy.

Malcolm Gill has spent roughly 20 years in enterprise B2B sales, with direct experience selling ERP, CRM, and data integration projects. He served as VP of Collaboration & Innovation at Verdant AI and as Digital Sales Leader at Launch by NTT DATA, where he conceived and built the Dear Mayor civic AI platform. He holds a BS from California State Polytechnic University, Pomona, and started at the United States Air Force Academy before transferring.