What is one underutilized technology in your industry that you believe others should explore? What specific problem does it solve better than conventional solutions?

Good Day, CBCT is severely underused in general practice and addresses a diagnostic dilemma that cannot be answered by any two dimensional system. When performing root canal therapy or implant placement, CBCT provides me with the necessary three dimensional visualization of root morphology, bone density, and relation to vital anatomical landmarks. It completely alters my approach to treatment before even picking up my instrument. For instance, with regard to retreating endodontically treated teeth, CBCT will allow for the detection of missed canals or fractures not visualized on conventional radiographic films. For implant surgery, CBCT provides exact measurements of bone width and angulation, resulting in a streamlined surgery and less surprises intraoperatively, meaning fewer modifications during treatment and better predictability. Operationally, it also cuts down appointment time and improves patient acceptance of treatment plans as patients are able to fully comprehend their diagnosis visually. The simple principle I follow: use CBCT if the information it provides will alter your treatment approach. If you decide to use this quote, I'd love to stay connected! Feel free to reach me at, drleung@angelaleungddspc.com and @angelaleungddspc.com

One underutilized technology in insurance that I believe others should seriously explore is AI-powered vehicle inspection through customer-submitted photos. In Latin America and many other markets, buying full-coverage car insurance still requires a physical inspection. An agent has to see the car in person, check for damage, take photos, and determine whether it is insurable. That single dependency has kept the entire industry tied to offline processes. It is the reason most insurers cannot offer a fully digital experience for their most valuable product. At Eprezto, we built an AI system where customers take photos of their vehicle themselves using their phone. The AI analyzes those images, flags possible damage, and helps determine whether the car qualifies for coverage. That replaced an entire offline process that the industry had accepted as permanent. The specific problem it solves is the physical bottleneck that prevents digital scale. Before this, selling full-coverage online was considered impossible in our market because the inspection had to happen in person. That meant coordinating schedules, sending agents, and waiting for paperwork. Every step added friction, cost, and time that made the customer experience slow and frustrating. With AI-powered inspection, the entire process happens on the customer's phone in minutes. Conversion rates for full-coverage policies improved because the friction that caused abandonment was eliminated. Operational costs dropped because we no longer need a network of physical inspectors. And the system scales without adding headcount. What surprises me is how few companies in insurance have adopted this approach. The technology exists. The customer demand for convenience is obvious. But most of the industry remains anchored to physical processes because that is how it has always been done. The lesson is that the most impactful technology in any industry is usually not the most complex. It is the one that removes a structural constraint everyone else has accepted as normal. AI-powered inspection is not flashy. It simply makes something that was previously impossible entirely possible. And that is where the real competitive advantage lives.

Most 3PLs are still using warehouse management systems built in the 1990s, but here's what nobody talks about: computer vision is sitting right there, ready to eliminate the single biggest source of fulfillment errors, and almost nobody's deploying it. When I was running my fulfillment operation, pick errors cost us about $47,000 annually in replacements and customer service time. We had barcode scanners, we had training, we had quality checks. Didn't matter. Human pickers grab the wrong SKU when products look similar or when they're rushing to hit rate targets. That's just reality. Computer vision solves this differently than conventional barcode scanning because it doesn't rely on the picker doing anything correctly. You mount cameras at pack stations, and AI visually confirms the actual product matches the order before the box gets sealed. Not the barcode. The actual item. I've seen warehouses cut mispicks by 89% within the first month of deployment because the technology catches errors that scanners miss entirely. The cost has dropped so dramatically that small 3PLs can now afford it. Five years ago you needed enterprise budgets. Today you can deploy computer vision at a pack station for less than the annual cost of one full-time QA person. But adoption is maybe 3% of the industry because most warehouse operators assume it's still prohibitively expensive or too complex to integrate. At Fulfill.com, we're starting to see the most forward-thinking 3PLs use this as a competitive differentiator. When a brand is comparing providers and one can guarantee 99.7% accuracy versus the industry standard of 99.1%, that difference is massive at scale. On a million orders annually, that's 6,000 fewer angry customers. The real unlock happens when you combine computer vision with your existing WMS data. You start catching patterns. Maybe certain SKUs get confused constantly, or specific pickers need retraining, or your bin locations are creating systematic errors. Barcode scanners can't tell you any of that because they only know if someone scanned something, not if they scanned the right thing. This technology exists right now and it's affordable. The only barrier is inertia.

The Technology: Headless Electronic Health Record architectures. The most underutilized technology in the behavioral health industry is the headless Electronic Health Record. Conventional EHR solutions are monolithic. They force clinicians to use rigid, outdated user interfaces that were designed primarily for insurance billing rather than patient care. This creates massive click fatigue and is a leading driver of administrative burnout among high performing therapists. As a CTO with a clinical background, I view bad software as a behavioral risk. A headless architecture solves this by completely decoupling the backend database from the frontend user interface. We use a legacy EHR strictly as a secure, compliant data repository. However, we build our own custom, highly streamlined frontend applications for our clinical team to actually interact with on a daily basis. This specifically solves the problem of cognitive overload. Instead of forcing our providers to adapt their natural workflow to terrible software, we build an interface that perfectly mirrors their clinical cadence. It strips out all the unnecessary administrative friction, allowing them to complete their documentation quickly and reserve their deep mental bandwidth for actual crisis intervention and patient care.

The biggest issue with nonprofit fundraising technology has nothing to do with the latest trends. It is the payment infrastructure that most organizations still have not fixed. I have watched nonprofits run beautifully designed campaigns and then lose donors at the finish line because the actual act of giving was clunky, manual, and slow. That is not a strategy problem. That is a plumbing problem. Electronic payment systems, including automated recurring giving, solve this in a direct and measurable way. The friction disappears. The follow-up calls disappear. The paper checks disappear. What stays is a clean, immediate experience that respects the donor's impulse to give and actually captures it. For a small nonprofit team already stretched thin, that automation also buys back hours that should be going toward relationships, not administrative cleanup. The adoption has been slower than it should be, and I understand why. Changing a process that technically still functions feels like a risk when your margin for error is small. But there is a real difference between a process that works and one that works well. Organizations that have modernized their payment infrastructure consistently retain more donors and have cleaner data to show for it. The flashiest technology gets all the attention. But the nonprofits I respect most got the boring stuff right first. Reliable electronic payment infrastructure is not exciting. It is just one of the highest-return investments a fundraising team can make.

I'm Runbo Li, Co-founder & CEO at Magic Hour. Open-source AI video models are the most underutilized technology in the creative tools space, and it's not even close. Most companies in this industry are either building proprietary models from scratch or slapping a thin wrapper on a single API. Both approaches miss the real unlock. Here's the problem everyone's trying to solve: how do you give non-technical people the ability to make professional video content without a production team, editing software, or weeks of learning curve? The conventional answer has been closed, proprietary systems. You're locked into one model's strengths and weaknesses, one company's roadmap, one pricing structure. That's a trap. What we do at Magic Hour is build templates on top of the best open-source models available. When a new model drops that handles motion better, or lighting better, or faces better, we can integrate it fast and route users to the right tool for their specific use case. A small business owner making a product ad needs something fundamentally different from a sports fan making a highlight reel. One model will never serve both well. I saw this play out in real time. Early on, we were using Stable Diffusion for everything. It was great for certain styles but terrible for realistic human motion. The moment open-source video models started improving, we could swap in better options without rebuilding our entire stack. A competitor locked into their own proprietary model? They're stuck retraining for months. We shipped the upgrade in days. The specific problem this solves is speed of iteration at the product level. In AI, the best model today will not be the best model in six months. If your architecture can't absorb that change quickly, you're building on sand. Open-source gives you optionality, and optionality is the most valuable asset in a market moving this fast. The companies that win in AI tooling won't be the ones that build the best model. They'll be the ones that build the best system for deploying whatever model is best right now.

The underutilized technology in the agency space is direct integration with telephony infrastructure, specifically using SIP trunking and programmable voice providers like Twilio and Telnyx, instead of relying on prebuilt voice platforms that abstract the phone layer away. Most agencies building voice AI for their clients treat the phone number as someone else's problem. They pick a voice AI platform, use whatever calling capability it offers out of the box, and accept the constraints that come with that. The constraints are real. Per minute pricing you can't negotiate. Call routing logic you can't customize. Recording and transcription stored on infrastructure you don't control. Limited control over caller ID, dial plans, and regional compliance. And almost no visibility into the call quality metrics that matter when a client's customer has a bad experience. Going one layer down and integrating directly with a telephony provider changes the economics and the capability surface. Per minute costs drop significantly because you're buying wholesale instead of retail. You can route calls dynamically based on time of day, geography, or any business logic you want. You can record and store calls on your own infrastructure, which matters for regulated industries and clients with specific data residency needs. You can deploy local numbers in dozens of countries without waiting for your voice platform to support them. The specific problem it solves better is margin control. Agencies using packaged voice platforms often discover that their biggest cost is the per minute markup their platform charges, which grows linearly with client usage. The more successful the client, the thinner the agency's margin gets. Direct telephony integration flips that curve. Costs grow slowly, and the agency captures the difference as the client scales. The reason it stays underutilized is that telephony has a reputation for being complicated, and it used to be. Ten years ago you needed a telecom background to touch this stuff. Today the developer experience from the major providers is good enough that an agency with a capable technical partner can get running in days, not months. The gap between reputation and reality is where the opportunity sits. My recommendation is to treat the phone layer as a strategic asset, not a commodity dependency.

An underutilized technology in residential cleaning is a shared Google Sheet used as a live master schedule for the whole crew. In our business, most of the work happens in the field, so the problem is not a lack of software, it is getting the right information to the right person at the right time. A simple shared sheet lets every team member see the day's jobs, special notes, and what products are needed at each location without logging into a complicated system. Compared with more complex scheduling platforms, it reduces confusion, cuts training time, and keeps the team focused on the work instead of the tool.

The most underutilized technology in small business operations right now is conversational voice AI for inbound customer communication — and the gap between its actual capability and the industry's adoption of it is significant. Most small businesses in home services, professional services, and wellness still rely entirely on a human answering the phone or on voicemail. The result is predictable: missed calls during busy periods, after-hours inquiries that go unanswered until the next business day, and inconsistent customer experiences that depend entirely on who happens to pick up. Voice AI solves a specific and concrete problem better than any conventional alternative: availability without headcount. A well-built voice agent handles inbound calls 24/7, books appointments, answers common questions, qualifies inquiries, and escalates complex issues to a human — all without the cost or variability of a receptionist. Unlike basic IVR systems ("press 1 for billing, press 2 for support"), modern voice AI conducts actual natural-language conversations. What makes it still underutilized: the perception that it requires enterprise-level investment or technical sophistication to deploy. That perception is outdated. At Dynaris, we've deployed voice AI for businesses with one to twenty employees at a cost that's a fraction of part-time reception staff. The specific problem it solves better than conventional solutions: the missed-call problem. Studies consistently show that the majority of callers who reach voicemail don't leave a message and don't call back. A voice AI that answers and engages within two rings recovers that revenue. For a plumber or HVAC company, a single recovered emergency call often pays for months of the service.

PostgreSQL Row-Level Security. Most SaaS teams handle multi-tenancy by adding WHERE tenant_id = ? to every query in their application code... and it works until someone forgets one. I've seen that exact bug cause a cross-tenant data leak in production at a startup I advise. RLS moves the isolation enforcement to the database itself... the policy runs on every query regardless of what the application code does, so a missed filter in one endpoint can't expose another customer's data. It's been in PostgreSQL since version 9.5, it's free, and it takes an afternoon to implement. Yet most teams I work with haven't heard of it because the tutorials all teach application-level filtering first and never revisit the decision.

AWS Spot Instances are one of the most underutilized cost levers available to teams running on AWS, and the reason is almost always the same: they weren't part of the original architecture conversation. Teams plan infrastructure for steady-state capacity and figure out scaling later... at which point Spot feels like a disruptive redesign rather than a natural component of the compute layer. The case for Spot is straightforward: If you understand your traffic patterns well enough to identify your baseline compute requirements, you can run that baseline on Reserved or On-Demand Instances and use Spot to handle scale-out. The cost difference is absolutely with it. Spot typically runs 70 to 90 percent cheaper than On-Demand for equivalent instance types. The constraint is that your application needs to tolerate interruption. Spot Instances come with a two-minute termination warning, so workloads requiring guaranteed, fast-resuming availability aren't a good fit. Where Spot shines is in stateless workloads, batch processing, containerized services with graceful shutdown handling, and any compute layer where you've designed for fault tolerance. The teams that benefit most are the ones that do the analytical work upfront: gather traffic data, model their baseline versus peak requirements, and design their scaling strategy with Spot in scope from the beginning. It's an architectural investment that pays back consistently, sprint after sprint.

One underutilized technology in our industry is integrated benefits data analytics that combines HRIS, enrollment, and claims reporting into a single view. In our work with a mid-sized employer, analyzing those combined data sources revealed drivers like dependent participation and pharmacy spend that were not obvious from headline renewals. Using modeling from that integrated view allowed us to consider a level-funded approach and design changes instead of immediately shopping the plan. That approach solves hidden-cost and volatility problems more effectively than reacting to renewal quotes alone and is worth employers exploring.

Enthalpy based ventilation controls outperform fixed timer strategies in mixed climates. They measure moisture and heat together, preventing expensive over ventilation. Conventional controls often dump outdoor air indoors when humidity remains punishing. That creates sticky rooms, longer runtimes, and avoidable compressor strain. I recommend pairing enthalpy sensors with variable speed equipment and zoning. The result is quieter comfort, lower latent load, and cleaner indoor air. Installers should start with humid regions, retrofit projects, and tight homes. Those applications reveal savings quickly, while comfort complaints fall sharply.

One underutilized technology is automated log file analysis for SEO and content operations. Most marketers focus on analytics platforms, but those tools show what users did after arriving, not how search crawlers actually moved through the site. Log analysis exposes crawl waste, orphaned pages, slow discovery, and valuable sections that bots rarely revisit. That matters because indexation problems often look like content problems when the real issue is poor crawl prioritization. I recommend reviewing logs during site changes, migrations, and large content expansions. It solves technical visibility issues better than standard audits because it shows search engine behavior directly, not assumptions based on page templates or tool estimates.

Most nonprofits are one text message away from a donation they never collected. I work with mission-driven organizations every day helping them run fundraising campaigns, and the technology gap I see most often is not about awareness. It is about adoption. Text-to-give has existed for years and most organizations still are not using it. The conventional approach relies on email campaigns and event donation tables. Both require a donor to be in the right headspace at the right time. That window is narrow. Text-to-give catches donors in the moment they actually feel something. One number, one text, one link, and the gift is done before the feeling fades. The resistance I hear most often has nothing to do with cost or complexity. The tools are accessible, the setup is straightforward, and donors already have their phones in their hands. The hesitation is almost always about familiarity, and familiarity is the easiest barrier to clear once you see the results firsthand. The organizations that make the switch tend to have the same reaction: they cannot believe how long they waited. Spontaneous donations go up, giving at events increases, and the experience finally matches how people actually behave. Sometimes the most powerful technology available is the one that feels almost too obvious to take seriously.

The technology that is most underutilized in legal marketing right now is generative engine optimization, or GEO, and the gap between the firms doing it intentionally and those ignoring it entirely is growing fast. Most law firms are still thinking about visibility purely in terms of Google search rankings. That still matters, but it's no longer the whole picture. A significant and rapidly growing share of people looking for legal help are getting their answers directly from AI tools like ChatGPT, Perplexity, and Google's AI Overviews. They're asking questions and getting synthesized responses that may or may not include your firm. Most lawyers have no idea whether they're showing up in those answers or not. The problem conventional SEO doesn't fully solve is that ranking on page one of Google and being cited or recommended by an AI engine require meaningfully different approaches. AI systems pull from content that is clear, authoritative, well structured, and directly answers specific questions in plain language. The dense, jargon-heavy, keyword-stuffed content that law firm websites have relied on for years is exactly the kind of content these systems tend to ignore. GEO addresses that gap directly. It's about structuring and writing content in a way that makes it extractable and citable by AI engines, not just rankable by traditional algorithms. For law firms, where trust and visibility are everything, being the source an AI recommends when someone asks about a legal situation in your market is an enormous competitive advantage. Very few firms are thinking about it yet. That window won't stay open indefinitely.

One underutilized technology I recommend is red light therapy. We have integrated it into our clinic to support cellular health, fight inflammation, speed up tissue repair, and shorten injury recovery time. In our practice it has not only helped speed tissue healing and overall recovery, but also patients often report better sleep, improved mood, more energy, and enhanced athletic performance. For issues of persistent inflammation and delayed recovery, red light therapy has been a huge boost to what we do in getting patients back to activity sooner.

Structured intake processes that can quickly adapt to multiple different types of loans are widely recognised but rarely implemented by many small-business lenders. The vast majority of lenders think of intake as a static document form; therefore, many lenders create friction very quickly with the lending transaction. For example, a lender will have very different data collection and documentation requirements for a real estate purchase than they will for a business acquisition. In addition, a lender will have very different data collection and documentation requirements for a refinance loan compared to a business acquisition. Using a dynamic borrower intake process allows lenders to obtain better quality data sooner in the lending transaction, ask the borrower fewer irrelevant questions, and move through the lending transaction more efficiently. The value of using this type of technology is not because it appears to be high-tech, but because it provides lenders with a better opportunity to eliminate unnecessary work before they begin to incur costs associated with completing a lending transaction. Poor-quality inputs during the early stages of a lending transaction typically have long-lasting effects through the remainder of the lending process resulting in long lead times, making multiple requests for the same information, and making it very difficult to find a suitable lending partner. An intelligent dynamic borrower intake process helps lenders identify missing information earlier in the lending process, clarify the borrower's expectations for mortgage financing, and allow for more efficient routing of lending opportunities from inception to completion. I believe there are many lender teams that should be looking into these technologies to create a much more efficient experience for the borrower and optimise their internal process.

One underutilized technology in my industry is structured 1-on-1 online coaching platforms that incorporate personalized pronunciation assessments. At Intonetic we use that format to diagnose each speaker’s unique sound patterns and build targeted practice rather than relying on generic lessons. Those platforms make it easier for coaches to deliver focused feedback and tailored exercises in real time, addressing issues that broad prerecorded courses often miss. I encourage more teams and professionals to explore personalized online coaching tools to improve clarity and vocal presence.

A technology more health brands should explore is first party identity stitching across paid media, site behaviour, and patient communication touchpoints. Many teams still optimise channels in isolation, which hides the real path to conversion. In telehealth, that blind spot leads to wasted spend, poor attribution, and messaging that feels disconnected from a person's actual intent. We see it solve the problem of fragmented trust better than conventional reporting tools because it reveals when interest is genuine, when hesitation is emotional, and when friction is structural. That makes acquisition sharper, education more timely, and follow up more relevant. In a sensitive category, continuity often matters more than reach.