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Q Research Software

Q Research Software is analysis software designed to help market researchers quickly find the story in their survey data.

Q Research Software Overview

Q Research Software is analysis and reporting software designed to help market researchers quickly find the story in their survey data.

Automates the grunt work – Everything that can be automated is automated, including cleaning & formatting data, statistical testing, generating tables, updating analyses and reproducing reports.

DIY advanced analysis and visualization – With the latest machine learning & statistical techniques, Q uses smart systems and automation for widely used methods including correspondence analysis, latent class analysis, regression, MaxDiff, Shapley, and TURF. It also fully supports the R language.

Share insight-filled stories – Visualize information to show patterns that matter and design reports that make storytelling effortless with seamless Microsoft Office and Displayr integration.


“It’s so user-friendly that you can design a MaxDiff in Q even if you have no concept of analysis whatsoever. With the warnings that pop up, Q holds your hand as you’re designing it.” Eddie Sheppard, Vice President, Calgary Office, Leger

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Helping market researchers to quickly discover stories in their data. Everything that can be automated, is automated. Including cleaning & formatting data, statistical testing, generating tables, updating analyses an... Read More

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Bottom Line

If you want to use predictive analytics for market research purposes only, Q Research is tailor built for the job. It has a ton of automation features that streamline the process of analyzing market research, saving workers a lot of time. Many companies appreciate this kind of niche-focused tool so certainly Q Research has its market.

However, if you need predictive analytics for a lot of different data analytics use cases, you might be better served with a different tool with a broader focus.

Product Description

Unlike the general-purpose predictive analytics tools in this list, Q Research focuses on a niche — market research. It automates tasks like cleaning and formatting data, statistical testing and updating reports, and it offers advanced visualization capabilities.

It includes both an intuitive, point-and-click interface for less experienced users and a coding environment for advanced data scientists.

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March 4, 2024

New online master’s in AI from Purdue is designed for people who build AI systems and for people who use them


Interdisciplinary degree takes aim at artificial intelligence skills shortage in the workforce

WEST LAFAYETTE, Ind. — Applications are open for Purdue University’s new 100% online Master of Science in artificial intelligence degree, which features two majors, one for people who build AI systems and one for people who make use of them. 

Purdue’s interdisciplinary AI master’s degree is designed especially to meet the needs of two distinct sets of working professionals: AI builders and AI translators. AI builders are scientists and technologists who need robust training in machine learning, statistical methods, deep learning and data science to build and implement AI infrastructure. AI translators are business leaders and policymakers who need to make informed decisions regarding artificial intelligence and lead teams to adopt, incorporate and employ AI in their organizations. Among their roles are determining what results from AI systems mean and how those results should factor into business decisions and actions.

In a survey by information technology consulting firm Deloitte, more than two-thirds of executives reported that their firms had an AI skills gap. Job-tracking site Glassdoor reports a worldwide shortage of trained personnel in the field and a pressing need both to train new employees and to re-skill or upskill existing employees.

“AI is inherently interdisciplinary, with applications across domains,” said Dimitrios Peroulis, senior vice president for Purdue University Online. “We have designed our online AI master’s program to leverage Purdue’s interdisciplinary strengths and intentionally built a program that meets the needs of learners with either a highly technical background or for those who do not have a technical background.” 

Learners in both tracks will benefit from coursework in such topics as the foundations of artificial intelligence, artificial intelligence ethics, and policy and social implications of artificial intelligence. AI builders take additional classes in machine learning and data science. AI translators have additional classes in applications of AI in the business, nonprofit and public sectors along with data management, data analysis and communication.

“Purdue’s program is unique in recognizing the importance of preparing leaders, business professionals and administrators to work in collaboration with programmers and technical experts to harness the power of AI,” said Cherie Maestas, professor and head of Purdue’s Department of Political Science and one of the faculty members involved in developing the new AI master’s program. “AI is rapidly transforming everyday practices in the workplace. Leaders in all areas need to understand the fundamentals of AI technologies and their applications to utilize them for bold, innovative solutions.”

All students complete a capstone course providing them with an opportunity to apply their learning to a project that may be useful in their workplace immediately. Students also have a wide range of interdisciplinary electives to choose from in rounding out their curriculum, allowing them to customize their degrees to their own needs and interests and the needs of their employers, and to garner useful professional skills, for example in leadership, change management and project management. 

The online courses are developed and taught by a diverse, campuswide collection of the same internationally known faculty researchers who teach on Purdue’s flagship campus. Because AI is a rapidly evolving field, the program takes a future focus by incorporating the most current research to provide up-to-date information to students. Purdue has been ranked among the top 10 Most Innovative Schools six years running and is a top 10 public university in the U.S., according to U.S. News & World Report and QS University Rankings. 

“We have a long history at Purdue of delivering online options that are just as high quality as our top-ranked on-campus programs, and we’re excited to bring that passion and expertise to such an important domain,” said Milind Kulkarni, professor and head of Purdue’s Elmore Family School of Electrical and Computer Engineering, who has been involved in developing the new AI master’s program. “Whether you’re someone with a technical background who wants to learn about modern AI techniques and applications, or someone who needs to understand how your company or organization can best take advantage of cutting-edge AI technologies, our program is going to have something for you.”

A total of 30 credit hours are required to complete Purdue’s online Master of Science in artificial intelligence. Students who average six credit hours per semester can graduate in a year and a half. Summer courses are available to expedite time to completion.

The AI master’s degree is among numerous Purdue interdisciplinary initiatives designed to address research in and advancement of AI technologies, including the Purdue Computes initiative and Purdue’s Institute for Physical AI .

“Purdue has extensive strengths in the field of AI research and we decided to apply these strengths to develop a new online master’s in AI to meet workforce needs in Indiana and beyond,” Peroulis said. “By offering an online master’s in AI, Purdue can cater to a broader audience, including working professionals in the U.S. and international students, addressing the growing demand for AI skills.”

For more information on Purdue’s fully online Master of Science in artificial intelligence and to apply, visit the program webpage .  There is no application fee for this program.

About Purdue University

Purdue University is a public research institution demonstrating excellence at scale. Ranked among top 10 public universities and with two colleges in the top four in the United States, Purdue discovers and disseminates knowledge with a quality and at a scale second to none. More than 105,000 students study at Purdue across modalities and locations, including nearly 50,000 in person on the West Lafayette campus. Committed to affordability and accessibility, Purdue’s main campus has frozen tuition 13 years in a row. See how Purdue never stops in the persistent pursuit of the next giant leap — including its first comprehensive urban campus in Indianapolis, the new Mitchell E. Daniels, Jr. School of Business, and Purdue Computes — at https://www.purdue.edu/president/strategic-initiatives .

Writer: Greg Kline

Media contact: Brian Huchel, [email protected]

Sources: Dimitrios Peroulis, [email protected]

Milind Kulkarni, [email protected]

Cherie Maestas, [email protected]

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InfoQ Homepage News How Continuous Discovery Helps Software Teams to Take Product Decisions

How Continuous Discovery Helps Software Teams to Take Product Decisions

Mar 14, 2024 4 min read

Ben Linders

According to Neil Turner, continuous discovery for product development is regular research that involves the entire software product team, and that can actively inform product decisions. Equating continuous discovery to weekly conversations with one or more customers can be misleading. Combining quantitative and qualitative research methods can help software teams gather data and understand what is behind the data.

Neil Turner spoke about how Redgate does continuous discovery for product development at Agile Cambridge 2023 .

Turner defines continuous discovery for product development as:

Regular customer research; By the team building the product; In pursuit of a desired outcome.

The main pitfalls that software teams experience when attempting continuous discovery are chasing weekly targets, carrying out unfocused research, and neglecting other channels of customer insights, such as metrics and surveys, as Turner explained:

Many teams get fixated on hitting weekly targets, and on speaking to a certain number of customers a week. This can lead to poor quality research as teams focus on research quantity over quality.

Turner mentioned that software teams can end up carrying out unstructured and unfocused customer research for the sake of hitting their targets (even if they are self-imposed targets), rather than considering the most appropriate research to help inform their product assumptions and decisions:

Just because a team is speaking to their customers on a regular basis doesn’t mean that they are carrying out high quality customer research. Most teams will benefit from a mixture of quantitative and qualitative data and it can be risky to make decisions based on the insights from a few customers.

Combining quantitative research methods such as surveys and metrics, with qualitative research methods such as customer interviews, can help teams to gather data across their customers and to better understand what is behind that data, Turner said.

Not all teams at Redgate carry out continuous discovery, Turner mentioned. It’s an approach that works well with an established product, but it’s not a one-size-fits-all approach, he added. It’s very much a case of a team choosing the most appropriate research approach to take given what they need to learn and the work being undertaken. This choice of research approach will be driven by the product designer working in, or with the team, Turner said.

Teams at Redgate that do carry out continuous discovery will often do this via short bursts of customer research, rather than say running a session at a set time each week, Turner explained:

They might plan for 2-3 days of focused research a month. This makes it easier to plan, schedule, and prepare for customer research sessions. It also makes it easier to see trends as insights are collected over the course of a few days, rather than say having weeks between sessions.

Teams will use tools such as Calendly to help automate recruitment and will often share the responsibility of facilitating sessions, along with writing up notes, Turner said. For example, some teams have set up a rota so that there is less of a dependency on a designer running all the research activities.

Turner mentioned that teams carrying out continuous discovery have been able to establish a good cadence of exploring a problem space to identify opportunities, validating ideas being worked on (such as via prototypes), and getting feedback for features that have made it into a product. This supports a dual-track discovery and delivery approach.

Teams also have a better understanding of their customers and can better empathise with their challenges. After all, it’s one thing reading some feedback from a customer; it’s quite another to hear that feedback directly from the customer’s mouth, Turner concluded.

InfoQ interviewed Neil Turner about continuous discovery for product development.

InfoQ: What did your software teams learn from doing continuous discovery?

Neil Turner : Teams have learned that there is no set approach to continuous discovery and that it isn’t the answer to every research question. For example, we have a team at Redgate whose focus is early research and development. They are better placed to run more traditional upfront research, rather than continuous discovery. They tend to work on very early concepts and don’t want to be slowly drip fed customer insights. Instead, they will typically prototype a concept and get early feedback through blocks of customer research sessions. Teams have also learned that continuous discovery takes a surprising amount of effort from the team. It’s not just a case of scheduling some sessions with customers and hoping for the best. Sessions have to be carefully planned, well run, and then properly analysed as a team. The benefits are worth the costs, but there are certainly costs.

InfoQ: What advice do you have for teams that want to start with continuous discovery?

Turner : My main advice for any team starting with continuous discovery is to level up their understanding of continuous discovery, to start small, and to adapt their approach. Too many teams hear about continuous discovery, jump into a cookie cutter version of continuous discovery (because they don’t know enough to refine their approach), and then give up when it’s not delivering the wealth of customer insights they expect it to.

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Staying in the Loop: How Superconductors are Helping Computers “Remember”

Superconducting loops may enable computers to retain and retrieve information more efficiently.

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Computers work in digits — 0s and 1s to be exact. Their calculations are digital; their processes are digital; even their memories are digital. All of which requires extraordinary power resources. As we look to the next evolution of computing and developing neuromorphic or “brain like” computing, those power requirements are unfeasible.

To advance neuromorphic computing, some researchers are looking at analog improvements. In other words, not just advancing software, but advancing hardware too. Research from the University of California San Diego and UC Riverside shows a promising new way to store and transmit information using disordered superconducting loops.

The team’s research, which appears in the Proceedings of the National Academy of Sciences , offers the ability of superconducting loops to demonstrate associative memory, which, in humans, allows the brain to remember the relationship between two unrelated items.

“I hope what we're designing, simulating and building will be able to do that kind of associative processing really fast,” stated UC San Diego Professor of Physics Robert C. Dynes, who is one of the paper’s co-authors.

Creating lasting memories

Picture it: you’re at a party and run into someone you haven’t seen in a while. You know their name but can’t quite recall it. Your brain starts to root around for the information: where did I meet this person? How were we introduced? If you’re lucky, your brain finds the pathway to retrieve what was missing. Sometimes, of course, you’re unlucky.


Distinct circulating current paths in a 4-loop network show the possible switching activity that allow flux to travel between loops. (cr: Q-MEEN-C / UC San Diego)

Dynes believes that short-term memory moves into long-term memory with repetition. In the case of a name, the more you see the person and use the name, the more deeply it is written into memory. This is why we still remember a song from when we were ten years old but can't remember what we had for lunch yesterday.

“Our brains have this remarkable gift of associative memory, which we don't really understand,” stated Dynes, who is also president emeritus of the University of California and former UC San Diego chancellor. “It can work through the probability of answers because it's so highly interconnected. This computer brain we built and modeled is also highly interactive. If you input a signal, the whole computer brain knows you did it.”

Staying in the loop

How do disordered superconducting loops work? You need a superconducting material — in this case, the team used yttrium barium copper oxide (YBCO). Known as a high-temperature superconductor, YBCO becomes superconducting around 90 Kelvin (-297 F), which in the world of physics, is not that cold. This made it relatively easy to modify. The YBCO thin films (about 10 microns wide) were manipulated with a combination of magnetic fields and currents to create a single flux quantum on the loop. When the current was removed, the flux quantum stayed in the loop. Think of this as a piece of information or memory.

This is one loop, but associative memory and processing require at least two pieces of information. For this, Dynes used disordered loops, meaning the loops are different sizes and follow different patterns — essentially random.

A Josephson juncture, or “weak link,” as it is sometimes known, in each loop acted as a gate through which the flux quanta could pass. This is how information is transferred and the associations are built.

Although traditional computing architecture has continuous high-energy requirements, not just for processing but also for memory storage, these superconducting loops show significant power savings — on the scale of a million times less. This is because the loops only require power when performing logic tasks. Memories are stored in the physical superconducting material and can remain there permanently, as long as the loop remains superconducting.

The number of memory locations available increases exponentially with more loops: one loop has three locations, but three loops have 27. For this research, the team built four loops with 81 locations. Next, Dynes would like to expand the number of loops and the number memory locations.

“We know these loops can store memories. We know the associative memory works. We just don’t know how stable it is with a higher number of loops,” he said.

This work is not only noteworthy to physicists and computer engineers; it may also be important to neuroscientists. Dynes talked to another University of California president emeritus, Richard Atkinson, a world-renowned cognitive scientist who helped create a seminal model of human memory called the Atkinson-Shiffrin model.

Atkinson, who is also former UC San Diego chancellor and professor emeritus in the School of Social Sciences, was excited about the possibilities he saw: “Bob and I have had some great discussions trying to determine if his physics-based neural network could be used to model the Atkinson-Shiffrin theory of memory. His system is quite different from other proposed physics-based neural networks, and is rich enough that it could be used to explain the workings of the brain’s memory system in terms of the underlying physical process. It’s a very exciting prospect.”

Full list of authors: Uday S. Goteti and Robert C. Dynes (both UC San Diego); Shane A. Cybart (UC Riverside).

This work was primarily supported as part of the Quantum Materials for Energy Efficient Neuromorphic Computing (Q-MEEN-C) (Department of Energy DE-SC0019273). Other support was provided by the Department of Energy National Nuclear Security Agency (DE-NA0004106) and the Air Force Office of Scientific Research (FA9550-20-1-0144).

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Counterpoint Research: TomTom is uniquely positioned for the software defined car era

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Counterpoint Research is a global technology market research firm specializing in the technology, media and telecommunications industries. As part of its ongoing analysis, it recognizes TomTom as an industry leader when it comes to in-vehicle experience, digital cockpit technology and navigation. We sat down with one of the organization’s founders to learn more.

Developments in the smartphone world are a sign of what's to come in vehicles.

Counterpoint Research says TomTom is uniquely position for the future of in-vehicle tech.

TomTom's vertical integration allows it to offer a strong digital cockpit platform.

Gen-AI and voice assistants are going to be so common, drivers will demand them in their cars.

Neil Shah is an industry analyst and founder of Counterpoint Research . With a background spanning engineering, telecoms, industry analysis, consumer electronics and now automotive tech, in particular in-vehicle infotainment, Shah is an insightful and forward thinker with an analytical eye that spans market segments.

Here’s what he has to say about TomTom's Digital Cockpit and automotive tech.

TomTom: Can you describe and summarize the lay of the land in the automotive IVI industry? How has it changed in recent years, where is it going and what are some of its biggest challenges?

There are a lot of parallels between vehicle tech and smartphones, and how these have evolved — especially IVI tech. Since most vehicle drivers are already using smartphones, they have become technologically mature and expect similar, or better, experiences from their IVI systems.

We have gone from basic phones, to feature phones, to smartphones with bigger displays and rich features, like cameras. We’ve also seen an increase in compute power and an increase in breadth of applications and connectivity. There is a clear and similar trend happening in the automotive industry and its IVI space.

We say that IVI has become more interactive and more intuitive. That has been possible due to the growth in semiconductors and compute power which has delivered better GPUs capable of powering those bigger displays. Alongside this we have Wi-Fi, 4G and 5G connectivity which allows us to connect easily and use more complex, data-heavy applications in the car.

While we’re seeing these technologies added in the dash, the wider cluster they’re part of is changing rapidly as well — to match the central display and provide a holistic experience. This is creating a step change in how drivers interact with their cars. You have knobs and then you go to a touchscreen, and that becomes a little unintuitive. So, across the board, we are seeing the entire instrument and infotainment cluster change, not just the displays.

The next stage in how we interact with the car is being driven by advancements in artificial intelligence and natural language voice assistants, which should help further elevate and integrate the interaction experience in a safe and natural manner.

TomTom: How can companies, like TomTom, stay ahead of emerging trends in automotive technology? And what’s making TomTom unique in the prevailing market?

When thinking about staying ahead of the curve, I like to look at the heritage or core competencies a tech company like TomTom brings to the industry. For example, in the mapping and navigation space there are just a couple of industry pacesetters, and TomTom is clearly one of them.

But as we trace its history, what makes TomTom unique is that it’s more vertically integrated across its business, especially with the digital cockpit, than most. TomTom has a long history of working in the vehicle, integrating systems and building in-car experiences, but it also develops the core technologies, such as mapping and navigation, which are important parts of that experience.

Having deep control over the software, navigation services, UX and UI, places TomTom in a unique position where it can optimize in-vehicle tech and experiences across the entire stack.

This leads to a more seamless, integrated solution that brings everything in the car together in one package — voice assistants, navigation, mapping, location search and so on. On top of that, TomTom’s steps to build a partner ecosystem to complement its first party offerings and bring that complete experience to OEMs, which might not have the expertise, positions the company very well in this booming market.

As we transition from buttons to screens to voice, being able to successfully integrate these multi-modal forms of vehicle interaction, and still provide an excellent and safe experience, will be what separate the players.

TomTom: We see increasing emphasis placed on in-car technology and being connected , but what role do TomTom and the digital cockpit have in delivering satisfying experiences for drivers and passengers?

Navigation is the number one app for the IVI right now. The driver’s impressions of the in-vehicle experience can be based entirely on how well the navigation works.

Having a very high quality, real-time map and an exceptional navigation experience is super important for every driver and every carmaker. If the experience is not that great, the driver will never use it and winning them back will be very hard.

There is a big opportunity to differentiate the in-vehicle experience from experiences mirrored from the phone and make it the preferred experience. The UX, navigation, ADAS and car sensors all need to be integrated. As cars continue to get more advanced, how they display information to the driver and how it’s consumed over the entire stack, not just the infotainment display, and how seamless it is, will be the key defining factors for good and bad IVI stacks.

This is where TomTom is going to play a big role. By owning the entire navigation stack, from the entire compute-optimized software to integrated rich content and services, and providing a complete digital cockpit experience, TomTom is in a unique position to offer something to OEMs and carmakers that can be fully integrated with their vehicles — that’s hard to compete with.

TomTom: What impact do you think the integration of TomTom's navigation technology has on the overall infotainment experience? And how is TomTom Digital Cockpit addressing the challenges of the industry?

One of the main challenges in the industry today is how to take control of the entire vehicle experience and improve it. As we move to software-defined vehicles, the software and IVI become the components that define what the car is like. So naturally, carmakers want total control over that, or they want to have a clear say on how it will be designed and developed, more so now than ever before.

TomTom Digital Cockpit, as a flexible, modular and integrated reference system, is offering carmakers and OEMs that opportunity in a unique way. Not only is it allowing carmakers to personalize their IVIs to their brands, while bringing market leading navigation and a growing collection of apps, it’s also helping them innovate and develop tech in areas where they previously lacked expertise.

Carmakers are seeing the value in working with partners like TomTom who understand how vehicles and their systems are made, can do software and, importantly, understand drivers.

The partner ecosystem TomTom is developing — the partnership with Microsoft and the Overture Foundation — is also important to the overall quality and potential of TomTom’s technologies.

There’s not one thing that makes TomTom Digital Cockpit or its maps address the challenges of the industry. It’s in all the above, everything happening together that’s making it such a valuable name in the industry.

[Want to read more about TomTom’s relationship with Microsoft? Click here ].

TomTom: You met TomTom at CES and spent a couple of hours on the stand viewing the company’s demos, what stood out to you and distinguishes TomTom’s solutions from others in the market?

For me, TomTom’s Map Maker and developer experience stood out and is a massive step change from what I saw just a couple of years ago.

It’s exciting to see because I think this will enable and catalyze overall development and innovation with TomTom’s map. As that improves, more partners will join the ecosystem and further innovate — whether on maps or the digital cockpit. This will directly, or indirectly, affect and improve the IVI experience too.

The other thing that stood out to me was obviously the new maps, [ TomTom Orbis Maps ]. They are fantastic and the demo was great. The way the look and feel of the map is evolving is another step change.

At the time, I was discussing it with Corinne and said, “This is such a beautiful map.” With integration, hopefully we see that beauty preserved after OEMs, carmakers and other developers have used and built on the map for themselves.

I want to see that map in my car.

[You can watch a version of that demo, click here .]

TomTom: Partnerships are proving to be key in the evolution of in-vehicle technology. How important are they to digital cockpit technology and what partnerships of TomTom’s excite you the most?

The Microsoft partnership and the relationship TomTom has with the Overture Foundation are two particularly exciting examples.

Open data is also incredibly important to building good tech today. We need more open data, and we need to do it in a collaborative way so people can innovate their differentiations on top of it. With base data that’s open there will be fewer silos, the experience of working with maps and location data will be more uniform and there will be accessibility for everyone. As a combination, this could lead to great things.

Collaboration and openness, and being strong on MS Azure, are massive advantages. Many enterprise developers use Azure and having the maps integrated into that makes it easy for devs to work with and is going to help obtain more probe data to feed back into the map and elevate the overall map quality and content.

And of course, working with Microsoft is helping bring things like ChatGPT-powered AI voice assistants to the car and in-vehicle experience, which is helping bridge the gap between those creating these technologies and the driver, while also acting as a bridge between where we are now and the next phase of the industry.

TomTom: Generative-AI powered voice assistants are fast entering the automotive industry. Are they becoming the new industry standard? How do you see this technology becoming a part of the IVI stack?

Before gen-AI technologies become common in vehicles we need the base, the compute, to take full advantage of gen-AI capabilities and to have the models running natively on the device, the car. There will also be the cloud to get the data, but we will see a hybrid approach where you might go to the cloud for some information while some processing is done onboard the vehicle. But for the ultimate experience, most of the computation will have to happen on a model running at the edge, on the car itself.

Even though the human-machine interaction (HMI) is multimodal ¬— using buttons, screens and so on — voice is emerging as the most natural and safe way of interacting with the car. If we can replace all the stuff that’s not needed, like extra clicks that can prove to be distracting, and replace that with one natural command providing targeted and accurate action, then we can transform how we interact with cars while also making them safer.

Gen-AI isn’t just about voice, though. Personalization is going to be a big thing for AI-based IVI in the future. Personalizing the in-vehicle experience and blending personal data with OEM data or partner data, will give faster answers to queries, with improved accuracy. And with AI, carmakers can create more personalized experiences, the lights of the cabin can be changed to suit the needs of the driver before they ask, for example.

Adoption of this technology should be quick too. Voice is the most natural way to interact, and smartphones are priming us to use it every day. We’re already starting to see more gen-AI capable phones hit the market, unlocking unique experiences, and this will explode over the coming year.

Soon everything will have a Copilot -like prompt. Every device will have a large language model (LLM) running in the background, and everyone will become used to using voice to get what they want, cutting through application silos. It will be a very natural progression. Consumers will be waiting for cars to adopt it faster!

TomTom: You’ve followed TomTom and its evolution for many years, you’ve even ranked TomTom as an industry leader in the infotainment sector. What has contributed most to TomTom attaining that recognition?

TomTom has come a long way since its early days in navigation and mapping, and it continues to keep moving forward.

Further evolving maps and developing a fully integrated digital cockpit stack as a platform is delivering what customers want. It’s a step change in terms of user experience and possibility.

Alongside this, the partnerships and the company’s market strategy over the past few years are what’s allowing TomTom to take its score to a very high level as we [Counterpoint Research] rank location technology companies.

It is difficult to match in terms of a more integrated location platform offering, from location-centric software to services powering advanced in-vehicle experiences.

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