Picture the quarterly business review. The enablement slide says 97% of the field force completed launch certification. The next slide, from Sales, says the new indication is tracking behind forecast in half the regions. Nobody in the room connects the two, because nothing on the first slide could have predicted the second.
That gap between what enablement reports and what the business needs to know is the real job description now. In Life Sciences, where a rep may get four minutes with a specialist and every sentence has passed medical-legal review, enablement is no longer about delivering training. It is about showing, person by person, that the field knows the right thing to say when it counts. Many enablement teams already assess and coach well; the seven questions below are a way to check where your own process is strong and where it is thin. Score yourself as you go, using the box further down.
1. Is enablement moving the business, or just moving people through courses?
Many enablement dashboards still lead with what the LMS can count: completions, certificates issued, hours delivered, modules uploaded. None of these are bad numbers. Completion still matters for compliance and for knowing who has been reached. It just answers a different question from the one the business is asking.
The commercial director wants to know something else. Can the respiratory team explain the exacerbation data without reaching for the visual aid? Does the oncology field force know which patients sit inside the label and which do not? Is the new hire in territory 14 ready for a ride-along with the regional manager, or will the manager spend the afternoon correcting her?
Those are readiness questions, and they need readiness evidence. A completion rate tells you someone reached the end of the module. It does not tell you what they kept.
The stakes are highest at launch. McKinsey's analysis of 210 launches between 2003 and 2009, published in 2014, found that about two thirds of new drugs missed pre-launch consensus sales expectations in their first year, and that 78% of year-one underperformers were still underperforming in year two. A launch rarely recovers from a weak start. If enablement cannot show, in the first weeks, whether the field actually knows the story, it is reporting on history rather than influencing it.
The shift is from measuring delivery to measuring what each person knows, on the specific facts that decide a customer conversation, and tracking whether gaps close.
2. Why does the field forget most of the kickoff, and what actually fixes it?
Every enablement leader knows the pattern. Weeks of preparation go into the kickoff or the cycle meeting: new visual aids, objection handling, the phase III data explained by Medical. Reps pass the end-of-day quiz and leave confident.
A month later, managers hear the same questions in ride-alongs. CRM call notes show the old message creeping back. The MSL gets pulled into meetings to cover basics.
The instinct is to schedule another session. Often that repeats the thing that created the problem. In many organisations the weak point is not the training but what happens after it.
This is not new science. Ebbinghaus described the forgetting curve in the 1880s, and Murre and Dros replicated it in 2015 with the same method, a single participant relearning lists of nonsense syllables: most of the loss came within the first day, and savings fell to about 4% at 31 days. It says nothing precise about how much a field force forgets of a kickoff. It illustrates forgetting in that experimental task.
The fix is not more content. It is structured retrieval: short, frequent questions spaced over the weeks after the event, with each person getting the correction for their own mistakes. Asking "how do we train people better?" is less useful than asking "how do we know the critical facts are still there ninety days from now?"
The kickoff is the peak of what your field knows. Without a plan for the weeks after it, the next morning is the start of the decline.
3. Is AI replacing enablement, or changing what enablement is for?
A year and a half after generative AI arrived in every boardroom, most of the conversation in enablement has been about content production. Can it write the module? Can it draft the quiz? Can it summarise the clinical study report?
It can, and often surprisingly well. But speed of production was never the bottleneck that mattered. The bottleneck is that enablement teams spend a large share of their week on operational work: converting slide decks into modules, localising for twelve markets, rebuilding assessments after every label update, chasing MLR sign-off on each variation.
If AI takes that work away, enablement people get time back for the part that creates value: deciding which capabilities the business needs, finding where knowledge is breaking down, and designing interventions that close it.
In a regulated industry there is a condition attached. Anything generated for the field has to come from approved documents only, and a human expert has to sign it off. A plausible but wrong statement about a secondary endpoint is not a productivity gain. It is a compliance incident. The teams getting value from AI in 2024 are the ones treating it as a drafting engine with a human gate, not an oracle.
A fair objection is that AI will soon answer reps' questions directly, so why train them at all? A knowledge assistant can help a rep prepare for a call. It cannot stand in front of the cardiologist for them. In a four-minute conversation the core facts must be fluent; the assistant is there to confirm a detail, not carry the argument.
4. Do we actually know the difference between training, enablement and readiness?
Ask five organisations to define enablement and you will get five answers: onboarding, content, coaching, certification, or simply "our LMS". None is wrong. All are incomplete, and the confusion is expensive, because it lets heavy investment in training sit alongside flat performance without anyone noticing the contradiction.
A working distinction we use:
- Training transfers knowledge. It gives people the information to understand a product, a disease area or a process.
- Enablement makes sure that knowledge gets applied consistently, through reinforcement, coaching, the right content and the right tools at the right moment.
- Readiness asks whether this person knows what they need to perform in front of a customer, today. Knowledge checks can show part of that; role play and ride-alongs show the rest.
The distinction changes what you measure. If training is the goal, success is attendance and completion. If readiness is the goal, those become inputs, and the outputs are accuracy on critical facts, consistency across territories and gaps that close and stay closed.
Readiness only means something if it is measured rather than assumed, which is why we count a critical gap as closed only when the same person gets it right on several later attempts, not once. Even then it is a verified correction within the assessment, not proof of what happens in the call.
5. Are we burying the field in content instead of helping it remember?
Enablement got very good at producing material and quietly bad at helping people use it.
Most companies now have a portal, a library of approved visual aids, recorded congress webinars and a battle card for each competitor.
And still, reps struggle to find what they need at the moment they need it. The issue is no longer availability. It is the distance between information that exists and information a person can use confidently mid-conversation.
The better question is not "what else should we create?" but "what does this person need to remember this week?" That requires knowing what each person already knows. A senior rep with ten years in the therapy area and a new hire from a different franchise should not receive the same reinforcement. One of them needs the new data. The other needs the basics of the mechanism.
In our experience, when reinforcement is aimed at each person's actual gaps, the field spends less time on material it already knows.
6. Is the LMS enough, or are we asking it to do a job it was not set up for?
The learning management system earned its place: structured training, completion tracking and proof of compliance at scale. In Life Sciences, where auditors will ask who completed which policy training and when, that remains essential.
The question is whether your set-up also answers performance questions: which product facts are fading since the last cycle meeting, which regions lost confidence after a competitor's congress data, which managers should coach which objection this week. Some LMS configurations can; many we see cannot. Where yours cannot, the answer is usually a measurement layer next to it, not a replacement. Our guide on upgrading your LMS goes into that in detail.
7. What will the best enablement teams be judged on five years from now?
Every few years enablement reinvents itself. First it was onboarding. Then content management. Then coaching. Now AI dominates every conference agenda. Step back from the tools, though, and a more interesting change is under way: the mission itself is shifting.
A decade ago the goal was a knowledgeable field force. The goal now is a field force that learns and adapts faster than the market moves. Labels expand. Guidelines update. A competitor presents at a congress and the objections change overnight. Access conditions vary by region and by quarter. An annual training plan cannot keep up with that pace, and nobody expects it to.
In that environment, speed of adaptation becomes the measure. How long does it take for a new piece of evidence, agreed by Medical, to become something every rep can explain accurately? How quickly can you tell which part of the field did not get it? Those are the numbers future enablement leaders will be judged on, not the number of programs they launched.
That reframes the role from training coordinator to something closer to the owner of the organisation's working knowledge: keeping it current, keeping it accurate, and knowing where it is thin. As AI takes on more of the repetitive production work, the human side matters more, not less. Technology can automate learning operations. People still build learning culture: managers who coach on the data, a field that sees reinforcement as preparation rather than surveillance, a Medical team that trusts the process enough to sign off quickly.
Where to start on Monday
You do not need to answer all seven questions at once. Pick the next business moment on the calendar, a launch, a kickoff or a cycle meeting, and change one thing about how you prepare for it.
Agree the short list of facts that decide the customer conversation. Measure what each person knows about them before the event, not only after. Plan the four to six weeks of reinforcement that follow, so the event is the start of the work rather than the end of it. And bring the results to the commercial team in their language: which regions are ready, which are not, and what you are doing about it.
This is the work we do at Atrivity, with short competitive challenges that show what every rep knows, a fix sent to each person for their own mistakes, and readiness views by team, country and topic. But the questions above matter whatever tools you use. Take the first one to your next launch meeting and see who in the room can answer it.