Beyond AI Pilots: Building Future-Ready Companies

Intro:You're listening to C-Suite Perspectives, a podcast by The Conference Board. 

Sara Murray: Welcome to C-Suite Perspectives, a signature series by The Conference Board. I'm Sara Murray, Managing Director, International at The Conference Board, and guest host of today's episode. Today we'll explore what it means for leaders in their organizations to become and remain future ready in an era of exponential technology, generative AI, and accelerating disruption. 

We'll discuss how CEOs can move beyond isolated technology experiments and build the mindsets, operating models, and cultures required to anticipate change, adapt quickly, and turn uncertainty into measurable strategic advantage. Joining me is Nick Davis, Senior Fellow at The Conference Board; Chief Innovation Officer at Mid-Ohio Food Collective-- a Feeding America member food bank; and CEO of Shopsight, a real-time agentic forecasting engine for global brands. 

He is also the author of the number-one Amazon bestseller, Future Ready. Nick specializes in building future-capable leaders and organizations at the intersection of strategy, innovation, technology, and sustainability. Welcome, Nick.  

Nick Davis: Thank you, Sara. Really excited to be here. Hopefullyit'll be some reallygreat conversationand look forward to chatting with everyone afterwards.  

Sara Murray: Great. Let's begin with the central idea behind today's conversation. When you describe a CEO or senior executive as future ready, what does that mean in practical terms?  

Nick Davis: Practical is the keyword of that. I love it. Many times we think about the innovator as this visionary leader who's really great at being able to predict the future, right? Have a crystal ball, if you will, and being able to navigate through this magical intuition. 

Research and data tells us that'sprobably not the case. There's certainly a dynamic of, experience tells us sometimes hard lessons and easy ones, where we want to go next. 

What we argue in the book is that a future-ready leader is really someone who can adopt a framework around innovation. The one that we use in the book is called WAVE, and I'll actually talk a little bit about an adaptation we've made to it here recently. 

But it gives a certain sense of structure and process to this idea of getting an organization, the managers within it, the individuals within it, a certain skill set that makes them really able to keep up with the speed and cadence of the future within the work that they're doing and the context that they're operating in. 

And so it really is that how do they make sense of this future together, align around it, test assumptions very quickly, and ultimately have a system that rewards that cadence with all the things that we all like-- promotions and recognition and excitement about the future that we're all heading towards. 

Sara Murray: I like that definition because it shifts the focus from predicting the future to being prepared for it and not necessarily having all of the answers but, as you say, building capabilities, mindset, the agility, just to respond confidently as the world continues to change. Many organizations are still reacting to change as it happens. 

What changes to strategy, decision-making, and ways of working can help them anticipate disruption and lead through uncertainty?  

Nick Davis: We all are talking a lot right now about AI and obviously AI adoption and a lot of this ends up getting pushed back on the tool itself. 

There was a recent survey that came out from Reuters that talked about 79% of organizations face adopting AI challenges. That's up actually double digits from last year, so we're seeing that actually get worse than better. Another one said 54% of C-Suite executives say adopting AI is tearing their company apart, which I thought was an interesting thing as I looked up some research to prepare for this. 

It's often blamed on models, model quality, the AI and the tool, but ultimatelyit's an operating problem, right? We're taking 2026 technology and we're layering it onto a 1996 way of working, which is making this very difficult from a change management and adoption standpoint. Recently in the talks that I've been given are some of the areas that we've been focused on anticipating this change. 

A fewkind of key things we'refinding: from a strategy standpoint, we really want to move from being episodic to continuous. You'll hear me a lot this morning or this afternoon, depending on where you're at in the world, talk about loops. Feedback loops, learning loops, and how competitive advantage is really about getting through those learning loops faster. 

If that's true, this set of futuristic muscles that we're building is really about being able to see the future faster and ultimately building a system around strategy that's continuous. You are constantly looking at the market around you and being able to navigate and pivot towards those conditions very fast, right? 

There's that, the John Boyd model for they call it OODA loop. He was a fighter pilot in the US a number of decades ago. Observe, orientate, decide, and act. His argument in the conditions of being a fighter pilot was whoever can go through that loop the fastest will win. Now Harvard teaches it as a strategy. It'swell-known within the business world and I think it still stands, especially from that futuristic context. 

Can you have a continual learning loop? Can you push decisions to the edge? More and more leaders can actually understand the context, see the vision of where we're going, understand that commonality, and they can make even faster decisions to move the organization to where it's trying to go. 

And I'd say lastly on this one is really evidence over opinions. We are still very guilty of operating from a " I've been doing this for a long time. I know what happens here, and I'm going to drive the ship towards that experience." 

And unfortunately, this is not just some catchy consulting term. The conditions of the future are going to be very different.We have to have different operating models.We have to operate more on evidence than prior opinion because it's just going to be very different.  

Sara Murray:Sothere's a lot that'srequired from leaders. They need to embed curiosity. As you said, they need faster decision-making, and that continuous learning really comes through in not just for themselves but into the way the organization operates is really key. 

You talked a little bit about technology. How can executives identify the technologies that matter the most and move from isolated pilots to enterprise-wide transformation? 

Nick Davis:Yeah. I love that, and I'll often pick on the word pilot, because I think it's misused and overused in many areas. For a lot of companies, unfortunately, ends up being kind of the innovation theater we've been talking about for years, right? They'll run a pilot and say they checked that box but ultimately not a lot of intent to actually scale the technology. 

You really happen to have that constant sensing agent, right? For us in the feeding world, as well as in my retail kind of AI life, a lot of the agentic tools that we deploy are focused on looking at the market around you within the context that you operate in. 

And that, in many cases, is telling us how we'repicking the technologies that we're investing in. From a food standpoint, of course, I'm going to care about things around government policy. I'm going to care about things that are breakthroughs in technology to get more food in people's hands faster. I'm going to be looking around the world at changes in fundraising, in corporate philanthropy, things like that. And within that context, I can identify the technologies around us that are most likely going to impact us in the next one, two, three, five years. 

And we do have a live radar where our team is watching those technologies accelerate or deaccelerate within our context, and that's where we end up going to our executive team, our board, and saying, "Hey, here's the things that we think are going to be important. It's not just about the tech that we're monitoring, but your alignment to take action against it." 

So if I go in a room and I say, "There's some giant breakthrough in CRISPR that allows us to make food in a whole different way," if half of our board doesn't believe me or thinks it's coming in 20 years versus two, we're immediately out of alignment already. The investment won't get made and we're going to slow ourselves down and ultimately miss that opportunity. So it really is the watching all around us and then ultimately getting that alignment as well.  

Sara Murray: The real challenge is scaling the technologies that genuinely create value and making them part of the organization's overall strategy, rather than the isolated experiments. 

Nick Davis:Yeah. I would agree. You know, for us, there's a lot of picking the workflow or picking the problem, and then applying the right technology to address it. I think there's a lot of that. We're an AI-forward company going on right now. It's just put it everywhere and we'll see how it goes. 

We start from the customer first and work our way backwards. It's much more of a use the AI for what it's good for, recommendations and some judgment with human oversight. And we use a lot of still code for math. You can imagine in a world where we're trying to feed the most vulnerable people in our country, if we get the math wrong, if we send the person to the wrong place, if we tell them there's food there and there's not-- which sometimes AI can be great at, sometimes not-- we have to be very careful with that. 

And soit's really saying is this the right use of this technology? How are we governing it? How are we making sure it's as accurate as it possibly can be? And then investing where we can.  

Sara Murray: Drawing on your own work, where have you seen technology deliver measurable improvements in strategy, operations, or the customer? And what do you think made those initiatives successful?  

Nick Davis: The example I've been using here recently is a funny one that I accidentally did and I found it out on a Conference Board workshop that I was leading. So yeah, obviously two different parts of my life, right? 

As you read in my bio, I've got a certain portion of what I do, which is nonprofit and philanthropic and feeding people. I've got a whole other part of my life, which is helping brands figure out which products are going to be successful in market in 15 minutes. That's our goal. Seemingly not in any way related from the problems that we're trying to solve. 

But as we built tools in both parts of my life and we started to look at, hey, how can we get better at being able to serve our customer, funny enough, I ended up leading two different engineering teams in two different companies that ultimately ended up building somewhat of a similar approach with the agentic tools that are out there. So accidentally built the same forecasting engine twice. And luckilyit's all in my one brain, so it was a little bit helpful to share those two ideas internally. 

You think about it from a feeding standpoint, right? We've had some funding cuts within our network, within the the hunger relief areas and we have to get more precise with what we do. 

Best way to do that is obviously being able to understand localization, what community wants what food at what time and what place, and bringing that food closer to the individual and their health needs, right? So if I live in a particular county, I may be having a particular hypertension or diabetes or some kind of an issue that needs to be addressed. I have certain food that would help me get healthier faster, right? SoI'm going to predict what that person needs in their body. 

We just finished a pilot with one of the largest kind of big box retailers here in the US. They also need to know what to put in front of what customer at what time in order for them to essentially purchase it on the shelf and take it home for whatever benefit they're trying to obtain. 

And interesting enough, the solution is pretty much the same, right? Can you use this AI to deploy a real-time trend intelligence and demand forecasting that allows us to put the right item in the right place for the right person? Obviously, the stakes are much higher in one of them because we'reliterally feeding families who are hungry. 

You take the learning from one and apply it to the other and it is a really interesting use case for seeing this AI actually have an impact.We've already seen that we are getting better very quickly at being able to understand where to put food in the right place. For us, it was anchored in real decisions for real people. 

Obviously we wanted to focus on outcomes and not movement of food. Every cycle we run with these, we get smarter and smarter. If I deploy it this week and the model tells us put an apple over here and a banana over there, then we'veultimately got the ability to measure that outcome. Was it successful? Did it impact that person's health? And get better and better over time.  

Sara Murray:We're going to take a short break and be right back with more of my conversation with Nick Davis. 

Intro: What does the future of work mean for your employees? How will your company navigate the future of AI? Will there be a global recession? At The Conference Board, our experts translate the latest research and economic analysis into insights and real-time problem-solving for your organization. Membership at The Conference Board provides your team with an assortment of knowledge from economics, marketing and communications, governance and sustainability, public policy, and human capital. 

As a member, you'll have access to our center experts, member-exclusive events, data and benchmarking tools, and peer sharing that will help you understand the present and shape the future. Consider becoming a Conference Board member today by visiting www.conference-board.org. 

Sara Murray: Welcome back to C-Suite Perspectives. I'm your host, Sara Murray, Managing Director at The Conference Board. I'm joined by Nick Davis, Chief Innovation Officer at Mid-Ohio Food Collective. 

So technology transformation depends as much on culture as it does on technology itself. How do you think leaders can create the environment where people are able to experiment, take obviously responsible risks, learn continuously in that loop that you were talking about, but keep the human needs at the center of innovation? 

Nick Davis: Love it. Yeah. A tagline I like a lot. Culture follows that cadence. Not posters on the wall or fun things that we write up for each other. I love the culture poster, but that'sultimately going to be a little bit more surface level than we need for this work. 

You don't really get it by declaring one. Obviously, you've got to be able to understand how to communicate well topeople what it is that they're trying to experiment around, and then create the safe space for them to be able to do so and incentivize it in a way that is really compelling. 

So for me, make small experiments. They're cheap, they're reversible, they're the norm. Allow people the tools to be able to iterate and innovate within their job very quickly. A new idea on Monday is in code, in production, and being tested in front of customers by Thursday, Friday. 

It is so fast now to go and do this work, especially from a software standpoint. I recognize hardware is a little longer but there's still faster and faster loops on this that we can do a regular check-in within the week. It's really prettyamazing for how fast and how cheap we can do it. 

My lead engineer told me last week we've deployed more code in the last month than we did the 10 years before that.  

Sara Murray: Wow.  

Nick Davis: Just think about how fast a small team can build with these agentic and AI assistance models and platforms. For me, celebrate learning, not activity, right? 

There's the old adage of fail forward, fail fast. Everyone's been saying that for over a decade now. It's well adopted because it's true. But this, for me, is more of capturing the learning in those loops and teaching the result to the models that we're using, people, AI, whatever it may be, so they can get faster. 

Because if you learn and you're still slow, it doesn't matter, right? You've got to have both. Speed and learning is a huge piece of it for me. And like I said, put the right tools in people's hands. There is a lot going on right now around, obviously, right-sizing models. 

There's a lot going on right now around which tools are best for which application. I don't see that changing any time soon. What we're seeing is one job may use one model, one platform, and they're fantastic and accelerate. One may use a different one, and if they had to switch, it would be a pretty terrible result. 

Really understanding where to put these fancy new tools to the maximum benefit and the cheapest cost.  

Sara Murray: But it's a great reminder that culture is really one of the biggest differentiators and obviously, organizations can invest in the latest technology. But if people don't feel empowered to experiment and learn and adapt, it's really difficult to fully unlock that potential. 

As organizations adopt these technologies, how do you think CEOs should balance the pursuit of efficiency and innovation with their responsibilities to employees, the customers, and the wider communities that they serve? 

Nick Davis:Yeah, love it. Obviously first there's a lot going on right now around these models, how expensive they'regetting and deployment is a big thing. But with all those conditions being fairly turbulent right now, I would still say our responsibilities as leaders is not to be applying the brake for efficiency. It's going to be leaning into the steering, right? 

I'm the CIO of my company at the food bank. I, I'm the one who approves model utilization, AI, and all that work that we're doing. And we are working desperately to empower as many people with these tools as fast as we can with governance and with cost views on top of it. 

In our world, efficiency is a service of dignity. We have toget more efficient to feed the increasing amount of people that we're trying to serve every day. If we aren't getting there, we're really going to be in a pretty tough spot. 

So when we frame it as fewer people per task, people are going to resist. But when we frame it as more mission per dollar, we're getting more out of the AI and we're able to feed more. People tend to run towards adopting it and are pretty excited about it.So it's just that reframing is pretty important. 

A big thing for us is data stewardship and data ownership. It is a leadership question, not a legal one. We certainly have our lawyers look at our data or our contracts. But for me it's a really important one to say, for us, and for anybody really, your customer's data is extremely important, how you're using it, being transparent about how it's being shared, to what outcome. In our world it'sa very big deal that we spend a lot of time thinking about the importance of that. 

Just stressing with our teamsnoperson is obsolete. We are going to upskill, reskill. These tools will evolve. It's going to be continual learning for all of us to really keep up with everything. But we're trying to drive that through our organization and through our community because ultimately if we aren't doing that, it's just going to be more people on the line that we have to feed down the road, which obviously is going to be off mission for us.  

Sara Murray: It sounds to me that there's a need for thoughtful decisions and the organizations that will succeed are those that combine that innovation with trust, the transparency, that clear sense of purpose. 

What's the problem here that we're trying to solve? What practical lessons do you think established companies can take from start-ups without compromising control or their ability to operate at scale? 

Nick Davis: For me, it's information velocity. Ultimately as somebody who's obviously operating a start-up right now and somebody who works at a large company, I get to see a front row view of both every day. The time it takes for information to go from one person to the other and a decision to be made is inherently faster at the start-up and that's really what the larger company has to emulate. 

Amazon used to have that two pizza rule, right? No team can get bigger than two pizzas can feed. The reason for that, you get decision rights within that group, you give them a budget and a P&L, you give them a goal and an outcome that they're trying to ultimately go for, and especially with AI accelerating, you turn them loose and let them build, and it's incredible how fast they can get things moving and deployed. 

When you get too much bureaucracy, too many decision layers, as we all know, things tend to slow down quite a bit. That's not new information per se, but for me, what we've seen the real accelerant be is not nearly as much money is necessary to build what we have to build, which means we can now get as much procurement, legal, CFO, finance, all those reviews that used to be necessary before we took a big bet on a million-dollar project. We can build the same thing and test it now for pennies on the dollar and we can build it in a couple days. 

And so those review cycles go away, you can speed teams up quite a bit, which is pretty awesome. I'ma somewhat loving but somewhat skeptic on the governance side. If this is a race car and I'm trying to stay at 200 miles an hour and win this race, it's not the engine, it's the brakes that win the race, right? Ultimately, can you put some good governance in there but have it be checks and balances that let them stay at speed versus slowing down and doing a pit stop every single time that they have to go and get a review. 

I want to be able to do a quick, I'mdirectionally seeing exactly what I'm hoping to see from this test, this experiment, when we get it in front of customers. And I very quickly want to say, "Good job. Go and keep running," versus, "Hey, let's park you here for a month while we think about things," and then slow you down and miss the opportunity. 

Those are always really sad ones to see.  

Sara Murray: Yeah, it's really interesting.We're nearly at time, but finally, if listeners remember just one thing from today's conversation about becoming future ready, what would you like it to be?  

Nick Davis: Yeah, I'd say, predicting the future is a component of this, but it's not the main part of it. The cadence is the big piece here. Can we control how fast we sense things around us? How well can we align the people within our group to the same outcome that we're trying to go after? Honestlyverify those results and execute on them is a huge piece. 

Don't reward traditional forecasting. Reward cadence, build the loop, ship the loop, and trust the loop is the biggest thing I'dbe staying focused on these days.  

Sara Murray: Thank you for joining us today, Nick. I really appreciate it.  

Nick Davis: Thank you, Sara. 

Sara Murray: And thanks to all of you for listening to C-Suite Perspectives. I'm Sara Murray, and this series has been brought to you by The Conference Board.  

Intro: You have been listening to C-Suite Perspectives, a podcast by The Conference Board.