Last week, I stood in front of our team at Centrical and talked about something that keeps me, and many other leaders, up at night: how to become an AI-first company.
I don’t mean just building AI features into our product (which we are doing, too). I mean fundamentally rewiring how we think, operate, and solve problems as an organization.
Leading this kind of change in a mature company is hard work. But it’s also necessary.
In this edition of The Future of Work:
Before we dive in: Want a bulletproof method to communicate change to your people? Check out this amazing framework Sally Earnshaw shared at our Big Game event in London last month:
Three Lenses on AI Transformation: Internal, Product, and the Bigger Picture
When I think about AI at Centrical, I look at it through three distinct lenses: internal ops, our product, and AI’s wider impact on the future of work. Each one matters.
Internal Adoption: What’s Working for Us to Get People on Board
How do we as employees, as departments, utilize AI to work smarter and faster? How do we bring individuals, departments, and the entire company to the next stage of AI adoption?
At Centrical, we’re figuring that out in real time as we promote a culture of experimentation and testing.
Here’s what we're doing:
Hackathons where R&D teams compete to build the most innovative AI solutions within our platform to solve real business problems.
Department-vs-department competitions to unlock new use cases for internal adoption of AI across the organization.
“AI Moments”: AI challenges and learning missions delivered through our own platform to share know-how, spotlight individuals using AI in new ways, and gamify the learning process.
Build-and-share sessions where employees demo their AI agents and workflows.
AI use case library documenting successful implementations others can replicate.
To do this, we’ve formed an “AI Builders Team,” a group of employees tasked with building the infrastructure to support the transformation company-wide, and guide and coordinate AI initiatives for less tech-enabled peers.
Our teams have built AI agents for all sorts of use cases. Here are just a few highlights:
Optimizing the Customer Journey
Automating Customer Onboarding Tasks
Platform Asset Generation (branded images, training content)
Self-Service Customer Enablement
Streamlining Product & R&D Processes
Monitoring Agents
Automated QA
Faster Dev Cycles
Product Usage and Customer Feedback Intelligence for feature prioritization
Driving Sales & Marketing Efficiencies
AI Sales Guide
RFP Response Generator
Automated Competitive Intel
Customer Story Creator and Finder
Outbound Intelligence
Scaling HR Initiatives
HR Policy and Knowledge Assistant
Quick Win for Your Organization: Start small. Launch a 30-day AI challenge where teams document one way they’ve used AI to save time, streamline a process, or improve quality. Share winners company-wide. Build momentum from there.
Product Strategy: Reimagine Your Solution
We don’t have an AI strategy. We have a product strategy. This means that AI is embedded in everything we do.
We’ve woven AI into almost every aspect of what we build within our platform, and we keep pushing further to personalize growth and development.
Our AI Assistant for team leaders is a perfect example:
Driving 88% more coaching conversations at a global financial services organization.
70% reduction in administrative work for managers at a global BPO, freeing them for high-impact performance development.
1.5 hours saved per week for managers at Capita using our AI insights and coaching recommendations.
Quick Win for Product Leaders: Don’t bolt AI on as a feature. Ask: “Where do our users waste the most time?” and “What insights exist in our data that we're not surfacing?” Start there.
The Bigger Picture: AI’s Impact on the Future of Work
The strongest current in the market right now is a headwind for human workers, particularly frontline employees and contact center agents. Hardly a week passes without a headline about layoffs.
Here’s the reality:
Fewer jobs requiring lower levels of proficiency
New roles emerging, but requiring skills many workers don’t have yet
The prevailing force is reduction
So, the question isn’t really whether automation will replace certain roles. It will. The question is: what happens to the people?
Move People Up, Not Out
Here’s my answer: automation is eating employee roles from the bottom up, which means employees need to move up. Workers at every level, myself included, need to gain new skills. This requires a fundamental shift in how we support and enable the workforce of today and the future.
On the upside, this is exactly what Centrical was designed to do. We help people grow, and we help organizations lead and navigate change.
Let’s take a large bank as an example. In the next three to five years:
Shrinking roles: Customer service positions will likely decrease
Growing roles: Fraud prevention, for example, will expand due to the rise in phishing attacks and deepfake scams
The solution: Internal mobility, not mass layoffs
We’re already seeing results with companies using our platform to facilitate these transitions. It’s essentially reboarding: reskilling and upskilling on the job.
What makes this work:
Employees stay in their current role while learning the new one
They gradually take on more tasks and responsibilities
Organizations maintain productivity during the transition
It’s a bridge, not a cliff.
Quick Win for Workforce Planning: Map your roles on a 2x2 grid: Growing vs. Shrinking, and High Skill vs. Low Skill. Identify the bridges between shrinking roles and growing ones. Start with one pilot program.
The Mindset Shift: Encourage Your Teams to Think Like Engineers
To make this work, we need to build AI-first organizations where people start thinking like engineers: constantly building, testing, and iterating.
This new reality requires a willingness to continuously learn and adapt, and genuine investments in employee development and internal mobility. Adam Grant’s Think Again comes to mind.
AI tools work best when humans can experiment, customize, and refine their use. Engineers and scientists seek new solutions, run experiments, and iterate constantly. That’s the mindset we need across our entire organizations, not just in our product or engineering teams.
The landscape is shifting fast, and we need to evolve just as quickly.
But that’s where the real challenge lies: driving change in a mature company is hard because you already have momentum, existing customers, and established ways of working. You can’t just tear it all down and start over. You have to adapt while keeping the engine running.
For leaders facing similar challenges, here’s my advice:
Start now. We began our transformation before it was a board agenda. When the landscape moves this fast, speed to market is everything.
Embed AI everywhere, not just in one product or department. Make it part of your DNA.
Focus on your people. Technology changes fast, but people change slowly. Invest in helping your team develop the skills and mindsets they need to adapt and thrive.
Embrace the discomfort. Becoming AI-first means admitting you don't have all the answers. It means experimenting, failing, and learning.
Find the opportunities in the disruption. Yes, there are risks. But there are also massive opportunities for companies bold enough to reinvent themselves and the industries they serve.
The Bottom Line
We’re living through a fundamental restructuring of work. The companies that will thrive are the ones that help their people grow faster than their roles are changing.
At Centrical, we’re building that future, for ourselves and for our customers. It’s hard, but it’s also the most important work we’ve ever done.
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