Seasoned Marketer: AI in Professional Services Marketing and BD: Where It Works, Where It Doesn’t and What’s Next

The conversation around AI in professional services has shifted. The question is no longer “should we use AI?” but “where does it actually move the needle?” For marketing and business development leaders at accounting and consulting firms, this distinction matters. Professional services operate on trust, long buying cycles and deep relationships, a fundamentally different environment from consumer-facing industries where AI-driven personalization and automation have already scaled.

Organizationally, AI excels at pattern recognition, research synthesis and content generation. But BD in professional services still closes on human connection. The opportunity lies in knowing where each strength applies.

A Simple Framework for Where AI Makes Sense

Before evaluating any tool, consider two dimensions: volume and repeatability (how often a task recurs) and relationship sensitivity (how much the task requires human judgment, political awareness or trust capital). AI delivers the strongest return on high-volume, lower-sensitivity work: research, prospect list building, first-draft content, cross-sell pattern flagging and proposal scaffolding. AI is not the answer for closing a deal, navigating a difficult client conversation or reading the room at a pitch.

Putting AI to Work

The following use cases each illustrate a different way AI can support BD in professional services. Each is evaluated on what it does, why AI works in that context, what success looks like and what your firm needs in place to get started.

Sandler Training — From Roleplay to Revenue

Business development training in professional services has a retention problem – most of what gets taught in a workshop is forgotten before the next client conversation. If skills don’t show up in real conversations, the training didn’t stick.

Sandler Training has built an AI-powered coaching ecosystem designed to address that gap. Tools like Fathom NoteTaker score sales calls against Sandler methodology in real time, feeding insights into an AI roleplay tool where practitioners can practice the exact scenarios they struggle with on demand. Coming next is an AI-driven assessment layer that identifies individual skill gaps, delivers targeted content and routes each person back into roleplay for deliberate practice. AI works here because it ties development to real calls, real gaps and real repetitions, making feedback immediate and personalized rather than generic and delayed.

As Karl Graf, President of Sandler Training (results driven by Trustpoint Management Group), puts it, the goal is shifting firms from accountability to ownership. In practice, that means professionals preparing for critical conversations before they happen rather than relying on post-call coaching after opportunities are lost. AI is what makes that shift scalable.

Success looks like practitioners actively rehearsing difficult conversations, BD coaching that doesn’t require a manager to track every rep’s progress and measurable improvement in conversion rates over time. For firms to get there, three things matter: a willingness to invest in BD development beyond the annual retreat, leadership that models the behavior by using the tools themselves and openness to AI-assisted coaching as a complement to human mentorship.

Propense.ai — Cross-Sell Pattern Recognition

Cross-sell patterns are buried in data most firms already have but are not analyzing. Propense.ai analyzes existing client data to surface cross-sell and expansion opportunities, identifying which clients are statistically most likely to buy additional services before they come to you. AI works here because the signals exist at a scale that relationship owners would miss entirely. LBMC, one of the firms using the tool, frames it this way: traditional CRMs were built for sales teams to track prospects through the sales progression – Propense applies that same concept to the client, delivering account-based planning for each client at an individual level, informed by knowledge across the entire firm. Its proactive approach allows partners and shareholders to manage their books of business more intentionally and strategically, which LBMC sees as a direct path to growth. LBMC has also worked with Propense to build a custom firm-level report that ranks total cross-sell potential by service, giving marketing and firm leadership a clear view of where the biggest opportunities sit. The impact shows up in individual client conversations too – LBMC shared that one of their Client Account Managers, prompted by a Propense.ai signal, surfaced a service offering he would not have thought to raise on his own, and it converted into a win. The result is more proactive conversations with existing clients, shorter cross-sell cycles and higher wallet share per relationship. Unlike traditional solutions, Propense does not require a fully integrated CRM or extensive data preparation – access to your time and billing system is sufficient to get started.

S+H Consulting — Using Data Intelligence to Optimize Pricing and Pipeline

Finance and accounting firm S+H Consulting (part of Smith + Howard) has spent more than three years building on the Microsoft Power Platform — Power BI, Power Automate and Power Apps — and recently expanded into Microsoft Fabric with Azure and SQL servers. Their approach treats business intelligence as the essential foundation for meaningful AI. As their team puts it, structured data is what produces consistent AI results; without it, AI is guessing.

Internally, S+H is using AI for event extraction to manage their BD calendar, resume scraping and analysis for talent pipeline work, and converting meeting transcripts into Gantt charts for project planning. AI works here because these are high-volume, repetitive tasks that eat into time better spent on client relationships. The efficiency gains free the team to focus on higher-value work.

On the client-facing side, the most compelling application is their revenue intelligence work: dimensionalizing revenue and connecting it directly to CRM opportunities. Their AI-driven tools surface tactical insights like penny-per-unit price impact analysis, customer pacing acceleration and sales team optimization, guiding teams where pricing inflection points exist. A recent client tool built a sales pacing dashboard by customer with automatic outlier identification, performance comparison across sales team tiers, and mix analysis showing what top performers do differently from the next tier down.

Success looks like clients making faster, data-backed pricing and pipeline decisions instead of relying on gut instinct. For firms looking to embed AI into optimizing long-term and recurring revenue, S+H’s model shows that the real leverage comes from getting your data structured first, then layering intelligence on top.

What AI Can’t Replace

Trust is still built person to person. AI can enable the conversation but it cannot replace it. Judgment about when to reach out, how to frame a message and what not to say requires experience AI cannot replicate. Clients in professional services hire people, not tools and the relationship owner remains the differentiator. There is also real risk in over-relying on AI efficiency signals like open rates, click-throughs and propensity scores at the expense of qualitative relationship awareness. Before scaling AI adoption, firms should also address safety, security and cultural readiness. The reframe is simple: AI should make your best people more effective – not serve as a substitute for them.

Key Takeaways for Marketing and BD Leaders

• Start with the workflow problem, not the AI tool. Identify where your team loses time or misses signals first.
• AI earns its keep in research, content scaffolding and pattern recognition – not closing.
• The firms winning with AI are investing in data quality and change management, not just software licenses.
• Measure what matters: time saved, pipeline coverage and cross-sell conversion – not just adoption rates.

The seasoned marketer advantage is knowing which AI signals to act on and which to ignore.