What a data-led sales consultant told us about AI in sales

AI helps a firm win more work when it rests on sound data, does the research and leaves the message to the people who know the client. Those were the points Jane Haack made when we spoke last month, and they match what we see in firms bringing AI into how they sell.

Jane runs IGNAITE Consulting, a sales consultancy for small and medium-sized businesses. Jane did well in B2B sales before training as a data analyst, and now works at the top of the sales funnel, "where marketing sort of stops and sales begins."

Winning work with AI starts with the data

Jane's results in sales came from the numbers. "The only reason why I was so successful in my career," Jane told us, "was because I always looked at the data." "Everyone is screaming AI," Jane said. "Nobody's looking at data." Jane has heard unofficial horror stories of poor data going into AI and "absolute garbage" coming out, the garbage "tenfolded."

When a firm's client records and pipeline are incomplete or out of date, AI turns out poor work faster, and the hours spent putting it right are hours nobody can charge for. Jane sees plenty of promises that AI will do so much that "you will never have to work again", "and it's like, 'Have you thought about compliance?' 'No.' 'Have you thought about data?' 'No.'"

We ask both questions first. Anything in the data, the tech stack or compliance that isn't ready goes first on the roadmap, before any solution is planned.

AI should do the research, and people should write the message

Sound data also gives AI a clear job in sales. "I'm a huge believer in automation is fine, using AI to do research is great, but write your own messaging." Unless the AI has been taught a lot about what you want to say and how it should sound, Jane said, "it's usually garbage."

That split is where the capacity comes from. The research behind a proposal or a follow-up can take a senior person hours, and AI can take on most of it. We hold every solution to the same standard: it can research and draft, and the person who owns it checks what it produces before it reaches a prospect or a client, so the work keeps its standard or gets better. The AI works from the firm's own material, such as how it writes and what it knows about its clients, so the firm owns its knowledge, rents the AI and isn't tied to one vendor.

Teams get behind AI when they can see the hours it saves them

Those hours only appear if the team uses what is put in place. Jane asked us the question a lot of owners are thinking: how do you handle "what I always call the beauty of being human", the people "not being trained on AI, don't want to touch it, their own prejudices"? And Jane put the change the way an employee would hear it: "Imagine you don't have to spend Friday mornings on a three-hour report anymore."

Hours like the Friday report sit in admin, and freeing them lets people hit their chargeable time targets. The rest of a team's time sits inside client jobs, where every job has a time budget and the fee is set from it. When a job runs over, the extra time is written off, so the firm bills less of the time it recorded, and the share it does bill is its recovery rate. When AI brings jobs in inside their budget, less is written off, and the recovery rate, fee income and gross profit rise together. The same team then takes on more clients without adding headcount.

Each solution comes with an enablement pack for the person who uses it, and leadership tells the team from the start that AI is there to support them.

Clients now expect a firm's AI to be safe

Winning work also depends on whether clients trust what a firm does with their data. Jane's niche is cybersecurity, IT services and managed services, firms Jane described to us as "the ones that are shouting security when it comes to AI." They are working out how to change what they offer "so they can ensure that their clients are staying safe with their AI usage".

A firm needs an answer ready when a prospect asks how its AI handles their data. We find out which AI the team already uses, including anything on personal logins, and bring it inside the firm's AI policy. Each solution then runs where its data allows: the AI provider's own cloud only when nothing sensitive is involved, the firm's own server where the provider never trains on what it sends, or a machine in the office where the data never leaves.

To see where client data goes in your own firm today, the quiz below takes a few minutes.

Thirteen results are enough to judge a solution

Once a solution is live, the data shows whether it is working. People who say they have too little data to be useful get what Jane calls the rule of 13: once you have about 13 of one kind, "you can start looking at the data and make actual informed decisions."

Thirteen jobs through a solution, with the time each one took and what needed correcting, tell a firm more than a feeling that it works. The same record shows when a solution starts to drift as the models change, so we check and tweak each one before its quality drops unnoticed.

What firm leaders should do now

Jane's points add up to the order a firm needs to follow, whoever does the work.

Start with the firm's own work, broken down into the individual tasks and processes that create value for its clients, from the research behind a proposal to the Friday report: who does each one, how long it takes, what data it uses and who signs it off.

Before any solution, check the layers those tasks sit on: the business and each department, the tech stack, the quality, completeness and security of the data, compliance and governance, and the people who will work alongside the AI.

Then set out a roadmap of solutions, each fully costed with the hours it frees up, what it costs to implement, its first-year return and what it does to the recovery rate, so the firm's leaders can see the case for each one before anything is spent.

Implement them one by one, in the roadmap's order, each running where its data allows, with its risks designed out, covered by the AI policy and owned by a named person who checks what it produces. Once they are live, keep managing them as the models change, with the firm owning its knowledge and renting the AI.

Done in that order, the firm frees capacity from the same team, wins more work and lifts its recovery rate and fee income, in a way that is compliant and keeps client data secure.

See where your firm stands

Our quiz asks which AI your team uses, where client data goes, what your clients have agreed to and who checks the work. Your report shows where your firm stands today and gives you a 30-day plan to make its AI safe with client data.

If you want our support putting it in place, book a free call.