95% of enterprise AI pilots fail<\/a> due to a lack of effective integration. As a result, they provide little or no measurable benefit to the bottom line.<\/p>\nBut one company that has worked hard to break this pattern is Epignosis. A workplace learning technology supplier, it employs 300 people who service the needs of 70,000 customers worldwide.<\/p>\n
Last year, its Chief Executive Nikhil Arora mandated that each employee should use AI daily to help augment their work. Everyone was given a subscription to OpenAI’s ChatGPT Large Language Model (LLM) as a result.<\/p>\n
But a key problem came to light: there was no means of understanding how much the tool was being used. There was also no apparent improvement in company key performance indicators either.<\/p>\n
On recognizing that the LLM was mainly being employed as a glorified search engine, the decision was made to bring in AI learning platform provider, Mindstone. The aim here was to offer AI competency training to the entire workforce.<\/p>\n
Tackling employee resistance<\/p>\n
But a process that should have taken three weeks ended up taking about three months due to employee resistance. Excuses ranged from a lack of time to people not seeing the value, or a belief they were already proficient. Dimitris Damaskos, Epignosis’ Head of Business Systems and AI-Enablement and a team of three, explains:<\/p>\n
We realized that just having top management evangelizing AI wasn’t enough. We needed to energize the top and bottom at the same time, and the rest would follow. So, my team was created because AI is nothing without context. We also took AI Champions who already existed, those who already had successful GPTs and we knew were good at AI. Then we said we’re opening up the Champions program to the whole company, so ‘come and help us write the organization’s AI strategy’.<\/p>\n
The point was to make everyone feel included and part of the decision-making process as “people resist change when it comes from someone else”, Damaskos says. His goal was to:<\/p>\n
Make everyone a designer on the change management initiative we were trying to do. So, we got volunteers and promised them extra training, more hours, a change to their job title to include ‘AI Champion’ so everyone knew. Most importantly, I’d written a big AI strategy document for 2026 and beyond, which I threw away. Instead, we started from scratch and said ‘alright, what do we need to do?’<\/p>\n
Every department of the company now has at least one champion, of which there were 15 in total. Their initial aim was to work out how AI could effectively be deployed across the business in a repeatable way to ensure it was embedded into ways of working.<\/p>\n
A personal AI assistant<\/p>\n
On the tools side, meanwhile, Mindstone released an alpha version of its Rebel AI agent orchestration platform in January this year, which it asked if Epignosis would like to test. The system, which was rolled out and onboarded between February and June, provides each employee with a personal AI assistant.<\/p>\n
Each personal assistant is connected to a team- or organization-wide file-based memory bank. This means a new workflow can be developed once, usually by an AI Champion, stored in either a team or company folder depending on its function, and shared with others.<\/p>\n
The assistant runs on each individual’s local machine or device to ensure control over data privacy and storage. But it also integrates with third party systems, including LLMs, using application programming interfaces or Model Context Protocols.<\/p>\n
The upshot is that Rebel learns how each employee – and the wider company – operates. It connects to the applications they use, such as emails, team files, and meeting notes, and undertakes repetitive tasks on their behalf.<\/p>\n
For instance, the system will read incoming messages, highlight what is important, and draft replies for their approval. It will give them a quick meeting summary before they join and afterwards write down action points. When starting a new task, it will also provide the background information required to move forward, although training it to do so inevitably takes time due to initial hallucinations.<\/p>\n
Identifying possible use cases<\/p>\n
To understand how best to deploy the platform, the AI Champions were also asked to identify possible use cases for their specific departments. In a process that took six months, the use cases were then evaluated, the required data sources recorded, and users trained up. As Damaskos says:<\/p>\n
People are using the system in widely different ways: some for automation, some for meeting preparation. Sales and customer success are taking call transcripts and getting Rebel to update the CRM system, so they don’t have to. We’ve also had 20% more demos per account exec since we started using it this year as the AI frees up more time. It’s doing the prep and other boring work, so they have more time to book demos – although we can’t know if it’s 100% due to AI or other initiatives too.<\/p>\n
Nonetheless, it did take some time for employees to accept the system, Damaskos indicates:<\/p>\n
We installed the application for everyone, but initially no one dared use it as they didn’t know what it was. So, we had meetings that were at least 30 minutes long to explain what this new technology was and why it was better than the tools they already had. Because we were doing it with the AI Champions, part of the onboarding was to give examples of what other people in their department were using it for. We also had bi-weekly showcases, where we demonstrated at least one cool AI use case to the whole company.<\/p>\n
One example here was automating childcare benefits. In the past, parents used to manually send their daycare receipts to payroll, which paid out a maximum of E300 per month. But the workflow automation took the requisite PDFs, compiled them into an expense report, and automatically sent them on, saving parents’ time in the process.<\/p>\n
Higher quality work and better employee satisfaction<\/p>\n
For the five percent of employees who are still resisting the platform though, Damaskos recommends simply sitting down on a one-to-one basis and clarifying its benefits. As he points out:<\/p>\n
FOMO [fear of missing out] is a strong motivator.<\/p>\n
But he is also keen to point out that the key focus of using such technology is not about boosting productivity:<\/p>\n
It’s a trap for you to count hours saved. People at the beginning were resistant. They were anxious AI was going to take their jobs. But when they saw the system working, they realized that, while it could take over some part of their role, it would never be able to take 100%. So, once people understood it was there to augment their work, they were happy and were proud to say this only took them five minutes as they knew how to use the AI correctly.<\/p>\n
From February to June, the system’s return on investment dashboard estimates it has saved Epignosis 12,477 working hours. It has also created nearly 2,000 different documents, which include presentations and Excel spreadsheets. But Damaskos believes that a better measure of success than productivity is actually higher quality work and employee satisfaction.<\/p>\n
To work out such metrics, he tracks adoption levels, outputs, and how many additional systems users have connected to their personal assistant. This is because “the more tools that are connected, the better the results”, he says. Users are also asked to record valuable use cases, which “aren’t necessarily the highest impact but do illustrate what AI is best at, such as summarizing transcripts”.<\/p>\n
Making work easier and more satisfying<\/p>\n
The next step, meanwhile, will be to migrate a selection of the organizations’ departmental AI models to the cloud. Two out of a proposed 30 models have already been transferred.<\/p>\n
The idea is that users will not need to ask their own chatbot to prepare for a customer event, for instance. Instead, the information will already be held in the CRM system and “magically appear half an hour before the meeting”, Damaskos says:<\/p>\n
We’re trying to standardize things as change management is a long process that takes a lot of time to do.<\/p>\n
As for key considerations when introducing this kind of technology, there are several. Firstly, Damaskos warns that Rebel is memory-intensive if local processing is required for data privacy purposes. As a result, the company had to replace a lot of computers that were only three to four years old.<\/p>\n
Secondly, commercial LLMs are token-heavy, which is expensive for small companies. As a result, Epignosis switched to using open-source models, GLM, Kimi, and MiniMax.<\/p>\n
Finally, Damaskos says:<\/p>\n
People need to be encouraged to use AI. They need executive evangelism to know ‘it’s OK to use AI, we like AI, you should be proud if you’re using AI’. At the same time, you also need a team that will give you ideas and help you resolve errors in the day-to-day things. So, you need permission from the top and support from the bottom. Our point is to try and automate as much as we can. It’s part of our arsenal to make everyone’s work easier and more satisfying. This is because when people are motivated, they perform better.<\/p>\n","protected":false},"excerpt":{"rendered":"Although all too few organizations appear to be listening, it has become a well quoted fact that AI…\n","protected":false},"author":2,"featured_media":131315,"comment_status":"","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[6],"tags":[179,7493,521,849,35195,14652],"class_list":["post-131314","post","type-post","status-publish","format-standard","has-post-thumbnail","category-agentic-ai","tag-agentic-ai","tag-agentic-artificial-intelligence","tag-ai-adoption","tag-future-of-work","tag-talent-management","tag-use-cases"],"_links":{"self":[{"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/posts\/131314","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/comments?post=131314"}],"version-history":[{"count":0,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/posts\/131314\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/media\/131315"}],"wp:attachment":[{"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/media?parent=131314"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/categories?post=131314"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/tags?post=131314"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}