I am in the data analytics consulting services industry. One emerging technology trend I believe will be tranformational is the rise of generative Business Intelligence. Generative BI uses artificial intelligence to create analytics reports from text prompts using the company internal data. Many people currently see generative BI as a novelty feature that helps build dashboards faster. In reality, its real impact is not speed, but who gets access to insights. Generative BI tools are shifting analytics from a specialist-only function to something business users can interact with directly through natural language. What's underhyped is how much this changes decision-making workflows. Instead of analysts acting as intermediaries for every question, non-technical users can explore data themselves, ask follow-up questions, and iterate in real time. This doesn't eliminate analysts—it changes their role toward data modeling, governance, and ensuring that AI-generated insights are actually correct and trusted. Organizations that ignore this shift risk building analytics teams that scale poorly as demand for insights grows. The single signal I monitor to track this trend is adoption depth, not feature announcements—specifically, how often business users (not analysts) are querying data through tools like Power BI Copilot and using those outputs in real decisions. When generative BI becomes part of weekly management meetings rather than a demo feature, that's when its impact becomes undeniable.
The most misunderstood shift in the next 5 years isn't the generative AI boom, but the rise of Agentic AI Workflows. The vast majority of organizations see AI as a fancy search tool or content generator. The real upside is agentic orchestration: AI systems that take action without our explicit command, and act across an enterprise platform. From a world of AI-assisted things to AI-led things, becoming overseers of AI, rather than executioners of tasks. The single signal that I watch for this development is density of Autonomous API calls in the Enterprise Service Bus: specifically I look for the weight of agent-initiated system-level "intercoms" - autonomous activity - versus human-initiated intercoms. When the agent-to-system traffic begins to substantially outstrip the human-initiated stuff across core company-offering functions like Procurement or Customer Service, it marks the moment that AI has gone from celebrity novelty to the operating tissue of the firm. Gartner research backs this trajectory. They predict that "by 2028, at least 15% of day-to-day work decisions will be made autonomously through agentic and other autonomous systems. This doesn't just mean a fancier chat interface, but a core redesign of the enterprise: if you're focused on the chat interface you're missing the revolution happening under your nose where the actual work is performed. The move to an agentic enterprise relies heavily on trusting your data and governance. It's easy to ride the hype of what the AI can say back to you. The winners are going to be those who actually make a leap of faith and let the AI do the work[(14+source)] .
Here's what everyone's missing while obsessing over AGI: decentralized digital identity. It's underhyped because it sounds boring—wallets, credentials, verification. But it's going to fundamentally rewrite how we interact with everything online. The numbers prove it. The decentralized identity market hit $3 billion in 2025 with a projected 70.8% CAGR through 2035. Digital wallets set to double from 83 million to 169 million users in just one year. This isn't speculative crypto anymore. It's real infrastructure getting deployed. What makes it transformative? Instead of every company owning your data, you own your identity. One verified credential works everywhere. No more password hell. No more giving your personal info to every random app that asks. The signal I watch: enterprise adoption of self-sovereign identity standards. When major banks and governments start letting you bring your own identity instead of forcing you to create new accounts for every service, that's when this goes from experimental to inevitable. We're already seeing the early signs. By 2030, you'll wonder how we lived any other way.
Autonomous exception handling in operations networks gets far less attention than it deserves. Most people still associate AI in supply chains with chatbots or demand forecasting. That view misses what is actually changing daily work. The real shift comes from systems that spot problems, weigh options, and resolve issues before anyone notices a failure. This moves operations from reactive to preventative without adding headcount. We already see this at Togo through HarnessOS. When a shipment runs late or a vendor misses a milestone, the platform does more than raise an alert. It evaluates the business impact, pulls context from similar past situations, and takes action. Sometimes it resolves the issue automatically. Other times it routes the problem to the right person with a clear recommendation. Traditional automation cannot do this. Scripts only follow predefined steps and break when conditions change. What makes this overlooked is the current obsession with generative AI for content. Operational AI that makes decisions in messy, real-world environments creates far more leverage. One customer we work with once had three people monitoring shipments full time. Today, one person handles exceptions because the system identifies and resolves about 60 percent of issues on its own. That change directly affects cost, speed, and reliability. The metric that matters most is resolution without human involvement. When that rate climbs from 40% to 60% to 75%, the work itself changes. Roles shift from monitoring to oversight and improvement. Companies that track this metric build real advantages. They improve outcomes instead of deploying tools that only look impressive in presentations. This technology matters because supply chains fail in unpredictable ways. No team can script every scenario. Systems trained to operate under uncertainty can adapt at scale. That is the shift worth paying attention to.
Agentic AI is the most underhyped trend that will reshape work over the next 2-5 years. Most people know chatbots. But agentic AI is different. These systems do not just answer questions. They reason, plan, and act. They complete multi-step tasks without constant human input. Here is why this matters: Gartner predicts 40% of enterprise apps will have AI agents by late 2026. That is up from just 5% in 2025. The market will grow from $7.8 billion today to over $52 billion by 2030. But the hype has not caught up to reality. Most coverage focuses on chatbots and image generators. The powerful shift happening in back offices stays hidden. AI agents now handle entire workflows - reconciling transactions, drafting proposals, managing supply chains. The signal I track: enterprise deployment rates. Right now only 11% of companies use agentic AI in production. But 93% of IT leaders plan to deploy agents within two years. That gap between intention and action tells the whole story. We are at the inflection point. I see this daily in my work. I use Claude Code to build systems that handle complex tasks - document processing, compliance checks, data analysis. The tool reasons through problems and writes code that works. Tasks that took hours now take minutes. The companies reporting 5x to 10x returns on agentic AI investments are not exaggerating. I have seen 66% productivity gains and 20-35% cost reductions firsthand. The breakthrough is quiet. It happens in spreadsheets and databases, not in headlines. But by 2028, Gartner says 33% of enterprise software will include agentic AI. That is when the world will notice what already changed.
An important trend that I believe will gain significance in the coming years is the creation of a system for provenance infrastructure of digital content. Although some people incorrectly classify this trend as a niche issue or simply a form of watermarking, in fact, the development of a system for provenance infrastructure of digital content will replace the lack of context for digital content created in an environment of cheap and indistinguishable generation. From my experience in developing GPTZero, it is clear to me that the most effective way to detect digitally published content after the fact is not sufficient. The existing classifications for digitally created content will not hold up when users and models evolve together. It will be more beneficial to begin verification of the content being attributed to these technologies earlier in the process, thereby allowing the institution to make determinations about the process of creation as well as the resulting content. This will change the incentives of users, allowing the effort and authorship of the content to become visible again, ultimately changing the way users behave. The only indicator that I track to determine whether provenance tools will become standard practice is whether or not they will transition from being optional accessories to becoming the default mechanismFoundational tools supporting existing platforms like LMSs, application systems and document management systems. Ultimately, the true measure of the success of provenance efforts will be determined by their acceptance and use by these intermediary organizations shipping these tools into existing systems, as opposed to flashy demonstrations found during the early stages of development.
Head of Business Development at Octopus International Business Services Ltd
Answered 3 months ago
Personalized AI copilots for professional workflows. Not the flashy, catch-all chatbots, but the quiet helpers built around very specific tasks--legal reviews, compliance checks, onboarding flows, structuring work. People still treat them as minor UI conveniences, yet when they're trained properly and paired with human judgment, they cut down on context-switching, speed up decisions, and make knowledge-heavy roles a lot steadier. We've been experimenting with custom LLMs in-house for regulatory monitoring, profiling client entities, and helping junior staff quickly track down precedent documents. What's surprised me isn't how fast they are, but how consistently they surface details and how easy they make it to audit a line of reasoning. A solid copilot doesn't take the wheel; it just lays out the road with fewer blind spots. The signal I watch most closely is how quickly teams are feeding and maintaining their own proprietary knowledge inside these systems. Once an assistant understands your internal compliance logic--not just what's in the public rulebooks--you start to see real leverage without adding risk. That's the shift I expect: less talk about AI as a client-facing feature and more about AI becoming part of a company's internal governance backbone. Almost no one is focused on that yet, but that's where the lasting value will come from.
An underhyped trend is how cyber insurance requirements are becoming the next set of security standards. In strategic planning, when clients pursue coverage, we see insurers shaping which controls get funded, which will drive broad adoption over the next two to five years. The signal I track is which security measures are required for various levels of cyber insurance within underwriting questionnaires and coverage terms. Even if businesses believe they have enough security solution in place to be protected, they may not be able to meet the requirements necessary to get cyber insurance.
One emerging technology trend I believe will genuinely matter over the next 2-5 years is AI agents integrated into core business workflows: not as standalone tools, but as decision-making layers across marketing, operations, and customer experience. It's often misunderstood as simple automation, when in reality it's about systems that can interpret context, act across platforms, and continuously optimize outcomes with minimal human intervention. The single signal I monitor is how many companies move AI agents from experimentation into revenue-impacting production use; especially in areas like personalization, lead qualification, and operational efficiency. Once AI starts owning measurable business outcomes, that's when its real impact becomes undeniable.
The trend I think people are missing is privacy first, on-device intelligence becoming the default for business apps. Most conversations jump straight to bigger models and faster clouds. In reality, the next wave of value comes from bringing intelligence closer to the user, so apps can make smart decisions without shipping sensitive data. As a CEO building and delivering apps every day, I see how much friction data governance creates for real organizations. When logic runs locally, teams move faster, users trust the product more, and compliance stops being a blocker instead of a feature. This matters over the next few years because regulation will continue to tighten, and customer expectations around data handling are already shifting. Apps that feel responsive, respectful, and reliable will win long before anyone asks what model is under the hood. From an operational perspective, this approach also reduces infrastructure costs and complexity, which directly affects margins and delivery timelines. The single signal I watch is how often clients ask for offline-capable features and explicit data residency guarantees at the start of a project. That question now shows up earlier in app talks.
Being the Partner at spectup, one emerging technology trend I believe will truly matter over the next few years is internal agent orchestration, not consumer chat tools, but AI systems coordinating real work across teams. What I have observed while working with startups is that most companies do not struggle with ideas, they struggle with handoffs. I remember advising a US based growth stage company where sales, finance, and ops all used good tools, yet nothing moved smoothly. The issue was not talent or effort, it was fragmented ownership across systems. Agent orchestration changes this by letting software manage workflows end to end, triggering actions, checking constraints, and escalating only when human judgment is needed. This is misunderstood today because it is confused with automation, which it is not. Automation follows rules, orchestration manages intent. At spectup, we started experimenting with this internally for investor readiness workflows, and the difference showed up fast. One of our team members noticed fewer follow ups, fewer missed steps, and far less internal chasing. The reason this matters financially is predictability. When workflows stabilize, forecasting improves, delivery timelines tighten, and leadership decisions get cleaner. That directly impacts capital efficiency, especially during fundraising. The single signal I monitor is how often teams manually reconcile information between systems. When that number drops without headcount increases, orchestration is working. Most people expect this trend to feel dramatic, but it will feel quiet. Fewer meetings, fewer status checks, fewer surprises. In my opinion, the companies that adopt this early will not talk about it much, they will just move faster with less stress. That is usually how real advantages show up, and it is exactly the kind of leverage we look for when helping founders scale with confidence at spectup.
Proactive communication platforms are going to dominate, and most businesses still operate in reactive mode. They wait for customers to reach out with problems instead of anticipating needs and reaching out first. Your systems already know when something's wrong. A shipment is delayed. A service outage happens. A payment fails. An account is about to expire. But how many companies actually notify customers before they have to ask? Not many. We've built proactive workflows into Nextiva's platform, and the difference is night and day. When you message someone about an issue before they notice it, you flip the entire interaction. Instead of them being frustrated and calling you, you're being helpful and solving it early. That completely changes how customers perceive your company. This isn't rocket science. It's just connecting your data to your communication channels and setting triggers. Order delayed? Send a text. Service down? Push an alert. Subscription ending? Email a reminder. It's basic stuff, but almost nobody does it consistently. The metric that matters is outbound-to-inbound message ratio. Most companies are ninety percent reactive. When you start seeing thirty or forty percent proactive outreach, that organization has figured out how to use their data. I track this because it tells you who's actually preventing problems instead of just fixing them after the fact.
Ambient computing in commercial equipment is massively underappreciated right now. People hear "smart devices" and think about apps and dashboards, but the real transformation happens when equipment makes intelligent decisions without human intervention. I'm watching how machine learning models can adjust refrigeration cycles, water usage, and cleaning schedules based on actual demand patterns rather than fixed timers. The metric I track is adaptive efficiency gain, which is how much energy and water consumption decrease when systems self-optimize compared to factory settings. In our subscription model at Easy Ice, this translates directly to lower operating costs and fewer service calls. What makes this trend significant isn't the technology itself; it's that it fundamentally changes what "maintenance" means. Instead of scheduled interventions, you get systems that prevent problems before they start and optimize themselves for each unique installation environment. A restaurant that serves 200 customers on Monday and 800 on Friday shouldn't run the same ice production schedule on both days. Ambient systems learn these patterns and adapt automatically. The shift toward truly autonomous equipment operation will redefine service expectations across industries. When your ice machine knows to ramp up production before your lunch rush, adjusts water flow based on mineral content variations, and delays defrost cycles during peak demand, all without anyone programming these responses, you're seeing ambient computing at work. That's the future worth preparing for.
The underhyped trend I'm watching is local AI inference—the ability to run large AI models directly on personal devices, without needing cloud access. Right now, most people associate AI with server farms, APIs, and constant internet connectivity. But we're starting to see a quiet shift. Thanks to advances in model quantization and on-device acceleration, people are running serious language models and image generators on laptops and even smartphones. Not toys—real tools. Why does this matter? Because it breaks the cloud's monopoly on intelligence. It makes AI more private, more accessible, and far cheaper to scale. For emerging markets, remote teams, and privacy-conscious users, that's a game-changer. Imagine an offline AI that helps with contracts, translation, or coding—even if your Wi-Fi is down or your budget is tight. The single signal I watch: GitHub repo stars and forks for open-source projects like LM Studio, llama.cpp, and Ollama. When developers start adapting their workflows to these tools, it's not just hype. It's momentum. Most of the world isn't ready for this yet. But give it 2-5 years, and the idea that "real AI" needs the cloud is going to feel outdated.
I think the most underhyped trend is the quiet buildout of interoperable product data rails for circular commerce. It sounds boring, which is why it gets missed, yet it will matter more than shiny AI features. As sustainability pressure rises, brands need machine-readable proof of materials, lifecycle impacts, ownership, and recovery paths. Without shared rails, recycling claims stay fuzzy, compliance stays manual, and tech budgets leak into one-off integrations. What excites me is the shift from dashboards to plumbing. When product identity, material composition, and chain of custody move through common schemas, marketplaces, and advertisers can transact with confidence, regulators can verify claims, and partnerships form faster. That unlocks real corporate development value because diligence, integration, and scale stop being bespoke exercises. The single signal I watch is procurement APIs exposing standardized product passport fields at scale. When major buyers require those fields for bids, adoption accelerates overnight. I have seen this movie in adtech and fintech. Once the rails exist, innovation stacks on top. The next five years will reward operators who invest early in the boring infrastructure that turns sustainability and recycling into verifiable, monetizable systems. It aligns with my operating style from deals to partnerships.
Energy storage beyond lithium is still flying under the radar. People are pouring their attention into bigger AI models, but none of that scales without a grid that can store huge amounts of power reliably and cheaply. I got a taste of the problem when I spent a week off-grid in Spain last summer. Solar panels were everywhere, yet the choke point was obvious: storage. Lithium works for phones and cars, but for the grid it's pricey, touchy, and tangled up in geopolitics. The real action is in sodium-ion, solid-state, and new thermal systems. Those are the technologies that will quietly reshape everything. The signal I watch is CATL's sodium-ion shipments and the early pilots running in China. Once you see consistent movement there, you know mass manufacturing is taking hold--and when that happens, Europe and the U.S. usually feel the shock wave a couple of years later.
Everyone obsesses over massive AI models that require warehouse-sized data centers. I think the real revolution is happening in the opposite direction. Small Language Models (SLMs) running directly on your phone or laptop are the future. I realized this last year when I disconnected my internet and still managed to run a decent chatbot locally on my MacBook using Llama. It wasn't perfect, but it worked without sending a single byte of data to a server. That changes everything for privacy and cost. Most companies can't afford to burn cash on API calls forever, and consumers hate waiting for laggy cloud responses. The trend is moving toward "Edge AI." This means the processing happens right where the user is, not in a server farm. It is underhyped because it doesn't look as flashy as a trillion-parameter model. The signal I watch isn't software release notes. It's hardware specs. I specifically track the Neural Processing Unit (NPU) capabilities in new smartphone chips from Apple and Qualcomm. When those chips get powerful enough to run a GPT-3.5 equivalent offline without draining the battery in an hour, the cloud AI bubble will deflate.
I'm with Gotham Artists, a boutique speaker bureau, and the technology trend I think is going to matter way more than people realize over the next few years but is pretty widely misunderstood right now is this shift from everyone using the same generic AI tools to businesses actually building personalized AI systems trained on their own data.Here's what I mean: Right now, if I'm using ChatGPT and my competitor is using ChatGPT, we're basically working with the same brain. Yeah, it makes us both more efficient, but there's not much differentiation there. We're just both getting faster at producing similar outputs. The real competitive advantage is going to come from AI that's trained on your specific stuff—your customer history, your workflows, your institutional knowledge, the patterns in your business that took years to develop.For us, that could look like an AI trained on our entire speaker roster, a decade of booking history, what different clients actually value, industry-specific patterns we've learned. That kind of system wouldn't just spit out generic content it would actually reflect the judgment and understanding we've built up that nobody else has. Our competitors literally couldn't replicate it because they don't have our data.The reason this is misunderstood is people hear "train your own AI model" and immediately think that's only something Google or Microsoft can do. Like you need a team of PhD researchers and a massive budget. But the reality is the tooling for fine-tuning smaller models on your business data is getting way more accessible. It's becoming less technical every quarter.The specific signal I'm watching is pretty straightforward: when does the cost to fine-tune and run a business-specific AI drop into what feels like a normal small-business software budget? I'm thinking roughly under $5K to set it up initially and maybe a few hundred bucks a month to keep it running.When those economics shift and I think we're maybe 12 to 18 months out—personalized AI stops being this cool competitive edge and just becomes table stakes for staying relevant. The companies that are thinking about this now and preparing for it will build advantages that actually stick. The ones just relying on the generic tools everyone else has access to are going to find themselves competing purely on speed, not on any real insight or differentiation.
The industry's current obsession with trillion-parameter models is an architectural dead end for scalable production. We are rapidly hitting diminishing returns on general intelligence relative to the exorbitant environmental and financial costs of compute. The pragmatic shift, and the one currently undervalued, is toward Small Language Models (SLMs) deployed directly on edge devices. This is a fundamental topology change. By decoupling intelligence from the cloud, we eliminate network bottlenecks and drastically reduce inference latency. On-device processing ensures sensitive data never leaves the user's control, solving the privacy compliance nightmare by design rather than policy. Furthermore, the energy efficiency of running quantized models on local NPUs is orders of magnitude better than querying a massive data center for routine tasks. In my architectural reviews, I track the "parameter-to-utility" ratio as the single most important signal of maturity. We are finding that specialized, sub-7-billion parameter models, fine-tuned for specific domains, consistently outperform generalist giants in real-world reliability and speed. The future of AI isn't bigger; it's distributed, dense, and local.
One emerging technology trend I believe will genuinely matter over the next two to five years is decision intelligence layers built on top of existing data and AI systems. This is often misunderstood as just another analytics or AI tooling category, but the real shift is not about generating more insights. It is about shaping how decisions actually get made inside organisations, especially as complexity increases and human attention becomes the bottleneck. Most teams already have dashboards, models, and forecasts. What they struggle with is turning that information into consistent, high-quality decisions across functions. Decision intelligence focuses on mapping decision paths, constraints, incentives, and trade-offs, then using automation and AI to support judgment at the moment it matters. That has real implications for pricing, risk, growth strategy, and operations. It moves AI from an output generator into a decision partner, which is where long-term value sits. The single signal I monitor is whether these systems start getting adopted by operators, not analysts. When frontline teams and leaders rely on them to make everyday calls, not just to report on the past, that tells me the category has crossed from theory into impact. Once decision quality becomes a measurable, improvable asset, this trend will stop feeling abstract and start shaping how modern organisations actually run.