What happened when we started implementing Copilot
The world right now is hyper focused on AI. Love it or hate it, you’re likely feeling the pressure to start using AI to drive value, save time and prevent getting left behind by the rest of the world.
But the biggest barrier you need to overcome is knowing where to start. If you have limited knowledge with AI, it’s tricky to understand how it will fit into your processes, what it takes to be successful and how to even implement it.
Part of the problem is knowing the tools to use – and user-friendly, accessible tools like Copilot are solving that challenge. But the other half of the equation is finding out what use cases work best for your business. And that requires experimentation.
This is a process we’ve gone through ourselves, testing Copilot across functions and processes and monitoring results. Here’s what we’ve learned so far.
Saving 30+ hours on recruitment admin
One of the first use cases we tested was implementing Copilot into our recruitment process, using AI to root out key candidate information, compare against job spec requirements and reduce manual input. The result was 20 minutes saved per candidate, amounting to a huge saving of 33 hours per month.
Building understanding with a product knowledge agent
We’re lucky to have highly skilled experts across our business – but often, their unique knowledge is locked within them. It means them constantly having to answer product questions to facilitate knowledge sharing.
The product knowledge agent provides insights across our core products and services, giving faster answers and protecting expert time.
Launching an initiative to scale AI experimentation
In order to realise the results of AI faster and ensure it boosted every business function, it was crucial that the whole business felt comfortable testing Copilot. Which meant creating a secure environment for experimentation and encouraging behavioural change.
We crafted a joint initiative, led by both HR and IT, ensuring people felt empowered to use AI in their daily roles, while keeping the business protected against any potential risk.
Ready to take the next step with your AI implementation?
We understand the anxiety around AI – for many people, it’s an unknown, and you want to make sure you do it right. That’s why a careful, measured approach to AI implementation is crucial. It starts with careful preparation and experimentation and ends with effective scaling and long-term change management.
Microsoft AI Business Process Industry Director
1moI ttally agree with you "biggest barrier" statement. Its great to see evidence of the savings and the aproach taken by Infinity to realise them.