At Tech Advisors, we see firsthand how artificial intelligence is shaping the future of business. While AI gets a lot of attention, smaller and more focused AI solutions often go unnoticed. Small language models (SLMs) are a great example. Unlike large AI models trained on vast amounts of data, SLMs focus on specific, confined information. Businesses can train them on internal documents, policies, or customer support data, making them more precise and efficient for unique needs. I've seen companies struggle to make large AI models work for their operations, only to find that a tailored SLM delivers better results with fewer resources. Another underappreciated area is autonomous mobile robots (AMRs) and co-bots. These aren't the futuristic, sci-fi robots people imagine but practical tools that improve efficiency in warehouses, factories, and even hospitals. They handle repetitive tasks like quality inspections and machine maintenance, freeing employees to focus on work that requires human judgment. One of our clients, a manufacturing firm, saw major gains when they introduced AMRs to assist with parts delivery. Instead of spending hours moving materials, workers could concentrate on precision assembly, improving both productivity and job satisfaction. Businesses should start looking beyond the biggest AI trends and consider what will truly make a difference in their daily operations. Investing in technology that solves specific problems-whether that's a small language model for customer support or a co-bot for routine tasks-can deliver faster and more meaningful improvements. The key is to think strategically about what tools will enhance efficiency without adding unnecessary complexity. In our experience, the right mix of targeted AI and automation makes the biggest impact, without the hype.
Video editing is one of the most time-consuming parts of production, requiring hours of sorting footage, syncing audio, and making precise cuts. AI-augmented editing assistants are an underappreciated technology that could completely transform this process, yet they don't get nearly the attention they deserve. AI-powered tools are already beginning to automate tedious editing tasks, from auto-selecting the best takes based on facial expressions and audio clarity to intelligently suggesting B-roll that matches the scene's tone. Features like automated dialogue syncing, instant colour grading recommendations, and AI-driven shot composition analysis could significantly speed up post-production workflows, allowing editors to focus on creative storytelling rather than repetitive technical tasks. One of the biggest challenges in modern video production is the demand for multiple formats, clients want their content delivered in landscape, portrait, and square formats to fit various social platforms. Manually reformatting every edit for TikTok, Instagram, YouTube, and beyond is incredibly tedious and time-consuming for editors. AI assistants could automate smart reframing, adjust compositions dynamically, and even optimise cuts for different aspect ratios, ensuring that videos look polished across all platforms with minimal manual intervention. For a production company like ours, this is game-changing. AI editing assistants don't replace human creativity but enhance efficiency, making tight turnarounds far more manageable without sacrificing quality. As AI continues to advance, these tools could bridge the gap between technical precision and creative intuition, making professional-grade editing faster and more accessible to filmmakers, content creators, and brands alike. It's an area that deserves far more investment and development.
One underappreciated area of tech development that I believe deserves more attention is technology designed to enhance, rather than replace, the human workforce. Too often, tech is developed with the sole aim of cutting costs by automating jobs entirely, prioritizing profit over people. While automation has its place, it can come at the expense of the human creativity, empathy, and critical thinking that make organizations truly thrive. At Carepatron, we have focused on building AI and automation tools that do not aim to replace healthcare professionals but instead support them. For example, automating repetitive admin tasks frees up clinicians to spend more time with their patients. This approach amplifies human potential rather than erasing it. The tech industry needs more investment in solutions that empower workers, tools that reduce stress, improve workflows, and help people do their jobs better. By focusing on augmenting the human experience rather than sidelining it for the sake of profit, we can create technology that fosters innovation while supporting the people who drive it. It is a more sustainable and ethical way to approach development and one that deserves far more attention.
From my experience mapping out content creation processes, I've found that process automation and workflow optimization tools are criminally underappreciated. Everyone's obsessed with generative AI and large language models, but they're missing the real game-changer - the tools that help us understand and optimize the actual human workflows these AIs are supposed to enhance. When we were building Penfriend, we discovered that a single blog post required over 22 distinct human decision points. That's just for one piece of content. Most businesses have hundreds of recurring processes like this, but they're throwing AI at problems they don't even fully understand. It's like trying to automate a factory when you don't even know how your workers actually assemble the product. The tech that deserves more attention is the boring stuff - process mining tools, workflow analysis platforms, and automation mapping systems. These tools help you understand the actual human processes before you try to enhance them with AI or other tech. I've seen clients blow six figures on AI implementations that failed because they never bothered to map out their existing processes first. Instead of another chatbot or AI image generator, we need more investment in technologies that help us understand and optimize the human side of the equation. Otherwise, we're just building faster ways to do the wrong things. I know it's not as sexy as the latest AI model or metaverse concept, but in my years of consulting, I've consistently seen that companies who invest in understanding and optimizing their processes first get exponentially better results from any technology they implement later. The future isn't about replacing humans with AI - it's about truly understanding how humans work and building technology that enhances those natural workflows.
In my experience as CEO of NextEnergy.ai, one underappreciated area is the use of AI in optimizing renewable energy, particularly solar energy management. At NextEnergy.ai, we have harnessed AI to transform solar panels into intelligent systems that analyze energy usage patterns, adapting to maximize efficiency. This has revolutionized energy savings for our clients, demonstrating AI's potential beyond traditional tech discussions. We employ AI algorithms that adjust solar output based on weather conditions, making real-time optimizations possible. For example, incorporating this technology has lowered energy costs significantly, with some clients in Northern Colorado experiencing reductions in utility bills of up to 30%. This not only cuts costs but also reduces carbon footprints, aligning financial savings with environmental responsibility. AI's role in predictive maintenance is another overlooked area. Our systems can foresee potential issues in solar infrastructure, preventing major disruptions and saving costs on repairs. This proactive approach is a game-changer for maintaining long-term efficiency and sustainability, making AI a compelling investment area for renewable energy.
One often overlooked area of technology that deserves more attention is the integration of cybersecurity with AI-driven threat intelligence. At NetSharx, we've seen the potential for AI to transform security measures. For instance, adopting AI-improved solutions can significantly reduce the mean time to respond to threats by up to 40%, as we've implemented for several enterprises. This integration allows for real-time analysis and response to security threats, a necessity in today's digital landscape where new threats constantly evolve. For example, our clients have benefited from reduced cybersecurity costs by utilizing AI-driven insights without the need for an expensive 24/7/365 SOC setup. The cost savings, alongside advanced protection, make this an invaluable investment. Many organizations remain hesitant due to the perceived complexity and cost of AI, but the value added through improved security and operational efficiency outweighs these concerns. By focusing on AI in threat intelligence, companies bolster their security posture while streamlining processes, enabling them to remain competitive in a digital-first world.
One underappreciated area of tech development that I believe deserves more attention and investment is edge computing. While cloud computing has taken center stage for its scalability and flexibility, edge computing is quietly emerging as a critical technology for the next wave of innovation, particularly in industries like healthcare, autonomous vehicles, and IoT. The idea behind edge computing is simple: instead of sending all data to a centralized cloud for processing, edge devices process the data locally, closer to where it's generated. This reduces the latency and bandwidth needed for real-time decision-making, which is crucial in scenarios where milliseconds matter. In healthcare, for example, wearable devices can process vital data on-site, immediately alerting doctors if there's an emergency, without waiting for cloud data processing. In autonomous vehicles, split-second decisions about navigation and safety need to happen on the edge of the network, without relying on distant cloud servers. I believe edge computing has been overshadowed by the widespread popularity of the cloud because it seems less glamorous. It's more of an infrastructure play, often behind the scenes, enabling other technologies to function seamlessly. But the potential impact it can have on real-time analytics, security, and data sovereignty is immense. By processing data closer to the source, we not only reduce strain on bandwidth but also improve privacy since sensitive data doesn't need to travel as far. As we move further into the IoT era, with billions of connected devices creating vast amounts of data, edge computing will be the linchpin that allows us to efficiently handle this influx. It's not just about cutting down latency; it's about creating systems that are more reliable, secure, and efficient. However, we still have a long way to go in terms of improving the infrastructure, increasing the adoption of edge devices, and reducing the cost of deployment. The value of investing in edge computing will only continue to grow as the demand for real-time, data-driven applications skyrockets. I believe this is a field that, if given the attention it deserves, could significantly shape the future of technology, enabling more autonomous systems, better healthcare outcomes, and smarter cities.
As someone who's been deeply entrenched in the evolution of authentication systems, I believe decentralized identity (DI) is a game-changing technology that is not getting the attention it deserves. In contrast to traditional, centralized systems, DI allows individuals to truly own and control their digital identities. This can revolutionuze how personal data is managed and shared, significantly reducing risks of identity theft. Our work with FusionAuth continually reveals how traditional identity systems can be cumbersome and inefficient. For instance, leveraging DI using blockchain technology can eliminate the need for storing sensitive data on centralized servers, thus reducing vulnerability to large-scale breaches. Imagine a world where your driver's license or library card is securely stored in a digital wallet, accessible only as you allow. Another avenue worth exploring is the integration of AI in security systems. AI's ability to learn and adapt makes it highly effective in enhancing authentication systems. By analyzing user behavior, AI can pre-emptively identify and neutralize threats, creating smarter security protocols. This technology can help achieve seamless and intuitive experiences in digital interactions, a leap forward in our industry's quest to blend security with user convenience.Decentralized Identity (DI) is an underappreciated technology that deserves more attention and investment. The idea of allowing individuals control over their digital identities, mirrored from physical items like licenses or memberships, is compelling. By using blockchain, users can manage and share their information securely, potentially reducing identity theft incidents. During my journey with FusionAuth, I've observed how decentralization can revolutionize data privacy. For example, in a CIAM system, DI can enable users to securely manage their identities without relying on third-party data storage. This shifts data ownership back to individuals, enhancing privacy and trust. Moreover, DI can streamline authentication across various platforms, eliminating the need for multiple passwords. In living through the digital identity evolution, I've recognized DI's potential to redefine access management by eliminating centralized vulnerabilities. Investing in DI could lead to more secure, user-centric digital environments.
From my experiences, I believe that the integration of AI in cybersecurity deserves more attention and investment. AI-driven security systems have revolutionized how businesses detect and respond to cyber threats. For instance, at Profit Leap, we use AI to monitor network activities, swiftly identifying anomalies that signal potential cybersecurity threats. This proactive approach not only minimizes damage but also improves overall network security. Implementing AI in cybersecurity has enabled businesses to respond in real-time to threats that traditional systems might miss. For instance, AI can detect patterns indicative of a data breach, instantly activating preventative measures. It's an area where the blend of advanced technology and strategic implementation can significantly protect and boost operational stability for businesses. Profit Leap has witnessed the impact of AI-powered cybersecurity tools in preventing data breaches and ensuring business continuity. By investing in AI cybersecurity solutions, companies can safeguard sensitive information, reduce vulnerabilities, and maintain customer trust, ultimately leading to sustaunable business growth.
AI development has largely focused on text-based systems, but multimodal AI-which combines text, voice, image, and video processing-has enormous untapped potential. This is critical for fields like education (interactive learning), Insurance (AI-driven claim process), and customer support (enhanced virtual assistants). Instead of businesses relying on separate AI tools for different tasks, investing in unified AI systems that process multiple data types simultaneously will create more natural, context-aware interactions. With OfficeIQ AI, we've seen the impact of multimodal AI in making knowledge more accessible, workflows more efficient, and decision-making more intuitive. The future isn't just chatbots-it's AI that sees, hears, understands, and acts intelligently in diverse environments.
One underappreciated area of tech development that deserves more attention and investment is edge computing. While cloud computing dominates conversations around data processing, edge computing offers a transformative way to reduce latency, enhance security, and improve real-time processing by bringing computation closer to the data source. This technology is particularly crucial for industries like healthcare, autonomous vehicles, and smart cities, where milliseconds matter. For example, in telemedicine, edge computing allows real-time processing of patient data at local nodes, reducing delays in diagnostics and treatment recommendations. Similarly, in manufacturing, edge AI can analyze equipment performance on-site, predicting failures before they happen without relying on distant cloud servers. Despite its potential, edge computing doesn't get the same level of mainstream investment as AI or blockchain. Yet, as IoT devices proliferate and data volumes grow, the need for decentralized, faster, and more efficient processing will only increase. Investing in edge computing now will future-proof industries that rely on speed, security, and real-time decision-making.
Real-time insurance pricing APIs are one area in tech that I feel is vastly underappreciated. Transparent pricing would go a long way to boosting customer satisfaction. Investing in dynamic pricing APIs that integrate with providers can vastly improve comparison shopping. Consumers typically get vague estimates instead of precise, real-time quotes. Dynamic pricing APIs offer accurate, instant quotes. I've seen early adopters of this technology improve customer trust and conversion rates significantly. More investment in this area could even disrupt the industry in my opinion.
SEO and SMO Specialist, Web Development, Founder & CEO at SEO Echelon
Answered a year ago
Good day, The underreported area of tech development worthy of more attention altogether is sustainable and energy efficient computing. With the growing need for computing power, especially with AI, cloud computing, and data centres becoming more common, the environmental impact of these technologies is being called into question. Advances in low power chips, renewable energy integration in data centers and in techniques to cool and reduce waste heat are critical to curtailing the carbon footprint of digital technologies. In the long run, it can end up saving money while also dramatically reducing the environmental impact of our burgeoning digital infrastructure. It could spring innovation in AI, machine learning, big data analytics, which means to make more climate neutral and sustainable solutions and products for these technologies, one that doesn't hurt in climate change.
Serverless computing is changing the way we create and launch applications, yet security in this realm is frequently overlooked. At Cloudraft, we think serverless security needs to be prioritized. It demands a distinct approach and tailored tools to tackle the specific challenges posed by short-lived functions and event-driven systems. Focusing on this aspect is essential for realizing the complete benefits of serverless technology.