Over the last few months, we’ve conducted a couple of sessions on artificial intelligence as it applies to Sales Operations functions…
The first session was a bit of a primer or fundamentals session, where we explored the basic history and evolution, the different flavors of AI technology available to us and how they’re being accessed and utilized in commercial environments. The most recent session delved deeper into a wide range of real-world sales applications to better understand and expose what separates high-value, high-impact AI use cases from other applications that are little more than slideshow candy.
In both sessions, we highlighted an issue that’s worth calling out as many times as it takes.
Right now, we’re living through an “AI-everywhere-all-at-once” moment. Boards of directors are asking about it. Executive teams are mandating it. Vendors are putting the AI label on anything they can. It feels urgent. It feels transformative. And everything about it feels shiny and new.
But the issue I’m talking about? It’s not new at all. In fact, we’ve been dealing with this issue since business process automation started to gain traction in the late 1960s and early 1970s:
We automate processes that really shouldn’t exist.
A surprising number of sales processes and workflows only exist because of some deficiency somewhere else. We build elaborate bottom-up forecasting processes because we can’t reliably interpret our pipeline data. We create layers of bid desk reviews because our pricing guardrails are too generalized. We bombard decision-makers with nice-to-know information because we lack specifics to recommend. We build approval chains to help enforce discounting discipline because our compensation plans quietly reward revenue at any cost.
And we bolt on reporting layers to monitor selling behaviors in the field because we haven’t aligned our metrics and incentives with strategy.
And now, AI is lowering the development barriers so dramatically that it’s easier than ever to automate all those pesky processes and workflows. In fact, we may no longer need a bulletproof business case, elaborate IT justification, or even a budget override to get it all done. AI makes process automation so accessible that we can skip any and all critical thinking…and therein lies the problem.
Because there’s nothing particularly innovative or transformative about using AI to automate workarounds that really shouldn’t be there in the first place.
So before we use AI to automate any of our Sales Ops processes and workflows, it’s a good idea to take a step back and answer some diagnostic questions:
- When was this process or workflow introduced, and what specific failure or risk was it meant to control or mitigate?
- Does this workflow exist because our data entry and reporting standards are unclear or unenforced?
- Are we layering approvals onto deals because our pricing guardrails lack specificity or clarity?
- If our comp plans were better aligned to our multiple objectives, would we need these monitoring processes?
- Are our forecasting cadences compensating for weak or nonexistent data-driven pipeline predictions?
- Is this reporting layer trying to correct behaviors that our incentives and KPIs are actually encouraging?
- If this workflow disappeared tomorrow, what underlying data, governance, or alignment issue would immediately surface?
- Will automating this process materially improve revenue, margin, or risk management…or just make the same things move faster?
- Are we trying to add more “judgment and insight” after the fact instead of embedding better guidance upfront?
- Are we trying to treat symptoms (exceptions, overrides, delays) instead of addressing why they’re occurring?
- If we rebuilt our Sales Ops architecture from scratch today, would we intentionally design this process back in?
To be clear, I’m not against using AI for Sales Ops, or any other commercial function, for that matter. Quite the opposite, in fact. Used properly, artificial intelligence can help us do all sorts of impactful things that we either couldn’t afford to do before, or that weren’t even possible by any other means.
What I’m cautioning against is using AI to further entrench legacy processes and workflows that may no longer be needed, rather than putting AI to much better and more valuable uses.
Always remember that the height of process efficiency isn’t making a workaround faster or less painful…it’s eliminating the need for that workaround entirely.
Making Sense of AI for Sales Operations
There's no doubt that artificial intelligence will transform nearly every aspect of sales and revenue operations. How do we cut through the hype and harness AI to generate improved sales results and business outcomes?
Sales Process Improvement
In this session, we share how to identify bottlenecks, develop procedures, and prioritize improvements. And, we discuss how to leverage technology to streamline and scale your sales processes.
Exploring Valuable AI Uses Cases in Sales Ops
Some AI use cases barely deliver marginal gains, while others have massive impacts on effectiveness and results. In this session, learn how to separate the high-impact applications from the low-value distractions.
