immixGroup interviews Joel Neeb of The Insight Age
November 7, 2024
By Ryan Nelson, Market Intelligence Manager
Joel Neeb is the keynote speaker for the immixGroup Government IT Sales Summit 2024 to be held November 21, in Reston, Virginia. He is the founder of The Insight Age, a company that helps lead transformations to prepare large enterprise organizations for AI. A graduate of the U.S. Air Force Academy, Joel also served as the Top Instructor Pilot at the Air Force Flight Training headquarters. He was a contestant on American Ninja Warrior in 2018, 2019 and 2022. Joel is the author of the book “The Insight Age: Moving from Overwhelming Information to Empowering Results.” With his background in the US Air Force and experience helping organizations prepare for AI, Joel brings unique insights applicable to both commercial and federal sectors.
Ryan Nelson, market intelligence manager for immixGroup, recently spoke with Neeb to learn more about corporate AI transformation.
Ryan: Thank you for talking with us today. To start, please tell us what you mean by ‘the Insight Age,’ and why it’s important for companies to prepare themselves for AI.
Neeb: With AI, we are moving from a period where we've been drowning in data and starving for insights to a place where we can focus on those insights like a laser beam. By insight, we mean information that has action and predictive capabilities associated with it. The companies that prepare best for The Insight Age are the ones that will win, just like the companies who prepared best for dot-com era were the ones who won. Some companies, for example, those like Sears or Blockbuster Video failed because, among other reasons, they couldn’t disrupt themselves in that new era.
Ryan: What would you consider to be best practice for agencies or organizations to prepare for the AI transformation?
Neeb: When the internet was just entering the public vernacular, large enterprise companies made a big show of how they were preparing to enter the Internet Age. Their websites had no utility or functional capability whatsoever. Meanwhile, companies born in the dot-com era, for example, Amazon, Google and others were building their internet capabilities from the ground up with a baked-in dot-com infrastructure and nervous system. They were the ones that were successful.
The same is true for companies in the AI era. You can't just bolt it onto your organization. You have to transform literally everything you do. And when I say everything, I bucket that into three categories:
- Your data and tools
- Your operating model
- Your culture
These principles are particularly relevant for federal agencies and contractors navigating the complex landscape of government IT modernization and AI adoption.
Transformation follows these phases, and they all are critically important. An organization can't change just one and be successful. We have to validate and clean up what we call our data debt, then our operational debt and then our cultural debt in that order.
We need to create an insight factory that turns our curated data into laser-focused insights that tell us what to do. And we need a culture of leaders that translate those insights into guidance for the organization.
Ryan: How does that differ from the status quo in business?
Neeb: There's a great adage that goes, “If you want to go fast, go alone. If you want to go far, go together.” Your choice of going fast or going far can have a significant effect on things like data debt.
I came from a company called VMware, which completed some 60 acquisitions over 10 years. With every acquisition, we told the company we acquired, “Keep your tools, keep your processes, keep your culture. Go as fast as you can.” And it worked well for 10 years.
In the federal space, agencies often face similar challenges with siloed data across departments. For instance, the Department of Defense's JADC2 initiative aims to connect sensors from all military branches to a single network, addressing the kind of data debt we're discussing.
People aren’t just buying point solutions or features; they’re buying outcomes, and they have to be happy with the outcomes that are taking place.
That’s a much more distant goal line than we had in the past, and it requires us to be much more coherent internally. We have to clean up our data, put things in common tool sets, consolidate platforms instead of using bespoke tools. Then we can begin curating inputs to get insights.
In other words, our value is in the layers of inputs that are correct, that are standardized, that are not lies. We need the right data as inputs to effectively create an insight factory from our organization. That's the data cycle.
Ryan: How do we move from there to cleaning up our organizational debt?
Neeb: An organization does not necessarily have broken processes; it may in fact have broken leadership models that don't work in this new environment. We’re carrying a lot of operational debt. To address the debt, you must see it.
That boils down to something as simple as mapping goals and strategy across the organization using models for objectives and key results — or whichever tool you might prefer.
It can be challenging for an organization’s business leaders to distill their plans for quarterly performance into, say, three objectives and a couple of key results, and to hold teams accountable for those metrics.
Ryan: Why can that be so challenging?
Neeb: It typically means the organization is vastly misaligned, with entrenched silos of operation rather than goals across the business. Once we see that operating model in full display, it becomes more possible to understand the business’ misalignment — its nuances and challenges — and to begin fixing the operational debt.
We need to use this information to turn insight into impact. An organization needs to understand how fixing this operational debt relates to the insights we’re now getting from data. As we move to a business environment that embraces AI, the organization must be fluid, continually shifting the target and enabling teams to successfully pursue those shifting targets. Does this mean something as radical as changing the business model, or is it more a matter or changing some tactics?
Regardless, this calls for mapping out operations in exquisite detail, because then you’re able to adjust something you can actually visualize.
Federal agencies, with their unique procurement processes and compliance requirements, may face additional challenges in addressing operational debt. The principles of clearly mapped objectives and key results, however, are just as crucial in the public sector for successful AI implementation.
Ryan: The last piece of this transformation puzzle is the cultural debt. What happens there?
Neeb: At this point, we have an operating model that's completely mapped out in a tool work board. We know what our vision is. We know our objectives and our key results. We know how we're successful, how we're failing.
This guidance flows down the organization every two weeks, the updates flow up, and so you have a kind of corporate respiration taking place. We breathe out the guidance, we breathe in the inputs that become the new insights, and we continue to make adjustments to everything in a very fluid way to, to ensure that we're successful.
This is happening at a level we've never been capable of previously. We hadn't been able to track everything at the scale we can with the software tools now available to us. Also, we couldn't wrap our minds around the data coming up the organization — nor would we try. We just picked something, and we'd use our own cognitive biases and focus on that instead.
Ryan: So how does this apply to addressing a cultural debt?
Neeb: Imagine a world without cognitive biases, where you're not just focused on the last fire you put out. Instead, you get all your updates of where you're winning, where you're losing, and then you get the insights about how you can adjust your strategy from an AI tool that has an IQ of a thousand. That tool can absorb all of this data and give us perfect insights with predictive power for the future.
You can’t get there, however, without acknowledging that your corporate culture needs to change with The Insight Age. If you do, you’ll see great results.
In one case, we built an AI bot to take in customer data, identify appropriate key use cases, and turn those results into a proposal customized not for a segment, not for a certain category of customer, but specifically for a particular customer. Before the advent of The Insight Age, this might have taken a dedicated team a month or more for a single proposal. Now it can happen in under a minute.
That company has gone from $5 million in sales last year to $30 million this year, because they’ve been able to connect the pain points to outcomes much more clearly. That simply couldn’t happen without a top-down understanding that the corporate culture had to change to fully embrace AI.
In the federal context, similar AI tools could be used to streamline the complex RFP response process, helping contractors quickly identify and address specific agency needs while ensuring compliance with federal regulations.
As federal agencies and contractors prepare for the AI era, understanding and implementing these transformation strategies will be crucial for successful modernization efforts and improved service delivery to citizens.
Ryan Nelson is a market intelligence manager for immixGroup, the public sector business of Arrow Electronics. immixGroup delivers mission-driven results through innovative technology solutions for public sector IT. Visit immixGroup.com for more information.
Interested in hearing more from Joel Neeb? Register to attend immixGroup’s 2024 Government IT Sales Summit scheduled for November 21 in Reston, VA.
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