How AI Dashcams Can Form the Cornerstone of Driver Trust and Safety Culture

ST: I have with me Tiffany Smith from Motive, who is going to talk to us about AI dash cams.

When we get to the end, we’ll have time for Q&A, and we’ve got roving microphones. Tiffany, over to you.

TS: Thank you, Simon.

Hi everyone. Thank you so much for coming to chat with me today. I’m hoping to make this very conversational. It’s not meant for me to just talk at you for the next 20 minutes. I hope I can share some really useful insights into how Motive is helping organisations today with AI dash cams and how we think about safety.

More than anything else, I want you to know who I am. I currently lead our solutions engineering team here in the UK. I know I have an American accent, but I do live in London. I moved here, I live here, and my primary responsibility is making sure fleets feel comfortable with the solutions we provide.

I welcome questions at the end. I have about 15 minutes of content. The first five slides are about Motive, so you know who we are, and then the second half will focus on the insights I hope you’ll take away.

Let’s get started.

That’s my face. Good to see me again.

Let’s talk a little about who Motive is.

Motive is a newer player in this market, but we’ve been operating in the US for about 12 years. Many of the fleets we’ve worked with there have expanded into this region, and we’ve done fairly well.

Obviously, we sell AI dash cams, but that’s not really the point of today’s conversation. The point is to encourage you to think a little differently about how you adopt solutions from Motive or any other vendor, and to give you the right questions to ask anyone you interact with from a fleet perspective.

Just by a show of hands, who here is responsible for fleet? Who looks after vehicles?

What about everyone else? Great. We’ve got a good mix.

The aim today is to make sure each of you leaves with a perspective that’s relevant to your role.

This is our mission. This is why Motive was founded.

I came from a physical operations background. I started my career in construction. I know I clean up well, but I actually began on construction sites dealing with fleets, safety, and safety managers.

I understand many of the conversations you’re having today. One of the biggest challenges I’ve seen, both before and since joining Motive, is organisations adopting the wrong solution for their safety challenges.

Sometimes you think you’re buying something that works in real-world conditions, and it doesn’t. Other times, you’re unable to get the data you actually need.

This is a quote from one of our largest customers in the US, FedEx, who uses our platform across most of their fleet.

As I said, we’re well established in the US. We’re newer here, but we’re not new to AI dash cams or to helping organisations improve driver safety.

At its core, Motive offers a broad range of functionality, but the main thing I want to focus on today is driver safety, because that’s what we do exceptionally well.

We detect events, help you understand those events, and provide the data your fleet needs to make better decisions.

As you’re making safety decisions, you’re thinking about your fleet and the challenges you’re facing. Often the focus is on finding the lowest-priced vendor or the one with the best presentation.

That’s understandable, but I would encourage you to focus on something else: accuracy.

How accurate is the data you’re getting from that solution? How quickly can you access it? And who within your organisation needs that information to make business-critical decisions?

What we’ve found is that organisations like yours are facing many of the same challenges: increasing costs, increasing risk, and increasing workloads.

When you’re introducing a safety solution and trying to build a strong driver safety culture, we always encourage fleets to think about the problem holistically.

An AI dash cam is only one small part of a much larger picture.

It’s also about organisational culture, driver behaviour, how your business responds to incidents, legal considerations, and everything else that comes with fleet safety.

Whatever technology you’re considering, especially around AI, make sure you test it thoroughly.

From our perspective, the most important thing I want you to leave with today is an understanding of what AI actually is and why it matters.

Anyone can say, “We have AI,” because it’s become a popular buzzword. But accuracy is what really counts.

In the second half of this session, I’m going to explain why drivers, and the people working with them every day, matter so much, along with some of the key statistics we discuss with fleets.

One statistic we often reference is that there are around 130,000 road casualties involving vehicles.

No solution can honestly claim it will prevent every one of those incidents. That simply isn’t realistic.

What technology can do is identify risky behaviour before it becomes a casualty.

To do that, you need something that’s accurate. It needs to recognise if I’m using my phone while driving. The speaker before me gave a great presentation on distractions while driving. If you get the chance, I’d recommend watching it.

There are countless hidden risks on the road that can put your fleet in danger, and for those of you working in legal or insurance, those risks become even more significant.

Now I’d like to step away from AI and the buzzwords for a moment and ask you to think about something you probably use every day at home.

I didn’t plan this around the fire safety theme, but think about your smoke detector.

You don’t spend every day looking at it. You simply expect it to alert you when something is wrong. You want to know your family is safe, and you want it to go off when there’s an actual fire, not because you’ve burnt the toast.

The same principle applies when you’re choosing technology for your fleet.

Whether it’s a dash cam, telematics, or ADAS, you need to know it’s identifying the right events at the right time, so you’re making decisions based on good data rather than bad data.

Any questions so far?

You’ll hear me say this often. Anyone who joins me on a customer call will hear the same thing.

Accuracy isn’t just another buzzword. For us, it’s the minimum standard.

We pride ourselves on being as accurate as possible because that’s what it takes to be a leading provider in this market.

This is what ultimately matters.

If your system says it has detected something but gets it wrong, perhaps blaming a driver who wasn’t actually at fault, you need the surrounding context.

You need that information immediately. Not an hour later, not even 15 minutes later. You need it in real time.

As you’re evaluating solutions for your fleet and your business, especially where safety is concerned, it’s important to understand why that speed and accuracy matter.

I see it every day.

I speak to fleets daily, sometimes hourly. I hear about vehicles that almost hit a tree, almost ran into the back of another vehicle, or almost struck a pedestrian. Those near misses are exactly the moments where accurate, timely information can make the biggest difference.

Especially now that I’m in London, and I’ve driven in London, it’s like driving Mario Kart.

For me, it’s really important to focus on the key areas. First is risk. Sometimes someone is genuinely tired. It’s not misconduct, they’re fatigued. How do you know that if you don’t have visibility inside the cab? That person may have had a long night, or the kids kept them awake. That’s not necessarily their fault. Maybe they just needed another night’s rest, but you wouldn’t know that without visibility into the cab.

The second part of this conversation is noise. This is where we often see fleets struggle. They’re getting a lot of bad information, and it’s not necessarily coming from AI. They might be getting inaccurate reports from drivers or from members of the public. Fleets spend a huge amount of time trying to establish what actually happened. It becomes a case of “he said, she said,” and that gets nobody anywhere. The most important thing is having visibility, so that when you’re making decisions, you’re doing so with the right information.

Here are some key questions I encourage you to ask, whatever solution you’re considering.

First, think about the detection rate. Does it detect genuine events in real time? How many false positives does it generate? What are you actually seeing?

Then think about the time to alert. How quickly does the driver receive the information? Is the alert delivered in real time? Does the driver hear it immediately?

You also need multiple ways of understanding the data. At Motive we talk about multimodal inputs. Ask your vendor, not just us, how much information feeds the AI model. How often are those models updated? What data is used to train them? Those answers tell you whether the system is continuously learning.

We also use human-in-the-loop review, where people regularly review and validate the data, constantly measuring performance.

Before asking what an AI system can detect, or which camera or vendor you should choose, I’d encourage you to think about these four pillars when evaluating any solution for your fleet, business or organisation. Dash cams are just one part of the picture. Any AI-powered solution should be assessed critically because accurate AI reduces noise, improves coaching, delivers the right data to the right people, and ultimately strengthens your safety culture.

If people trust the technology you’ve invested in, they’re far more likely to use it, whether that’s for coaching or improving their own driving. That’s what matters most.

So how do you make sure you can trust the data you’re using to coach drivers or support your organisation?

I built this framework to help explain it. I know the words are quite small from where you’re sitting, so I’ll walk you through it.

The foundation is understanding operational context. That means looking at GPS tracking, insurance claims, whether people are making opportunistic claims against your business simply because your logo is on the side of a van, which we see quite often, as well as fines and other operational data.

Then there’s what I call the “messy middle”. At the top sits your safety culture. Quite often, when I speak to fleets, poor safety culture is really the result of weak foundations. They’ve struggled to interpret the data, they don’t understand what the AI is telling them, and they’re left with more questions than answers.

How do I actually use this information to improve safety within my organisation?

For me, there are four key principles.

First, your safety culture needs to be repeatable and fair, and that starts with accurate coaching data.

Second, you need lasting behavioural change. Sometimes we hear about a driver having one bad event, then another, then another. That’s why accurate detection matters.

The third principle is prioritisation.

The fourth is coaching.

Finally, there’s the driver experience.

Together, these principles help you understand what’s most important for your fleet. As you work through this hierarchy, understanding the data, assessing its accuracy and deciding how to use it, you can apply it as a framework for evaluating any solution that’s presented to you.

AI is AI, but ultimately your business is making an investment. You should understand exactly what you’re being promised and whether the solution can actually deliver on that promise.

Of course, I have to talk about AI dash cams because I work for Motive.

One of the key points I’d make is that the “messy middle” can often be solved with a strong AI dash cam solution. You get real-time coaching, accurate AI detection, event recognition and reporting that helps the business understand what’s happening.

For example, you might see a 20% reduction in close following or a 5% increase in mobile phone use. Those insights help you focus your efforts.

But I’d encourage you to take three or four steps back before buying anything and really think about what information the system is actually going to provide.

When the information is credible, and as you’ve probably heard me say about 25 times today, when it’s accurate, everything becomes easier for the business.

The biggest challenge I hear from fleets is administration. They’re spending time filtering out false positives, clearing dashboards and responding to emails from pedestrians who claim one of their vehicles hit them.

Those situations are much easier when you have contextual evidence. The discussion disappears because there is no grey area. Either the incident happened or it didn’t, and the evidence shows exactly what took place.

Just by a show of hands, particularly from those managing fleets, how many of you have had incidents recently where you didn’t have enough visibility or wanted more information?

Yeah.

Yeah, we hear it all the time.

For us, one of the things we always say is that getting information within seconds is critical. You need that information immediately. Once you’ve adopted a solution like this, it’s also important to make sure trust grows within the fleet.

Whenever I speak to fleets, I always say that safety culture is built on trust. You have to trust the data, and you have to trust the people involved. One way you achieve that is by making sure the right controls are in place, including privacy protections and GDPR compliance. Those things matter.

So, the question I’ll leave you with today, before you all head off for food and hopefully enjoy the sunshine, is this:

Are you getting the right signals? Are you getting the right information? And is your fleet actually doing what it needs to be doing?

We’d be happy to continue the conversation afterwards as well.

Thank you.

ST: Thank you very much, Tiffany. Does anybody have any questions?

Audience member: One of the biggest issues we’re seeing with the Traffic Commissioners and the DVSA is bridge strikes. Can you see this technology being useful for drivers in helping them spot and avoid those situations?

TS: That’s a great question.

At Motive, we’re thinking about bridge strikes quite holistically. They usually come down to two things.

First, bad data. The GPS routing may have been incorrect and directed the vehicle down the wrong route.

Second, the driver may not have been paying attention or fully aware of their surroundings.

There are already solutions on the market that account for vehicle height, but for me the most important thing is coaching the driver before the incident happens.

In many cases, it probably isn’t the first time that driver has encountered a low bridge. It might be the fifth or sixth time. Because we make the data easy to understand and review, fleet managers can often identify those patterns before a bridge strike actually occurs.

It’s certainly something we’re thinking about, although I can’t say exactly where it sits on our product roadmap at the moment.

ST: Thank you. Does anybody else have a question?

Audience member: I think a lot of organisations already have different systems in place. If you’re looking at a solution like this, what are one or two leading indicators you’d use to tell whether it’s genuinely driving lasting change, rather than simply creating more data and more noise?

TS: That’s a great question.

The first thing I see fleets measure is the reduction in repeat behaviours. How often is the same behaviour occurring week after week? Many fleets benchmark this over a thousand miles or a defined driving period.

For example, they might compare 15 mobile phone events over one thousand miles with five over the next thousand miles. A reduction in those events is a strong KPI.

The second thing, which I don’t think gets talked about enough, is user adoption.

Quite often organisations buy these systems, log in once, and then only return when an emergency alert appears.

For any solution like this to be successful, people actually need to want to use the platform. They need to engage with the data.

One of the strongest leading indicators I see is active engagement. The best users are in the platform every day. I’ve seen people spend 500 minutes in a single day reviewing the data, really analysing what’s happening.

Those are the two things I typically look for. That’s how you know a fleet is genuinely paying attention.

Audience member: You described accuracy and trust as prerequisites for building a good safety culture.

From your experience, what trade-offs do organisations make when they’re balancing short-term metrics against longer-term improvements?

TS: It really depends on the fleet.

If I understood your question correctly, you’re asking about the trade-offs organisations make when choosing a solution and deciding which metrics to prioritise.

For smaller, less complex fleets, they often focus on just the first two or three behaviours that matter most. It’s the Pareto principle. You tackle the highest-priority issues first.

The trade-off is usually around coaching. Smaller fleets tend to have limited resources, so they have to decide whether to rely more on the technology or spend significant amounts of time coaching individual drivers.

For example, they might ask themselves whether they should spend 20 hours a week coaching one driver who keeps showing signs of fatigue.

For larger, more complex fleets, the trade-off usually comes down to investment. How much is the business willing to spend to solve the problem?

From what I’ve seen, some safety leaders think of it as an either-or decision. Either they introduce dash cams or they risk upsetting their drivers.

In reality, it shouldn’t be viewed that way. The technology should reinforce good driving behaviours and help build trust with drivers, not undermine it.

So, ultimately, it depends on the fleet. I’ve seen the full range of approaches.

Audience member: Thank you.

ST: Great. Are we done?

Okay. Thank you very much, Tiffany. That was excellent.

TS: Thank you. I really appreciate it.

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