As companies look to take up new technologies to transform their businesses, challenger advisories are looking to expose cracks in the traditional model of the consulting industry. Jordan Richards founder and CEO of design-led AI product studio &above explains how the firm is using a lean approach to help take on the big consultancies, and help clients adopt AI at a rapid pace.
&above has carved out a distinct position as a design-led AI product studio. Where did that focus come from, and what gap were you seeing in the market?
Over the last few years, what was previously known as digital transformation has become AI transformation. Google is known to have had a significant role in supporting its customers through digital transformation, and &above is a dedicated Google partner. Together, we have spent the last few years building AI solutions that drive transformative impact to their customer’s businesses. Through our unique combination of skills and experience, we’re able to bring together a balance of beautifully designed products that people love, robust engineering for market-leading solutions, and behavioural science, to ensure that solutions are adopted successfully. And I find that really exciting.
The business has grown fast. What’s driving that – is it the AI moment, the model, or something else?
I think what makes us different is that we bring enterprise-level experience at startup pace. When you work with giants like Google, you understand things that most SMEs simply can’t grasp. But we have the agility that many larger companies simply can’t offer. We’re able to take on the big consultancies with a lean approach that helps businesses move from what to how with AI at a rapid pace.
You’ve said 95% of AI implementations aren’t delivering any P&L uplift. That’s a striking number. What’s going wrong?
The problem is that AI is being mandated by senior leaders, but the gap between their expectations and the reality of business is growing, so they’re failing to see any return on their investment. I think this is down to a couple of reasons:
- There is a solutioning superpower with AI. Anything is possible. But what is really important is the same product methodologies, which means you need to start with a really clear problem space before you build the solution.
- It’s more important than ever to design for human behaviour. What I mean by that is that we build AI solutions that people love, so that they actually use them, which creates the desired behaviours we’re looking for. Only when that happens will the solutions be adopted at scale.
The workshop model is central to how you work with clients. Walk us through what that actually looks like in practice.
We created the AI launchpad because with AI initiatives, it’s more important than ever to have a cross-functional group of stakeholders in the room aligned around the problem, the business case, and co-creating the solution so that they can own it.
In our workshop sessions, we clearly define a problem and explain how success can be measured for the business. We take it to a rapid prototyping session where all stakeholders get hands-on with tools to design their vision of the solution, then we go under the hood to really understand how the solution will be integrated and constructed. Finally, we get to the session that everyone loves, which is designing AI for humans. This is all about the science and art of driving adoption of AI and creating desired behaviours by putting design first in the process.
Importantly customers leave the workshop with a plan of attack, which details rapid steps they can take to progress their AI maturity. Often, we will follow up with building a technical proof of concept on a sample data set to prove what’s possible and validate the solution.
You work with clients like Google and Tesco. What does an organisation that gets AI implementation right look like versus one that’s still stuck?
Firstly, I think organisations need to move beyond GenAI use cases to gain true value from AI. While it has its uses, agents can carry a huge amount of potential.
Also, in any size of organisation, a lot of an employee’s experience is through their manager. What we have actually found is that those managers and leaders who are actively using AI themselves and demonstrating that to their team, see their teams become more power users of AI across the board as they are setting the example.
Companies treating AI seriously with product methodology are able to gain great value from it as they bring their skills together. So, taking design, engineering, and product management, and using AI to rapidly develop and iterate a solution, ensuring that it is designed for humans and integrates with wider business systems. That’s the easiest and quickest way to ensure that any AI investment drives both impact and value for your business.
You started your career as a Google apprentice rather than going the university route. How does that shape the way you run &above today?
I loved learning as an apprentice because I was able to learn through doing and practice. I think this skill is more valuable than ever with AI, because theory cannot keep up with the pace of what is possible. I feel that, truly, for you to understand and embed how best to work with AI, you need to be hands-on.
I’ve been able to take the knowledge and experience from my journey as an apprentice and apply it to how we, as a company, approach AI learning, and how we deliver AI for our customers.
When it comes to building your own team, are you hiring differently to the rest of the industry, and does background matter to you?
Firstly, we take a lean approach to recruitment, and generally only contract talent in new roles where they can rapidly bring expertise and refine what is required. We combine this with AI augmentation to fully optimise the duties and workflows around the role.
When we’re ready to hire permanent, full-time talent, I take a different approach, which is to not read a CV. With my alternative background being an apprenticeship and leaving college early, I do not believe that qualifications on paper are the only thing that matters. I like to see applied practice.
So, for me, problem solving is a really important skill. We regularly set tasks, which enable me to understand and see how people think and approach challenges. I’m always interested to see how the candidate discovers more about our business and what questions they ask us during the interviews.
It’s often unrealistic, in certain roles, to expect masses of AI experience. So, instead, I’m always keen to understand how people would like to use AI if they were able to have access to our engineering resources, and if there were infinite possibilities. This creative thinking helps me to understand the candidate’s appetite for AI in their role.
The big consultancies default to graduate pipelines. Is that a mistake, especially for AI work specifically?
I don’t think this is a mistake, necessarily. And actually, I think businesses need to invest more than ever in early talent. The thing is, right now AI is removing some of those early-stage roles, and that’s an opportunity for businesses to bring in AI-native talent, especially through applied practice like apprenticeships. People have not yet learned the clunky, complex ways of working in a business and can approach it with a fresh mindset of how AI would be applied to their workday.
Where do you see the AI implementation market heading in the next 12-18 months – consolidation, maturation, something else?
So far, the real focus has been on GenAI, but we’re reaching the stage where Agentic AI is beginning to mature, so we’re going to be seeing a lot more augmentation, with the potential for that to turn into complete automation in many cases. And I think partly as a result of that, SaaS will rapidly decline in value.
Businesses will also see more AI squads and innovation teams being built or contracted. However, I still think that a lot more AI projects will continue to fail, if they are not designed in a way to augment human workflows and drive the right behaviours for adoption at scale.
What does &above look like in three years?
More or less as it does now, just bigger. We intend &above to be a leading AI product studio with multiple scalable products of our own being utilised by thousands of people (and agents) daily. We’re already more than halfway there.
If you could give one piece of advice to a business leader who knows they need to move on AI but doesn’t know where to start, what is it?
It’s impossible to boil it down to one single point, because there are too many mistakes being made. But I would say that the biggest issue that most teams face is that they know AI will add huge value to their organisation, they just don’t know where to start; which practical steps to take.
That’s why we run AI Launchpads. One-day sessions to rally leaders around a shared high-value problem. Because with AI, teams now have a solutioning superpower, often throwing AI at it and seeing if it sticks. Starting with a clearly defined problem cuts through that. Your business case is there from the get-go, and you can measure the return.
So, we get people in the room working together to co-create a solution they can own. It helps that they can get hands-on with tools and prototype what an ideal solution looks like, because they need to understand what the reality of the solution might be. Then, we take the solution and build a proof of concept, ready to test technical feasibility and validate the solution.
It only takes a few weeks to go from problem to a working solution. That’s the start-up culture we bring to organisations that are stuck.
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