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When we say \u201ctechnology\u201d there\u2019s a lot more than just artificial intelligence. Yet when talking about tech trends, AI is what most executives will point to. This year, leaders are seeing many trends around AI, from coding to handling multiple steps without human intervention to regulation. And a few executives will steer away from that conversation completely. We asked our Fast Company Impact Council<\/a> members what technology trends they see gaining steam this year, and received an onslaught of ideas. We share 24 of those here.<\/p>\n<\/div>\n In the music space, AI platforms will start incorporating more tools that protect copyright and ethical use, especially as AI tools increasingly become more integrated into artists\u2019 workflow. While there is justified pushback and fear around AI-generated art and music, AI will continue developing rapidly, and I predict there will be much-needed parameters to prevent artist exploitation balanced against more AI tools that are especially helpful for emerging artists to assist with everything from building websites to creating visuals. \u2014 Matt Mandrella<\/a>, City of Huntsville, AL<\/a><\/p>\n The problem of deepfakes will worsen significantly, leading to increased misinformation and higher levels of social engineering that lead to major breaches, high fraud levels, and losses. To counter this, enterprises of all sizes will have to leverage AI to more proactively monitor and respond to these deepfakes on social media, the internet, and the dark web, going well beyond the enterprise\u2019s traditional borders. Businesses will need to be able to go after attacker\u2019s infrastructure before it can be weaponized and used against their customers and employees. \u2014 Scott Harrell<\/a>, <\/strong>Infoblox<\/a> One of the most exciting trends this year is the rise of generative AI in drug discovery, with antibiotics as a powerful case study. Moving beyond early prediction and screening, today\u2019s generative models can design new molecules by embedding potency, safety, and other drug-like parameters directly into the system. We\u2019re using these models to design novel antibiotic candidates in silico<\/em>. We\u2019re also seeing more collaborative AI ecosystems that help these models learn and improve. Shared data and infrastructure further strengthen these systems\u2014especially in antibiotics, a foundation of modern medicine. \u2014 Akhila Kosaraju, MD, Phare Bio<\/a><\/p>\n<\/div>\n From my vantage point advising boards and C-suites in edtech, the strongest trends include AI-powered personalized learning tailored to individual needs, with skepticism around fully automated models. Leaders want augmentation, not replacement. Expect growth in tools that enhance decision-making, productivity, and workforce agility as organizations define how humans and technology work together. Align AI adoption with measurable business outcomes rather than novelty. \u2014 Alan Baratz<\/a>, D-Wave<\/a><\/p>\n At the core of a successful retail strategy is collaboration. Specialized vertical AI agents will change the way retailers and suppliers communicate and collaborate, by surfacing alerts and by leveraging AI missions that make autonomous decisions that give retail the optimization boost it needs. Currently, retailers, suppliers, and distributors each hold only a slice of the truth due to complex workflows, fragmented data, and cross-company processes that are siloed. Vertical AI agents can automate, negotiate, coordinate, and problem-solve, turning well-defined coordination into a competitive advantage. \u2014 Are Traasdahl<\/a>, Crisp<\/a><\/p>\n Strict data access legislation will increasingly affect Europe\u2019s competitive prospects in AI development. If companies are unable to collect enough data, they might push forward with biased AI models built on small data sets. Additionally, Big Tech companies from the U.S. and China will lobby for exemptions when operating in Europe. Its results are already seen in how the U.S. equates EU regulation with a digital tax against Big Tech. \u2014 Denas Grybauskas<\/a>, Oxylabs<\/a><\/p>\n<\/div>\n We\u2019re moving from AI as a novelty to AI that can actually reason and adapt in context. The next wave isn\u2019t just chat interfaces and LLMs, it\u2019s systems that learn from real-world behavior and actions. We\u2019re starting to see that show up in more interactive website experiences, including empowered personalization, where sites don\u2019t just guess what you want, but ask directly and adjust based on your response. For example, a grocery site might notice you consistently choose organic products and ask whether it should prioritize those going forward. \u2014 Kevin Laymoun, Constructor<\/a><\/p>\n The biggest AI trend isn\u2019t adoption\u2014it\u2019s accountability. As automation accelerates, leaders need to make human ownership visible: Who is responsible for decisions if something goes wrong? Pair every AI rollout with clear responsibility and explanation. That discipline builds trust faster than speed alone. \u2014 Tyler Perry<\/a>, Mission North<\/a><\/p>\n AI built on secure enterprise data, not broad consumer datasets, will accelerate as companies demand automation they can trust for accuracy and real decision\u2011making. But the real breakthroughs will come only when AI is embedded into the operating model, in a way that reshapes workflows, roles, governance, and decision rights instead of sitting on top of legacy processes. The fastest gains are likely to show up in back\u2011office automation, commercial augmentation, operations orchestration, and talent systems, where AI already has a track record of compressing cycle times and increasing performance. \u2014 Alice Mann<\/a>, Mann Partners<\/a><\/p>\n<\/div>\n The trend I\u2019m watching most closely isn\u2019t a single technology; it\u2019s the convergence of AI and trust infrastructure in the Global South. The Solvers we work with are asking how to make sure this works for communities that have historically been the last to benefit and the first to be harmed. That tension is where the most important and underreported scrappy innovation is happening right now. \u2014 Hala Hanna, MIT Solve<\/a><\/p>\n Agentic AI is the big shift. We\u2019re entering the era of AI that acts: agents that plan, call tools, and complete multistep tasks with humans in the loop, moving from demos to real workflows. We\u2019re prototyping interfaces for major brands where AI navigates on behalf of users. My advice: Pick one customer journey and rebuild it as if the user never touches a menu or form. That exercise reveals how much of your current UX is scaffolding AI can collapse. The real differentiator is the trust layer: reversible actions, intent signals, data provenance, and audit trails. That separates a demo from a product people rely on. \u2014 Peter Smart<\/a>, Fantasy<\/a><\/p>\n I predict an embrace of analog. Flip phones and landlines, CDs and records, point-and-shoot cameras, and other un-algorithmic tech are gaining traction among kids who know social media is preying on them, and among parents course-correcting the \u201ciPad kid\u201d phenomenon. \u2014 Lindsey Witmer Collins<\/a>, WLCM Studio<\/a><\/p>\n<\/div>\n AI-powered localization and AI conversational platforms are the technology trends gaining significant momentum within creator marketing. Tools like TikTok Symphony enable one creator asset to speak 20 languages while preserving voice and authenticity, transforming the economics of global creator marketing. One strong creator relationship can now serve 20 markets through AI localization. At the same time, Gemini and ChatGPT are hiring ad sales teams from TikTok, Snap, and Meta to build new ad formats where creators will play a central role across search and conversational AI. \u2014 Ben Jeffries<\/a>, Influencer<\/a><\/p>\n The future is at the intersection of AI and blockchain. As online activity is increasingly dominated by agents, those agents are going to need programmable money in the form of stablecoin to carry out their tasks. Blockchain technology excels at confirming authenticity, which will be highly relevant in areas ranging from validating model inputs to confirming the audit trail of a document being read by AI. \u2014 Michael Tannenbaum<\/a>, Figure<\/a><\/p>\n People keep saying SaaS is dying and maybe that\u2019s true for the big players, but there\u2019s real opportunity for smaller, industry-specific products. We\u2019re building agents using OpenClaw, but the process isn\u2019t intuitive. As we build internally, it\u2019s clear this space is just getting started, but the window to get it right is closing fast. \u2014 Kalie Moore, High Vibe PR<\/a><\/p>\n<\/div>\n Three trends are gaining steam. First, AI is becoming a teammate, embedded in workflows and able to act, not just advise. Second, change readiness and AI-native learning are now strategic: Continuous learning in the flow of work will define adaptability. Third, human skills rise in value. As AI expands what\u2019s possible, human wisdom (judgment, empathy, discernment) determines what matters. The future belongs to teams where technology and talent work side by side. \u2014 Jacqui Canney<\/a>, ServiceNow<\/a><\/p>\n First, there\u2019s no need for staff increases. We are able to outsource all services without having any employees and to run our organization with the minimum of labor cost. Second, all our outsourced providers use AI extensively\u2014our social media team in South Africa, developers in California, bookkeeping in Texas, fractional controller service nationwide, and assistant and chief of staff in North Carolina. \u2014 Larraine Segil, Exceptional Women Alliance<\/a><\/p>\n The most important technology trend is the convergence of AI and human-centered design. AI is rapidly accelerating concept generation and research synthesis, but its real value depends on ethical frameworks and inclusive intent. We also see growth in accessible design, as aging populations and diverse users demand better solutions, and accessibility shifts from regulatory requirement to consumer aspiration. The firms that succeed will pair emerging technology with empathy, rigor, and measurable impact. \u2014 Ben Wintner<\/a>, Michael Graves Design<\/a><\/p>\n<\/div>\n For two decades, the internet has been a visual experience mediated by screens, SEO, and pixels. But as conversational AI matures, voice is positioned to become the new browser. This year, we\u2019ll see conversational AI and voice in particular really take off, as brands realize the next platform war won\u2019t be fought over devices, but over who owns the conversational gateway to the internet. \u2014 Khozema Shipchandler<\/a>, Twilio<\/a><\/p>\n As AI adoption accelerates, institutional agility will determine the winners. Organizations that can reskill their workforce to take advantage of the disruptive power of AI the fastest will move ahead of their competitors. Using AI is only half the equation, and the less important half. The real challenge is redefining the work to translate AI innovation into productivity at scale. \u2014 Steve Holdridge, Dayforce<\/a><\/p>\n I see the design industry moving away from a scattershot mix of AI chat and image generation tools, toward integrating AI and automation into the actual design and delivery process. Major software platforms are absorbing startups or embedding AI directly into their ecosystems to improve stability and address IP risk. That consolidation is starting to replace design tool chasing with more intentional platform strategies. The firms that benefit most will be the ones building around workflows, not the novelty of the tool of the moment. \u2014 Steven McKay<\/a>, DLR Group<\/a><\/p>\n<\/div>\n AI agents for software development have improved by leaps and bounds. Instead of coding assistants, we\u2019re now working with coding agents that have moved far beyond simple code completion. The paradigm has dramatically shifted from AI assisting human coders to human coders now assisting the AI with coding. This shift moves us away from the incremental \u201cassembly line\u201d model toward a true orchestration model\u2014a new era where AI agents are becoming the primary drivers doing the tedious work of the software development lifecycle. \u2014 Alex Balazs<\/a>, Intuit<\/a><\/p>\n I\u2019m watching two trends closely. One is personal health data becoming real-time and actionable. Wearables are moving beyond steps and sleep into glucose monitoring and stress tracking. People aren\u2019t waiting for annual checkups. They\u2019re experimenting and adjusting daily. The other is a return to physical experiences. There\u2019s renewed interest in tangible tools, higher-end audio, vinyl, and devices that encourage offline focus. It\u2019s not anti-technology. It\u2019s intentional technology. People want better quality and less noise. Both trends are about control, over your body and your attention. That theme is only going to get stronger. \u2014 Logan Mulvey<\/a>, GoDigital Music<\/a><\/p>\n We are moving from AI models as the core value creation engine to novel\/proprietary training data for models being the true differentiator and providing defensibility moats. \u2014 Shely Aronov<\/a>, InnerPlant<\/a><\/p>\n<\/div>\n2.<\/strong> DEEPFAKES<\/strong><\/h2>\n
3. AI IN DRUG DISCOVERY<\/strong><\/h2>\n
4. PERSONALIZED LEARNING<\/strong><\/h2>\n
5. VERTICAL AI AGENTS<\/strong><\/h2>\n
6. REGULATION AFFECTS DATA COLLECTION<\/strong><\/h2>\n
7. AI ADAPTING IN CONTEXT<\/strong><\/h2>\n
8. AI ACCOUNTABILITY<\/strong><\/h2>\n
9. AI EMBEDDED IN THE OPERATING MODEL<\/strong><\/h2>\n
10. AI AND TRUST INFRASTRUCTURE IN THE GLOBAL SOUTH<\/strong><\/h2>\n
11. AGENTIC AI<\/strong><\/h2>\n
12. A MOVE BACK TO ANALOG<\/strong><\/h2>\n
13. AI-POWERED LOCALIZATION<\/strong><\/h2>\n
14. AI AND BLOCKCHAIN INTERSECTION<\/strong><\/h2>\n
15. INDUSTRY-SPECIFIC SAAS<\/strong><\/h2>\n
16. AI AS A TEAMMATE<\/strong><\/h2>\n
17. OUTSOURCED PROVIDERS USE AI<\/strong><\/h2>\n
18. AI AND HUMAN-CENTERED DESIGN<\/strong><\/h2>\n
19. VOICE AS THE NEW BROWSER<\/strong><\/h2>\n
20. TRANSLATE AI INNOVATION INTO PRODUCTIVITY AT SCALE<\/strong><\/h2>\n
21. AI FOR WORKFLOWS<\/strong><\/h2>\n
22. AI AGENTS AS SOFTWARE DEVELOPERS<\/strong><\/h2>\n
23. REAL-TIME, ACTIONABLE PERSONAL HEALTH DATA<\/strong><\/h2>\n
24. AI AS PROPRIETARY TRAINING DATA<\/strong><\/h2>\n