EXECUTIVE SUMMARY
Most revenue teams are not seeing ROI from AI because they layer AI on top of old human-led workflows.
- Sales productivity now depends on shifting to AI-led workflows supported by humans, not the other way around.
- Distributed teams benefit most: AI removes coordination delays, makes coaching continuous, and standardises execution.
- Leaders must redesign KPIs around decision quality, not activity volume, with AI orchestrating the flow.
THE NEWSLETTER
1) The core problem: AI is being added…not integrated
Revenue leaders face a paradox: budgets for AI are at record highs, yet most leaders quietly admit they don’t see meaningful commercial lift.
McKinsey’s 2024 Global Sales Survey reports that only 11% of sales organisations say AI has materially improved productivity at scale.
Gartner finds that 6 out of 10 GTM leaders “cannot explain how AI improves execution in their operating model.”
Deloitte observes that most AI adoption is still ‘activity substitution’, not workflow redesign.
It is clear that teams add AI to their existing model instead of redesigning the model around AI.
And nowhere is this more evident than in distributed revenue teams.
2) Distributed teams will struggle most
Hybrid and remote revenue teams face four structural limitations:
a) Fragmented communication
Teams work across different tools, communication channels and time zones.
b) Inconsistent execution
Every rep interprets playbooks differently, and managers can’t monitor all deals.
c) Coaching gaps
Sadly, HBR reports that managers spend only 14% of their time coaching, despite being the top productivity driver.
d) Slow judgment cycles
Decisions (i.e.: pricing, forecasting, prioritisation) take too long because information and data is scattered.
AI doesn’t fix these issues when it sits on the side but when it runs the workflow, these constraints can truly disappear.
3) Shifting from human-led to AI-led workflows
Here is the structural change happening in leading organisations:
Old model: human-led workflows supported by tools
- Humans initiate tasks
- Tools help with productivity (think CRM, calendars, notes, templates etc.)
- Managers drive process compliance (if they know the process themselves)
- Information flows upward through manual reporting
- Coaching is episodic
- Forecasting depends on human judgment and activity logs
New model: AI-led workflows supported by humans
- AI initiates tasks, sequences work, and routes information
- Humans handle exceptions, decisions, and relationships
- AI enforces process, removes variance, and eliminates “dead time”
- Information flows downward as real-time recommendations
- Coaching becomes continuous
- Forecasting becomes probabilistic and machine-updated
This shift is not theoretical, and BCG’s 2024 research shows companies redesigning workflows around AI see 30 - 50% faster cycle times and up to 20% growth in rep capacity. Significant revenue upside alongside productivity uplift which leads to an improved bottom line.
4) Here’s a research-backed model showing an AI-led workflow:
(I didn’t create this but myself but felt it was worth sharing a simplified version to the technical ones floating around)
Layer 1: “AI orchestration”
Consider the AI as the central brain. It observes signals, predicts next-best actions, and triggers workflows automatically.
Good for deal prioritisation, pipeline risk alerts, up/cross sell flags, renewal probability to name a few examples.
Layer 2: “Human decision”
Humans step in for judgment, exceptions, and relationship moments. The leader’s role shifts from policing tasks to improving decision quality (more strategic).
Layer 3: “Automated execution”
AI handles repetitive tasks like research, summarisation, personalised messaging (although I’m a bit split on this one!), meeting prep and follow-up, CRM hygiene.
These tasks no longer rely on rep discipline which we know varies rep to rep.
Layer 4: “Operating rhythm”
AI reshapes the team’s calendar and weekly reviews become real-time alerts, pipeline meetings shrink, coaching becomes ongoing, forecasting becomes dynamic rather than scheduled.
This is where commercial productivity is unlocked.
5) To illustrate, I’ve put this simple table together showing what changes in daily execution:
Before (human-led workflow) | After (AI-led workflow) |
A rep starts their day: | A rep starts their day with a single AI-generated brief: |
- opens CRM | “Here are the 2 deals at risk” |
- figures out which deals need attention | “Here are 5 outreach sequences generated for today” |
- writes email outreach | “Here is your meeting prep pack for later on” |
- prepares for meetings | “Here is the sentiment shift from yesterday’s calls” |
- updates notes |
|
- guesses where risk sits |
|
Energy goes into sorting, not selling. | Information flow to them, not from them. |
6) Leadership implications to all this…
1) Leaders shift from inspection to enablement
AI surfaces risks and patterns in real time so managers spend more time coaching judgment, less time asking for updates.
2) Consistency increases across distributed teams
AI removes the variance created by geography, manager style, and rep interpretation.
3) “Always-on” coaching becomes possible
AI delivers feedback on every call, email, and meeting automatically and with consistency.
4) Forecasting becomes a leadership asset not a stressful chore
AI reduces “happy ears” by grounding forecasts in behavioural and historical data.
5) Time allocation shifts toward customer impact
Leaders and reps reclaim hours previously spent coordinating and can refocus these doing what they love: solving customer’s problems.
So where does that leave us ..
In an AI-led workflow world, the new Leadership question becomes:
“Are we making higher-quality decisions, faster and more consistently, across every part of the revenue engine?”
Organisations that answer “yes” will win.
Redefining workflows to be AI-led should already be work in progress by now. If not, you’re right to be worried. Competition is out there more than ever.