CASE STUDY · WORKPLACE LEARNING SOFTWARE
Epignosis's own people put 50+ AI workflows to work in three months
Epignosis, the company behind TalentLMS, let colleagues lead an AI rollout built around their real daily work, with Mindstone's support.
The result
12
departments with AI workflows live
from sales and support to finance and product
-
76% vs 23%
still active monthly, a year after the earlier training
Mindstone AI training that came before this rollout, compared with generic AI training. Worklytics data.
-
70,000+
organisations use Epignosis products worldwide
TalentLMS, TalentCards, eFront and TalentHR. Company figure.
-
4
people approved to pilot a new engineering model
fast experiments alongside updating older systems, after the rollout
The challenge
People were using AI. The work itself had not changed.
Epignosis was not starting from zero. Its people had been through Mindstone's AI training, its leaders were paying close attention, and teams were trying new tools across the business.
The risk was that AI would stay a layer of personal experimentation: tools people tried, liked and then forgot, on top of the same old ways of working. Getting one person to adopt a tool is easy. Getting whole departments to change their habits is far harder. The company did not need more enthusiasm. It needed the work itself to move differently, in a way it could see and count.
AI enablement became one of our top strategic goals for the year, not a side project, but how we decided the company should work.
Dimitris DamaskosHead of Business Systems & AI Enablement, Epignosis
What they did
Real work first, led by colleagues
With Mindstone's support, they connected company knowledge, files, meetings, Slack, email, the customer database and finance systems, then built around daily problems.
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Step 1: Real work
Start with the repeated, deadline-driven jobs people dread.
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Step 2: Champions
27 colleagues built workflows on Rebel and showed their teams how to use them.
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Step 3: Shared memory
Save each method into the company's knowledge, so anyone can find it.
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Step 4: Reuse
One person's way of working becomes the whole team's.
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Step 5: Scale
Track who is using what, help anyone who is stuck, then push further.
The first result
A specialist's queue became a seller's own task
Before
Waiting on a specialist
Sellers sent customer questionnaires to an internal specialist and waited up to a week, against the customer's deadline.
After
Done by the seller
One seller completed the questionnaires herself in about half an hour, with the specialist's knowledge built in.
What changed by team
From one person's shortcut to how whole teams work
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Sales
Workflows took over repeated admin, research and follow-up drafting, giving sales development reps time back for selling.
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Security and finance
Security answers cite their sources, with human sign-off. A finance workbook caught a discount-rate method error.
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Knowledge
A colleague later reused a method someone else had saved, with no handover at all.
Where it went next
With work changed inside, Epignosis aimed higher
The change inside the company gave Epignosis the confidence to ask bigger questions, about its customers as well as its own teams.
It began exploring how the same approach could power TalentLMS: spotting knowledge gaps in people's daily work and recommending the right course. It also approved a small task force to try a new way of building software, running fast experiments alongside the work of updating older systems. What started as colleagues fixing the jobs they dreaded had become something the company could build its plans on.
The real return isn't time saved. It's significantly better quality work in the same time.
YOUR TURN
Your people have AI training. Is the work changing?
Epignosis's colleagues rebuilt the jobs they dreaded, then showed their teams how. Find where that pays off first in your company.
- Where AI pays off first in your work
- What teams like yours rebuilt first
- A straight answer on the return
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Sources and notes
- 50+ AI workflows: workflows live at Epignosis; base: Epignosis departments; period: the three months of the Rebel rollout. Measured. Source: case study p.1 ('50+ Agentic Workflows Built').
- 12 departments: departments with workflows live; same base and period as above. Measured.
- Three months: duration of the rollout to the 50+ workflows. Measured.
- 27 colleagues: active champions who built workflows and showed others how; base: Epignosis staff; period: the rollout. Measured. Source: case study p.1 ('27 Active Champions').
- 76% vs 23%: share of people still active each month a year after Mindstone AI training, against generic AI training. Measured by Worklytics; base counts not recorded. External source: Worklytics.
- 70,000+: organisations using Epignosis products. Self-reported company figure; Source: case study p.2.
- 4: people in the task force approved to pilot a new engineering model. Self-reported by Epignosis; Source: case study p.5.
- Up to a week and about half an hour: one seller's time to complete customer questionnaires before and after. Estimated, one client-stated instance. Source: case study p.4.
- Company size band 200-2,000: from 'about 250 staff'. Estimated. External source: City AM.
- Epignosis name and Dimitris Damaskos quotes used with the company's consent. The second quote's original punctuation has been changed from a dash to a comma.