👋 Welcome to the 50th issue of The Enablement Edge newsletter!

Sit back, relax, and get ready for:

• 🧭 Best Practice – Clear decision rights as part of effective leadership
• 🏗️ Key Concept – The five layers of sales measurement
• 🎬 This Week’s Video – The key to connecting enablement to business outcomes
• ⚠️ Key Mistake – Assessing below the level of the job
• 🖥️ Piece of Tech – Best revenue intelligence software

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🧭 Best Practice

Last week, I came across a great LinkedIn post from Amy McClain, Head of Revenue Enablement, on something I think leaders often underestimate: how much friction we create when people have to reverse-engineer what we expect from them.

Amy used a brilliant analogy involving a robot vacuum repeatedly bumping into a wall until it eventually finds a way through.

Her point was much more human than the analogy would suggest!

If we have a specific view of the outcome, the constraints, or how much freedom someone has to make decisions, we need to make that clear before the work takes place.

Interestingly enough, I covered this very aspect in my recent guide to sales leadership competencies.

One of the competencies I included under talent and leadership development was delegating authority with clear decision rights.

The problem often looks something like this:

A sales manager is told to “own the business”, only to find out by trial and error that more than a few of the following are true:

• Pricing exceptions still need approval
• Territory decisions still need approval
• Hiring decisions still need approval
• Performance interventions still need approval
• Changes to team priorities still need approval

Very quickly, ownership starts to become quite theoretical.

The manager may have responsibility for the outcome, while still having to work out which decisions they are genuinely trusted to make.

That is why, as I suggest in the guide, the boundaries around delegation need to be explicit.

Someone should understand which decisions genuinely sit with them, where input or consultation is expected, and where senior approval is still required.

The exact line will vary by role and organisation.

A frontline manager might have full freedom to redesign their coaching rhythm or redistribute certain accounts, while compensation changes or major commercial exceptions still sit with senior leadership.

What matters is that people know where the boundary sits before they reach it.

There is also a development aspect to consider here.

Managers build judgement by making real decisions, working through the trade-offs, seeing what happens, and getting feedback afterwards.

If a senior leader keeps stepping in and supplying the answer, the immediate problem may disappear, but the manager’s capability has had very little chance to grow.

So Amy’s three questions are a very good place to start:

1. What outcome are we looking for?
2. What constraints need to be considered?
3. Which decisions can they make without coming back to us?

Clear those up early, and people can spend much more of their energy exercising judgement instead of trying to guess what is in their leader’s head.

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🏗️ Key Concept

Over the last six months, I’ve spent a slightly ridiculous amount of time working through sales and GTM metrics.

It started with a fairly simple question: how do all these different kinds of measures actually fit together?

So I started mapping the wider system.

Eventually, I organised it into five interconnected layers:

1. Sales enablement – the programmes, resources, systems, and operating conditions supporting sellers.

2. Sales execution – what sellers and managers actually do across the sales process.

3. Pipeline health – the quantity, quality, composition, and movement of opportunities.

4. Sales outcomes – the effectiveness, efficiency, and productivity of the sales organisation.

5. GTM and commercial outcomes – growth, retention, customer economics, and the wider financial performance of the revenue engine.

From there, each layer kept opening up into another measurement problem.

Sales outcomes became separate frameworks for effectiveness, efficiency, and productivity.

Pipeline split into generation and progression.

Execution led into activity and process.

Enablement opened into training, coaching, methodology, technology, content, and cross-functional collaboration.

Eventually, I ended up producing more than 15 individual guides, each going deeper into the metrics, formulas, examples, and interpretation behind a particular part of the system.

And only after doing all of that did the wider picture really come together.

What I ended up with is probably the most comprehensive and coherent sales metrics framework I’ve come across.

What I find most useful about it, though, is not the sheer number of metrics.

It is having a way to understand where each metric sits and what it can reasonably tell you.

A training completion rate is useful evidence about a training programme.

Pipeline coverage tells you something about whether enough opportunity exists to support future targets.

Win rate tells you something different again.

And net revenue retention sits further out in the commercial system, influenced by sales alongside product, customer success, pricing, and other factors.

The layers give you the context.

They also make it easier to decide where your attention should go.

An enablement leader will naturally spend more time around enablement and execution. A frontline manager will sit closer to execution and pipeline. A CRO will spend more of their time across sales outcomes, pipeline, and broader GTM performance.

And when something changes, you can move into the surrounding layers to understand it more deeply.

This comprehensive measurement framework will help you identify which metrics matter now, which ones help diagnose a change, and where to look next when the headline number does not give you the answer.

I’ve brought the whole system together in my new sales metrics guide, alongside the individual sub-guides that sit under it.

🎥 This Week’s Video

Why do sales methodologies such as MEDDPICC, Challenger, or Sandler lose momentum after rollout?

This video explains how sales processes, manager coaching, reinforcement, tools, and leadership behaviour determine whether a methodology becomes part of everyday selling or gradually fades.

​

⚠️ Key Mistake

One of the easiest ways to overestimate the effectiveness of sales training is to assess something at a much lower level than sellers will need to perform it in the field.

We can use discovery as a good example.

In a real conversation, a seller may need to listen to incomplete information, separate symptoms from root causes, decide which thread is worth exploring, judge whether they have enough evidence to move forward, and adapt their questioning based on what they hear.

There is quite a lot going on, cognitively speaking.

And yet, after the training, we might assess all of that with a multiple-choice question asking them to identify the “best” response from four options.

If they get it right, we’ve learnt something. They can recognise the correct answer when it is presented to them.

What we still don’t know is whether they can make the same judgement while speaking to a buyer, with incomplete information and no list of answers in front of them.

This is where I find Bloom’s Taxonomy very useful.

Bloom’s Taxonomy is a framework originally developed to classify different levels of learning. The revised version organises cognitive activity across six levels:

Remember → Understand → Apply → Analyse → Evaluate → Create

I don’t think of this as a ladder where every training programme should try to reach the top.

Sometimes remembering is exactly what the person needs to do.

The more important focus point is whether the level at which we teach and assess someone matches the level of thinking and action the work will eventually require from them.

A knowledge check may be perfectly appropriate for testing a seller’s recall of product information.

Applying a qualification framework, however, requires practice with a real or simulated opportunity, while the ability to analyse a discovery conversation is best assessed using an actual call recording.

And if the job requires them to decide whether a deal is genuinely qualified, the assessment should probably require some form of judgement rather than recognition alone.

I find this particularly helpful when reviewing learning objectives, because words such as understand, know, and learn can hide quite a lot of ambiguity.

Ask yourself, “What will the seller actually need to do differently at work?”

Once you are clear on that, it becomes much easier to decide what kind of practice makes sense, how demanding the assessment should be, and what evidence you would eventually want to see in the field.

I put together this Bloom’s Taxonomy for Workplace Training template to help you work backwards from what someone actually needs to do on the job, identify the level of cognitive demand involved, and then align the learning objective, practice, assessment, and field evidence around it.

It connects the whole path:

Workplace performance → Learning requirement → Bloom level → Learning objective → Practice → Assessment → Field evidence

🖥️ Piece of Tech

One of the things I found most interesting while researching revenue intelligence software is how similar these platforms can look from the outside.

AI summaries. Deal scores. Risk alerts. Forecasts. Recommended actions.

Peal the first layer of those features, though, and the platforms can be working from quite different evidence.

Imagine a $500k opportunity starts going wrong.

A conversation-led system might pick up that the buyer never showed much urgency, procurement concerns surfaced on the latest call, or the economic buyer still hasn’t been involved.

A pipeline-led system might notice that the close date has moved twice, the deal has spent too long in stage, and its forecast status has changed.

A relationship-led system might see that engagement is concentrated around one champion while other senior stakeholders have gone quiet.

All three are looking at the same deal.

They simply have a different view of what is happening inside it.

A lot of this comes back to where the technology originated. Some revenue intelligence platforms grew out of conversation intelligence, others from forecasting, CRM activity capture, relationship intelligence, or deal inspection.

You can still see some of that heritage in the market today.

Gong, Airspeed, and Avoma have strong conversation-led foundations. Clari has deep roots in pipeline inspection and forecasting. Backstory puts automatic activity capture at the centre of its model. Ebsta leans heavily into relationship and stakeholder evidence. Weflow works much closer to the Salesforce record itself.

Of course, the boundaries are becoming less distinct.

Conversation platforms now forecast. Forecasting platforms analyse calls. CRM-native tools are becoming more sophisticated.

Even so, understanding the evidence layer underneath the product is still incredibly useful when you’re evaluating one.

Once you look at the platform from that perspective, the feature list becomes much easier to interrogate.

I’d want to understand four things:

1. What does it capture? Calls, emails, meetings, CRM changes, pipeline movement, stakeholder activity?
2. What does it connect? Can it reliably tie those signals back to the right account, opportunity, contact, stage, and forecast?
3. What does it infer from that evidence? Deal risk, relationship strength, methodology gaps, forecast confidence, or suggested next actions?
4. And where does that intelligence actually get used? Coaching, deal reviews, forecast calls, CRM updates, executive inspection?

The AI layer then sits on top of all of this.

I go much deeper into how the leading platforms differ, what each one is strongest at, where the trade-offs sit, and which types of teams they are likely to suit in my full guide to revenue intelligence software.

These are my 4 resource picks for this issue from The Enablement Edge Blog:

• ​Sales Leadership Competencies: What Effective Sales Leaders Know and Do​
• ​Sales Metrics: The Ultimate Framework for Measuring Sales Performance​
• ​Bloom’s Taxonomy for Training: Verbs, Learning Objectives & Template​
• ​Best Revenue Intelligence Software: Compare the Top Tools for 2026​

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Federico Presicci - The Enablement Edge Newsletter​
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