Your company bought ChatGPT Enterprise. You've got CoPilot licences for the whole team. Someone from marketing even built a prompt library in Notion.
Leadership asks: "What's our AI maturity level?"
And someone confidently answers: "We're doing well. Everyone has access."
That's the myth. And it's costing you competitive ground.
Because maturity isn't about what you've purchased. It's about what your team actually does, repeatedly, with measurable impact.
Here's what the myth looks like in practice:
A life science tools company spends a small fortune on AI subscriptions. They announce it in the all-hands meeting. IT sends login credentials. Marketing creates a Slack channel called #ai-experiments.
Three months later, the CFO asks for ROI.
The answer? "Well, people are using it for emails..."
That's not maturity. That's expensive spell-check.
Our ELRIG Drug Discovery 2025 survey revealed this pattern across 107 exhibitors: 37% have AI tools but no structured programme. The result? 69% of individuals in those organisations don't use AI daily despite having access.
Meanwhile, companies with structured programmes - even small pilots - see 53-73% daily usage.
The difference isn't the tool. It's the system around it.
Real AI maturity operates on a ladder, and most companies are stuck on the bottom rungs pretending they're at the top.
What it looks like: Individuals use ChatGPT for personal productivity. Maybe someone writes better LinkedIn posts. Perhaps a sales rep uses it for prospect research. But nothing is documented, shared, or measured.
The test: Ask three people on your team to describe their most valuable AI use case. If you get three completely different answers with no common thread, you're here.
The risk: Innovation happens in shadows. When that person leaves, their productivity hack leaves with them. You're building on sand.
What it looks like: Someone (usually marketing or a motivated individual) creates a library of prompts. "Use this for competitive analysis." "Try this for email personalisation." There's a shared resource, but adoption is voluntary.
The test: Open your prompt library. When was it last updated? How many people contributed? If it's been static for six weeks, you're not really at level 2.
The risk: Documentation without adoption is expensive theatre. You've created a museum, not a workflow.
What it looks like: Specific teams run focused experiments with clear metrics. Sales tests AI for battlecard updates and measures time saved. Marketing pilots AI-generated content variations and tracks engagement lift. Results get presented to leadership monthly.
The test: Can you name three active pilots, their success metrics, and who owns them? If you have to think about it, you're not here yet.
The benefit: This is where ROI becomes measurable. You're no longer guessing. You're testing.
What it looks like: AI isn't a project. It's infrastructure. CRM auto-enrichment runs daily. Competitive intelligence monitoring happens continuously. Content creation workflows include AI steps by default. New hires get AI onboarding as standard.
The test: If you turned off your AI tools tomorrow, what would break? If the answer is "nothing critical," you're not operationally integrated.
The difference: Companies at this level don't debate whether to use AI. They debate which AI to use for which task.
What it looks like: AI capabilities become part of your value proposition. You're faster, more personalised, or more data-driven than competitors because of how you've embedded AI. Customers notice the difference even if they don't know it's AI-powered.
The test: Would a competitor study your workflows to understand your advantage? If your AI usage isn't defensible or difficult to replicate, you're not here.
The reality: Less than 5% of life science companies operate here. The ELRIG data showed only 7% as power users across organisations.
The gap between Level 1 and Level 4 isn't technical. It's operational.
Data quality blocks progression. You can't operationalise AI if your CRM data is inconsistent, your customer segments are outdated, or your competitive intelligence is scattered across email threads. Our survey found 44% cite data quality as their primary barrier - ahead of budget, skills, or technology concerns.
Governance paralysis freezes pilots. Legal wants policies. IT wants security reviews. Compliance wants approval frameworks. Meanwhile, individuals are using consumer ChatGPT anyway because the official tools are locked behind approval processes. 36% cited governance concerns - not as reasons to avoid AI, but as reasons they can't scale it.
Skills gaps create uneven adoption. Power users race ahead. Light users dabble occasionally. Non-users watch from the sidelines. Without structured training, the gap between enthusiasts and everyone else widens daily.
The brutal truth? Even in organisations without formal programmes, 38% of individuals experiment personally. The revolution is happening with or without leadership's permission. The question is whether you're capturing that innovation or letting it stay in the shadows.
Here's what the data shows about progression:
Power users are twice as excited about AI as regular users (57% vs 28%). Non-users? Zero per cent excited.
Sentiment follows usage, not the reverse.
You don't get confident and then start using AI. You start using AI and then get confident. But only if you create the conditions for small, repeatable wins.
Companies stuck at Level 1-2 lack those conditions. They have tools but no wins. Access but no outcomes. Enthusiasm but no evidence.
The path forward isn't more webinars about AI's potential. It's creating structured opportunities for people to experience value firsthand, then systematically scaling what works.
Most companies overestimate their AI maturity by at least one level.
They call themselves Level 3 (pilots) when they're really Level 2 (documentation without adoption).
They claim Level 4 (operational) when they're really Level 3 (pilots that haven't scaled).
This optimism isn't harmless. It prevents the honest diagnosis required to progress. If you think you're operationally integrated when you're actually running ad hoc experiments, you won't invest in the data quality and governance frameworks that operational integration requires.
The companies winning at AI aren't the ones with the biggest budgets or fanciest tools. They're the ones who accurately diagnosed their starting point, focused on one level at a time, and measured progression ruthlessly.
The ELRIG survey exposed the execution gap. The data showed 68% optimistic but only 7% winning.
That gap exists because most companies are solving for access when they should be solving for adoption.
The companies that close this gap won't do it just by buying more tools. They'll do it by honestly diagnosing where they are, building structure around what works, and measuring progression relentlessly.
AI maturity isn't about the tools you own. It's about the systems you've built, the wins you can prove, and the capabilities you can repeat.
We've built a diagnostic specifically for life science commercial teams navigating AI adoption. It takes 5 minutes and assesses your actual maturity level across five dimensions: usage patterns, structural support, data readiness, governance frameworks, and impact measurement.
You'll get:
No sales pitch. No gate-kept results. Just an honest assessment of where you are and what to do next.
Because the first step to maturity is admitting where you actually stand.