3 People-First Rules For Building AI Value
In the corporate Land of Oz, we’ve spent years pinning our hopes on the wizard of Artificial Intelligence.
McKinsey recently reported AI-powered agents and robots could unlock $2.9 trillion in annual US economic value. Who could say no to that yellow brick road, right?
Yet only 6% of executives are seeing organisation-wide ROI, despite widespread use of the tools.
Until now, the importance of human skills to business value has been a given. We risk losing sight of this by prioritising tech in the hurry to „keep up“ in the „AI race“. The human skills that created business value in the first place becoming weaker through lack of use as AI adoption tips into AI dependency.
This isn’t an anti-AI argument here, but a pro-human one. AI creates the most value when it augments human judgement, creativity and problem-solving rather than replacing them.
It’s only by shifting the focus from the tool to the person behind it that AI can deliver on its potential.
Here are three things we suggest you focus on to get your people moving.
1. Meet Your People Where They’re At
Your employees are exhausted. The pandemic accelerated trends that were already in motion: chronic uncertainty, digital saturation and a workforce increasingly unmoved by lofty company visions.
In 2020 alone, we experienced 5 years‘ worth of digital transformations in just 60 days. That’s the context your AI strategy is landing in.
The AI picture is moving quickly, too. If you ask most companies what their AI strategy is, it’s, „Yes, and yesterday.“ Vagueness at pace. Yet somehow people just need to keep up. Whether it’s the latest model update or a shift in strategy or frontline messaging, we’re moving quickly, and people are exhausted from the change.
Consequently, we’re seeing changes in how people show up, what they expect and how much of themselves they’re willing to give.
And so, the quality of your learning experience really matters if you want to address a workforce who are already numb to so much change.
Enablement needs to tap back into the experiences and qualities that make us human: community, storytelling, empathy, intimacy and wonder.
Some quick examples of how we’ve done this with our own clients include:
- Storytelling: Used the hero’s journey to turn capability-building into a stronger narrative experience.
- Community: Ran a three-day camp with no laptops or slides—just pen, paper and each other.
- Empathy: Used customer personas and public spaces to help teams see experiences through someone else’s eyes.
- Intimacy: Sent handwritten invitations to make a leadership experience feel more personal and connected to a storied brand.
- Wonder: Turned a global risk strategy into an animated side-scroller game to bring more play, imagination and story into the experience.
2. Solve What The Business Needs. No, But Actually.
At the risk of sounding cliché, this is an opportunity for us as enablement professionals to reimagine how we’re perceived by the business.
Too often, we’re order takers, detached from what the business is really trying to achieve. AI transformation gives us an opportunity to understand the problem better and speak back insights the business cares about.
How do you do that in practice?
We start with something called the compelling question: a strategic narrative that keeps us grounded in what we’re trying to achieve. At Sponge, this question isn’t allowed to use the words „training“ or „learning“—harder than it sounds! From there, we outline where the business is heading and the behaviour changes needed to get there.
This can be difficult for those of us who are more content-focused. The familiar response is: „There’s a problem, so let’s fill a knowledge bucket and scale it out.“
Instead, get specific about the behaviour you want to change. What were people doing yesterday? What do you want them to do differently today? Where should that behaviour show up in the moments that matter? Specificity is what takes this from good to great.
Once you’ve defined the behaviour, you can build the learning around it and measure whether it is showing up in performance.
Above all, remember that AI adoption isn’t simply a knowledge problem. It’s a behavioural one.
3. Find Your Focus
With the AI landscape moving at such speed, AI adoption planning can leave organisations scrambling to figure out the best way forward.
When you’re looking for the right next move, slow down, simplify, pick one thing and do it well. Whether that means changing how you respond to the business or embracing a different type of enablement experience, stay focused enough to do it properly.
We’ve found that AI enablement challenges often fall into one of four categories:
- Translating strategy into everyday behaviours
- Building awareness and fluency around AI
- Equipping leaders to confidently manage the change
- Accelerating champions and early adopters
Most organisations will recognise more than one of these. Trying to tackle them all at once will lead to failure.
Start with the category causing the most friction. If you’re struggling to decide which one that is, look at your biggest pain points and work backwards from there.
To make this easier, we developed a free tool called ATLAS, which helps you identify where to start and suggests activities to get your strategy moving.
Don’t Forget Who’s Behind The Curtain
Your AI strategy means nothing until people start doing things differently, and you need a breakthrough in humanness and human-led enablement to reconnect people with the work.
The key thing to remember is that people enablement in the AI era is about protecting and promoting human value in the most human way possible. Only then will people start doing things differently.
To explore these ideas in more detail, watch The Human Behind the Curtain. Three people-first rules for building AI value., where I unpack the three people-first rules for building AI value and the examples behind them.
The yellow brick road is still worth following. Just remember who’s behind the curtain.