Two Groups Talking Past Each Other
Here's what I'm seeing in the market: a significant and growing gap between sales leadership's AI knowledge and the AI tools and agents being brought into companies.
Side 1: Leadership
Founders, sales leaders, and executives are too busy with their day jobs to fully experiment with AI and keep up with all the tools and trends. The speed of change is genuinely impossible to track when you're also running a company.
Side 2: GTM Engineers
A new class of operators who can build chatbots, content agents, list-building tools, and automated outbound workflows - and who are capitalizing on the hype with companies that feel they need all of these tools to stay competitive.
GTM Engineers aren't necessarily tying agents to prioritized use cases. Companies end up with a collection of AI tools acquired through "drive-by" selling - where there's zero alignment between the tool and the actual business problem it's supposed to solve. A graveyard of half-baked agents, wasted spend, and zero measurable impact.
And the leaders and executives responsible for AI ROI don't have enough AI experience to evaluate which tools to adopt, or the bandwidth to measure which use cases will actually yield productivity and efficiency gains.
Why This Gap Exists - and Why It's Getting Worse
The AI tooling landscape is expanding exponentially. New models, new agents, new workflow tools every week. Even dedicated practitioners can barely keep up. For a founder or sales leader with a full-time job, staying current is impossible.
Meanwhile, the incentives for GTM Engineers and RevOps tool vendors are to sell - not necessarily to validate that the tool solves a real, prioritized business problem. The result is companies acquiring AI capabilities they don't know how to use, measure, or connect to the workflows that matter most.
"Startups don't need more AI tools as much as they need someone who can connect business drivers and use cases to the right AI solutions, measure the before-and-after, and actually deliver productivity and efficiency gains."
What Closing the Gap Actually Requires
Start with Business Drivers
Before evaluating any AI tool, identify the specific business problem you're trying to solve. Is it tied to a KPI you already track? Name it. Vague problems produce vague AI deployments and zero measurable ROI.
Prioritize Use Cases
Not every workflow deserves AI. Score use cases by impact, feasibility, and accuracy requirements. The highest-priority candidates are high-frequency, high-effort tasks where AI assistance has already been proven elsewhere.
Match Tools to Use Cases
Only after you've defined the problem and prioritized the use case should you evaluate tools. The question isn't "should we use AI for this?" - it's "which solution best addresses this specific, prioritized use case?"
Measure Before and After
Baseline the current state before deploying any AI: time, cost, error rate. Measure after. Publish results internally. This is the only way to separate actual ROI from the feeling of having done something with AI.
Frequently Asked Questions
The AI business impact gap is the disconnect between executives who are too busy to evaluate AI tools and measure their impact, and GTM Engineers building and selling AI agents without connecting them to prioritized business use cases. Companies accumulate AI tools that don't tie to business drivers, can't prove ROI, and create a graveyard of half-baked agents with wasted spend and zero measurable impact.
GTM Engineers are a new class of operators who build AI-powered workflows and agents for sales and marketing functions - chatbots, content creation agents, list-building tools, data enrichment pipelines, and personalized outbound systems. The challenge is that GTME skills are primarily technical. They know how to build tools but aren't always focused on connecting those tools to the highest-priority business use cases that will deliver measurable ROI.
Start by identifying business drivers and use cases before evaluating tools. Define success in business terms: revenue lift, cost reduction, or cycle time improvement. Then evaluate tools against the specific use case. Measure before and after adoption to capture actual impact. The goal is not more AI tools - it's connecting the right solutions to high-impact use cases and proving the improvement with data.
Experienced sales leadership bridges the AI business impact gap by combining business acumen with AI knowledge. They understand a company's business drivers well enough to identify where AI can deliver the greatest gains - and have enough AI experience to evaluate tools, recommend solutions, and measure before-and-after impact. This combination of business context and technical AI knowledge is what most companies currently lack.
Seeing the Same Disconnect in Your Org?
If you're accumulating AI tools without a clear connection to business drivers - or trying to figure out where AI can actually move the needle - let's talk. A focused 30-minute conversation is usually enough to identify the highest-impact use cases to start with.
Book a 30-Minute CallNo commitment. No pitch. Just a direct conversation about bridging the AI impact gap for your team.