This Houston expert describes the main phases central to any innovation journey. Photo courtesy of Slalom

As a technologist, one thing I learned early in my career about the technology landscape is its constant improvements and I understood that companies who kept up with those changes remain successful and competitive. However, only companies mastering a disciplined innovation framework are truly able to harness the power of emerging tech to help them solve their most complex business challenges.

Innovative solutions come in all shapes and sizes, but not all of them should come to life. Specifically, when considering digital solutions, there are a few widely accepted innovation approaches in the product engineering field. This quick guide describes the main phases central to any innovation journey.

Feasibility Study

Ideating can be fun but executing a feasibility study will ground you on what will work and what may still be science fiction. The thought here is to spend two to four weeks doing research and talking to experts to answer a few key questions that will help you determine the feasibility of your idea or concept. Through the study, you will learn how to look at it from both a technology and a business perspective. More importantly, to answer the question 'Is it even possible to accomplish your goal with this technology?'

A subject matter expert (SME) will quickly tell you yes or no and why. If you find the technology is indeed suitable, then you will move on to evaluate the business feasibility. Does it make financial sense? Does it work within established business policies? Will there be a healthy Return On Investment (ROI) within an acceptable timeline? If you find positive responses to those questions, then you should feel confident to move on to a Proof of Concept (PoC) or even jump to a Minimum Viable Product (MVP). On the other hand, if either the technology is not feasible or the solution doesn't make business sense, then you've just saved yourself a lot of time, budget, and possibly headaches.

Proof of Concept

This phase is about testing the theory and proving the hypothesis, technically speaking. You'll need to go through a Proof of Concept if the technology solution you have in mind hasn't been tested in either a lab setting or in the field. Thinking outside of the box and innovating is all about trying new approaches and solving problems in a novel way, so you'll have to spend the time and budget ensuring it will work as expected. However, you must be very careful to not get carried away.

A proper PoC should take four to six weeks, max. It should help you quickly determine whether the technology will live up to its promise or if you need to pivot to another approach. Building a team with the right skillset is vital to this process because they are the ones evaluating the proposed solution and comparing it to the expected outcomes. Any signs of discord should empower the team to stop the project, saving further investment, and should help you decide if another approach is even possible. If all criteria has been met, then move on to the MVP stage.

Minimum Viable Product

At this point, you have confirmed the solution you imagined works and you are ready to unlock its potential. But you must start small. You must prioritize all the features you want this product to have and decide what the core functionality should be. This is important because if you choose too many features to start with, you may initially spend too much money and time and may even miss a window of business opportunity you may have lined up. Hence the name of the MVP, it is a product that employs the minimum time, money, and features while still being a viable product.

In summary, if you have an innovative idea for a technology solution, I recommend you first determine whether it's feasible, both technology and business wise, through a short and focused study. If feasible, then you can put that concept to the test through a PoC and determine its desirability. If this product is indeed desirable, then moving into building an MVP will help you understand its viability – and that is how you can successfully innovate while keeping risks at bay.


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Alfredo Arvide is the director for the products and innovation practice at Slalom Consulting in Houston, where he helps clients solve their most complex business challenges by leveraging emerging technologies and applying innovative technology solutions.

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Houston food giant Sysco to acquire competitor in $29 billion deal

Mergers & Acquisitions

Sysco, the nation's largest food distributor, will acquire supplier Restaurant Depot in a deal worth more than $29 billion.

The acquisition would create a closer link between Sysco and its customers that right now turn to Restaurant Depot for supplies needed quickly in an industry segment known as “cash-and-carry wholesale.”

Sysco, based in Houston, serves more than 700,000 restaurants, hospitals, schools, and hotels, supplying them with everything from butter and eggs to napkins. Those goods are typically acquired ahead of time based on how much traffic that restaurants typically see.

Restaurant Depot offers memberships to mom-and-pop restaurants and other businesses, giving them access to warehouses stocked with supplies for when they run short of what they've purchased from suppliers like Sysco.

It is a fast growing and high-margin segment that will likely mean thousands of restaurants will rely increasingly on Sysco for day-to-day needs.

Restaurant Depot shareholders will receive $21.6 billion in cash and 91.5 million Sysco shares. Based on Sysco’s closing share price of $81.80 as of March 27, 2026, the deal has an enterprise value of about $29.1 billion.

Restaurant Depot was founded in Brooklyn in 1976. The family-run business then known as Jetro Restaurant Depot, has become the nation's largest cash-and-carry wholesaler.

The boards of both companies have approved the acquisition, but it would still need regulatory approval.

Shares of Sysco Corp. tumbled 13% Monday to $71.26, an initial decline some industry analysts expected given the cost of the deal.

Houston researcher builds radar to make self-driving cars safer

eyes on the road

A Rice University researcher is giving autonomous vehicles an “extra set of eyes.”

Current autonomous vehicles (AVs) can have an incomplete view of their surroundings, and challenges like pedestrian movement, low-light conditions and adverse weather only compound these visibility limitations.

Kun Woo Cho, a postdoctoral researcher in the lab of Rice professor of electrical and computer engineering Ashutosh Sabharwal, has developed EyeDAR to help address such issues and enhance the vehicles’ sensing accuracy. Her research was supported in part by the National Science Foundation.

The EyeDAR is an orange-sized, low-power, millimeter-wave radar that could be placed at streetlights and intersections. Its design was inspired by that of the human eye. Researchers envision that the low-cost sensors could help ensure that AVs always pick up on emergent obstacles, even when the vehicles are not within proper range for their onboard sensors and when visibility is limited.

“Current automotive sensor systems like cameras and lidar struggle with poor visibility such as you would encounter due to rain or fog or in low-lighting conditions,” Cho said in a news release. “Radar, on the other hand, operates reliably in all weather and lighting conditions and can even see through obstacles.”

Signals from a typical radar system scatter when they encounter an obstacle. Some of the signal is reflected back to the source, but most of it is often lost. In the case of AVs, this means that "pedestrians emerging from behind large vehicles, cars creeping forward at intersections or cyclists approaching at odd angles can easily go unnoticed," according to Rice.

EyeDAR, however, works to capture lost radar reflections, determine their direction and report them back to the AV in a sequence of 0s and 1s.

“Like blinking Morse code,” Cho added. “EyeDAR is a talking sensor⎯it is a first instance of integrating radar sensing and communication functionality in a single design.”

After testing, EyeDAR was able to resolve target directions 200 times faster than conventional radar designs.

While EyeDAR currently targets risks associated with AVs, particularly in high-traffic urban areas, researchers also believe the technology behind it could complement artificial intelligence efforts and be integrated into robots, drones and wearable platforms.

“EyeDAR is an example of what I like to call ‘analog computing,’” Cho added in the release. “Over the past two decades, people have been focusing on the digital and software side of computation, and the analog, hardware side has been lagging behind. I want to explore this overlooked analog design space.”