Refocusing for impact: Doing more with what matters
Key takeaways
Constraints are shaping how innovation happens.
Organizations in transportation and auto insurance are facing system, budget, and regulatory constraints, making modernization a challenge. Legacy systems slow progress, separating innovation from core operations leads to misalignment, and evolving AI regulations benefit from phased experimentation.
Change demands new ways of thinking.
Traditional approaches to problem‑solving no longer work as well in a rapidly transforming mobility landscape. Leaders must:
- Use mobility data to reframe challenges.
- Explore bold ideas to unlock fresh insights.
- Build cross‑functional teams that balance experimentation with strategic focus.
Redefining success is essential.
Pilots should be treated as learning experiments, not judged by traditional KPIs. This mindset encourages iteration, reduces fear of failure, and accelerates innovation in a fast-moving environment.
Collaboration and “co‑opetition” are becoming strategic tools.
Organizations benefit from clarifying their risk tolerance and embracing more flexible partnerships. Smaller‑scale experiments with multiple partners can reveal the best strategic fit while protecting long‑term objectives.
AI is transforming both operations and consumer expectations.
AI paired with mobility data enables more agile planning, especially for long‑term infrastructure and mobility projects, and personalized consumer experiences such as behavior‑based pricing.
Introduction
In a landscape defined by rapid technological shifts, leaders in the transportation ecosystem are increasingly challenged to deliver impact with fewer resources and greater urgency. The businesses that thrive are those willing to re‑examine long‑standing systems, rethink traditional approaches, and reimagine what innovation looks like within real‑world constraints.
In a panel conversation at Arity’s Insights Conference, leaders from the worlds of mobility data and mapping, auto insurance, the public sector, and the automotive industry discussed the challenges their businesses are facing and how that shapes their approach to management and innovation.
Managing within constraints
Organizations pursuing AI and modernization must navigate a complex mix of legacy systems, constrained budgets, and evolving regulations that shape how they can innovate.
System constraints
Established industries such as insurance contend with legacy structures such as separate billings, claims, and policy systems that don’t adapt smoothly to a world ruled by massive data assets, AI solutions, and other technological advances.
An insurance executive shared that “before we could even start thinking about AI or any of these nice, fun technology things, we had to get off of the legacy systems with our products…At one time across our six-state footprint, we had maybe 112 different policy forms, and we are consolidating that down to about 12.”
Consolidating and modernizing these systems is an important step in creating data management efficiencies and a stable foundation for new tools like AI.
Budget constraints
Businesses may be limited by debt, leaving few resources for innovation. A mobility data executive explained, “We have a ton of technical and commercial debt that gets in the way of us being able to innovate. So we’re trying to figure out better ways to smartly process data, better ways to find signals outside of the noise. The constraints have required us to be a lot more creative, and I think also be a little bit more realistic.”
Two approaches can support innovation even when resources are tight.
First, don’t conceive of innovation as separate from everyday operations, something that requires its own budget and success metrics. Instead, consider integrating innovation into the operating budget, tying it more tightly to core goals and priorities. One of the panelists explained that “Our new approach is our innovation budget is zero, which I love. I find it very challenging. You’re not going to give me any money to be innovative, so I’m going to figure it out.”
Second, tie innovation to customer needs – and budgets. An automotive executive described their business’s approach to piloting new work in partnership with customers. “We work through partners by essentially having our innovation – our piloting and our test programs – be customer funded. That forces us to address problems that need to be solved because somebody’s writing a check to solve that problem…you’re leading it right from the customer.”
Regulatory constraints
Nascent AI regulations are rolling out at the same time as businesses are trying to integrate AI tools into their workflows and products. One insurance company leveraged their multi-state presence to run a pilot in one state, remained flexible as that state’s AI-focused regulatory requirements rolled out, and then took those learnings into consideration as they decided whether to extend the pilot to other states.
Managing through change
Transportation is one of the most disrupted industries today, with massive changes in modes of transportation, automation, infrastructure, sustainability, and energy sources. This requires a complete shift in how organizations identify problems, test ideas, and mobilize their teams.
Change the paradigm
Traditional methods of identifying problems and developing solutions just don’t cut it anymore. In the transportation ecosystem, mobility data can help reframe what’s possible.
In the public sector, transportation methods are evolving rapidly, with autonomous vehicles, connected cars, and electric scooters and bikes converging on roadways and requiring fresh approaches for safety, traffic management, energy, and infrastructure planning.
But the old data cycles are too slow. Accident reports, traffic sensors, and cameras, for example, offer a lagging assessment of incidents and don’t provide enough insight into potential dangers. Municipalities need to understand context, assess efficacy, and bring new solutions online more quickly than ever without breaking the bank – and mobility data can give leaders a fresh perspective.
In the insurance industry, consumers are increasingly open to new models that are not simply based on proxies such as their credit rating, age, or gender. Mobility data can explode that paradigm by basing policy premiums on real-world driving behavior.
Think big
Don’t overlook the strategic value of wild ideas and the ability of data to support insights that might be useful in a real-world scenario. Such as: What would happen to a municipality’s traffic patterns if all the cars were removed from the road? Of course, this is far-fetched — but as a thought experiment, what would need to happen to make this a reality, and what can be learned from that and applied to current challenges?
Leverage internal teams
Collaborative internal teams are critical for navigating shifting strategies and rapid change. Some business leaders have created “centers of excellence” or cross-functional teams that focus on growth areas in the market and experiment with tools like mobility data to build new systems. These teams can help businesses balance disruption with stability, maintain focus once decisions are made, and keep teams committed to their primary objectives.
Reframe success
Innovation in the transportation ecosystem requires leaders to redefine success. Traditional KPIs may not be appropriate when measuring the results of a pilot project. It can be beneficial to reframe pilots as learning experiments that will yield critical insights and inform future pilots. There’s no failure – only lessons learned.
For example, an insurance executive described their approach to exploratory projects: “Sometimes you have to admit that this is not working, it’s not a total failure, but it’s just not working or working as well as it should, and you have to revamp and start again or come up with something new. That can be hard, especially when you’re working with a team and they’re pouring everything into it, but it’s not working. We’ve done that with each line of business that we’ve gone into – you learn from the line that went before you, things that did not work, you change it, you get better. Each time is a little better. And that encourages people on the project.”
Managing with partnerships
In the fast-moving transportation sector, innovation, competition, and collaboration often overlap. Organizations can tap into new opportunities when they get clear on their appetite for risk and embrace more flexible, exploratory relationships.
Determine your risk tolerance
Here’s a key question for transportation leaders to answer: How much risk are you willing to take on when deciding who your partners and customers will be? Businesses are on a spectrum when it comes to technology adaptation — some are ahead of the curve and playing with new tools and systems, while others are more conservative and may need more support.
Working with the less advanced businesses may create more opportunities for innovation, but the flip side is whether these businesses can then support long-term success. Experiments with the faster-moving businesses in your sector, on the other hand, might put you in a leadership position with a solution that fills a market need, but the risk may be higher.
Decide who to partner with (and how)
AI creates opportunities for businesses to do more. The line between partnership and competition is blurring, and “co-opetition” is becoming more common. In the ever-changing transportation context, relationships need to be more agile, and businesses may want to test the water before fully committing. Experiment with multiple partners at smaller scales and see what starts to gel. Create clear guidelines for these more flexible, experimental collaborations to protect everyone and ensure that strategies remain aligned.
When determining risks in potential partnerships, an executive from the mobility data industry explained, “AI does seem like it’s democratizing a lot of opportunities for lots of different people to succeed in areas that maybe are further up the stack than what they’ve done before. It’s created a ton of opportunities to partner, but it’s also created a set of guardrails that we bring into these relationships to protect ourselves. Ultimately it comes down to, are we able to align ourselves on the leadership level? Can we look at their strategy and think, this is a partner that we want to take the risk with, and do we understand that risk going in?”
Managing in a world of AI
AI is now ubiquitous in the transportation space, causing disruption but also creating opportunity. Leaders need to consider how AI-powered tools can enhance operations and power opportunities for innovation.
Be agile
Dynamic planning tools powered by robust, near real-time mobility data allow organizations to be more agile. For example, a typical public sector transportation infrastructure project might have a 20-year time horizon. But as new transportation modes come online, the plan will need frequent reevaluation. Moving from reactive data sources (such as analyzing fatalities) to proactive telematics (such as driving behavior) will help better support pivots over that 20-year time frame without losing sight of the plan’s core goals.
Better serve consumers
Consumer expectations are evolving rapidly, with more desire for personalized solutions based on behavior, such as tailored retail offers or lower insurance pricing. Pairing AI tools with mobility data can uncover surprising new insights about consumer behavior and support the development of innovative new products. For an automotive industry manager, “The promise for AI in predictive models is finding connections that we never anticipated or didn’t know were there. And that’s what I’m really looking forward to as we pull more and more data into our predictive model. What are those connections we hadn’t thought of?”
Be intentional
Managers are under pressure to integrate AI and demonstrate improvements in both internal processes and with business results, A clear and intentional value proposition will guide businesses as they determine how aggressive to be with AI. Is your business willing to experiment despite potential risks, or is it better to watch and learn before committing?
A public sector manager shared that “the first big, bold issue is: how aggressive are we going to be with AI? You fall into these three camps of: really lean into this and figure out what it means to the industry and go heavy; be in the second tier, watching your competitors fumble around with some of this, and then try to get engaged; or bury your head in the sand. I think we can all agree that option three will ensure you get disrupted. So, between those first two options, how bold can you be and how much authority and responsibility do you have in your firm around how aggressive you can be?”
Conclusion
Managing change effectively creates the conditions for sustainable innovation. Businesses and organizations in the transportation space, from insurance to vehicles to the public sector to data providers, know that the path forward won’t be paved with perfect information or unlimited resources. But businesses that experiment, learn, and iterate with intention; embrace partnerships and agile teams; modernize systems and ground every decision in purpose; and approach AI with both ambition and discipline – they will emerge stronger, smarter, and better equipped for the future.
Key questions for mobility leaders to consider
1. How can we modernize our legacy systems to remove friction and unlock the full value of AI and mobility data?
Many organizations are stuck behind siloed, outdated systems that limit agility and innovation. Leaders must assess where consolidation or modernization will create the greatest impact.
2. Are we embedding innovation into our core operations, or treating it as a disconnected side initiative?
Innovation should be woven into the operating budget and business processes, not isolated in special projects. Leaders must evaluate whether their structures encourage or hinder real integration.
3. What is our risk tolerance when choosing partners, and how do we balance experimentation with long-term viability?
With “co‑opetition” on the rise, leaders must clarify how aggressively they want to test new collaborations, and what guardrails are needed to protect strategy and resources.
4. Are our teams equipped to operate with agility and cross-functional collaboration in an era of rapid change?
Flexible teams can test ideas quickly while maintaining alignment with overall strategy. Leaders should question whether their current team structures support agile cross functional collaboration.
5. What intentional value proposition guides our use of AI, and how bold do we want to be in experimenting with it?
The pressure to adopt AI is high, but leaders must decide how forward‑leaning to be and what outcomes they are aiming to achieve — internally, with consumers, and across planning processes.