Technology Trends Outlook 2025<\/a> is getting quite a bit of attention after its release over the summer.<\/p>\nHere\u2019s the thesis, as stated by the writers of this wide-ranging report:<\/p>\n
\u201cThe global technology landscape is undergoing significant shifts, propelled by fast-moving innovations in technologies. These are exponentially increasing demand for computing power, capturing the attention of management teams and the public, and accelerating experimentation. These developments are occurring against a backdrop of rising global competition as countries and corporations race to secure leadership in producing and applying these strategic technologies.\u201d<\/p>\n
In response, the authors unveil no less than 13 facets of enterprise AI to explore as we move into the end of a banner year for smart tech advancements. They refer to these as a \u201cbaker\u2019s dozen,\u201d but whatever you call them, as the study authors point out, these should be required reading for business leaders right now. Here\u2019s the unabridged list:<\/p>\n
1. Agentic AI<\/p>\n
In AI today, it\u2019s all about agents, agents, agents. Our generation has presided over not only the overall trajectory of AI, which is about four years old, but the rapid shift from text-based LLM models regurgitating facts to an agentic approach where AI increasingly \u201cdoes things\u201d on its own initiative.<\/p>\n
McKinsey writers note that \u201csignificant advancements have emerged, from general agent platforms to specialized agents designed for deep research,\u201d and hint at the game theory behind this phenomenon, which has made yesterday\u2019s science fiction seem like reality.<\/p>\n
2. Artificial Intelligence<\/p>\n
This might seem recursive, to list \u201cartificial intelligence\u201d as one of 13 trends related to, well, artificial intelligence.<\/p>\n
So to paint a little bit of a portrait of this, here are some of the questions McKinsey people ask regarding the overall trajectory of AI now:<\/p>\n
\u201cAs we look to the year ahead, several pivotal questions emerge: Will lower model inference costs and the explosion of small, specialized models continue to reshape access to and benefits from AI? Which enterprise strategies will unlock the most value as organizations race to move from experimentation to full-scale adoption? And, as innovation accelerates, how can leaders ensure that responsible practices \u2014 around ethics, transparency and governance \u2014 keep pace with AI\u2019s rapid integration into business and society?\u201d<\/p>\n
That shows a focus on the AI timeline that is probably useful as part of this survey.<\/p>\n
3. Application-Specific Semiconductors<\/p>\n
Yes, specialized hardware has played a big role. Nvidia looks like the biggest winner, but other companies have made their bones as chip makers, too. That\u2019s not to mention all of the other related businesses that have thrived on semiconductor plays as AI-specific GPUs and other tools emerge.<\/p>\n
\u201cAI workloads have driven much of the recent innovation in application-specific semiconductors,\u201d the authors write. \u201cThese highly customized chips have provided the specialized processing required for AI training and inference at scale and for the immense computational demands of AI algorithms.\u201d<\/p>\n
4. Advanced Connectivity<\/p>\n
Again, this one sounds overly broad \u2014 what are we talking about, Ethernet connections?<\/p>\n
Fundamentally, I guess what McKinsey is talking about here is non-Ethernet connections: examples of cited connectivity include 5G and 6G wireless (including satellite\/LEO support), the use of low-power networks (LPWAN, IoT connectivity) and network innovations like edge computing and non-terrestrial communications.<\/p>\n
And yes, advanced connectivity is part of the whole ball of wax, which is why innovators have their eyes on a future decentralized internet with AI running on it.<\/p>\n
5. Cloud and Edge Computing<\/p>\n
This is sort of an easy one, as it\u2019s clear how today\u2019s AI revolution happened on the back of the cloud computing era. But we also know that one can now put a strong LLM on an edge device, so edge computing is coming back into fashion.<\/p>\n
So on one hand, McKinsey estimates global demand for data center capacity at 19% to 22% increases each year from 2023 to 2030, for an over 300% increase in demand and something like 171 to 219 gigawatts a year of capacity. On the other hand, edge computing will gain, too.<\/p>\n
6. Immersive-Reality Technologies<\/p>\n
AR\/VR, haptics, smart glasses \u2014 people are talking about the use of immersive tools to drive AI advancement. Here\u2019s how the report authors talked about this:<\/p>\n
\u201cBy providing safe, controlled environments, AR and VR allow users to practice skills and experiment without real-world risks. In healthcare, immersive technologies support medical training and patient treatments. The AR\/VR market saw steady but modest growth in 2024, with headset shipments increasing by 10 percent, though forecasts suggest growth may slow in 2025.\u201d<\/p>\n
7. Digital Trust and Cybersecurity<\/p>\n
Of course, cybersecurity is going to be a huge focus, too.<\/p>\n
McKinsey partner Charlie Lewis identifies several core foundations including asset management, vulnerability management and identity management.<\/p>\n
\u201cTrust is no longer a soft issue; it\u2019s a business-critical asset,\u201d adds Roger Roberts, a McKinsey partner in the Bay area. \u201cIn a world of AI-generated content, cross-border data flows, and rising cyber risk, digital trust is the license to operate. The companies that build it in by design will be the ones customers choose\u2014and the ones that earn the backing of societal stakeholders.\u201d<\/p>\n
I also noted how the team enumerated key elements of what might build future cybersecurity architecture:<\/p>\n
Privacy engineering
\n Technology resilience
\n Smart contracts
\n Explainable AI
\n Automation for governance risk compliance<\/p>\n
That helps to build a bit of theory for cybersecurity in future years.<\/p>\n
8. Quantum Technologies<\/p>\n
Quantum is another ground-breaking field, this one based on those classic ideas of quantum mechanics that seem, to many of us, so quaint and romantic in their theory but coldly mechanical when applied to computer science.<\/p>\n
McKinsey\u2019s writers note that 2025 has been \u201can eventful year for quantum computing,\u201d citing contributions from household names like Amazon Web Services, Google, IBM and Microsoft and projects like Google\u2019s Willow chip and Microsoft\u2019s Majorana 1 processor, as well as IBM\u2019s Quantum Heron and Ocelot, an AWS project.<\/p>\n
For goals, they identify error correction and scalability, and the authors suggest more of a focus on quantum in the years to come. They also cite international buy-in, estimating that 34 countries now have national quantum programs, and noting that the United Nations has declared 2025 the \u201cInternational Year of Quantum Science and Technology.\u201d<\/p>\n
9. Future of Robotics<\/p>\n
This is a big one: just a few weeks ago, I was writing about new Nvidia Jetson technology for robots.<\/p>\n
McKinsey\u2019s people are clued in here, too.<\/p>\n
In their survey of robotics, they include both humanoid designs, which have captured the human psyche, and other types of industrial or \u201ccivilian\u201d robots that are shaped differently. They enumerate current challenges of humanoid designs this way:<\/p>\n
\u201cUntethered humanoid robots are constrained by power,\u201d they note. \u201cA state-of-the-art humanoid robot can operate for about four hours before needing to be recharged, which takes roughly two hours. Humanoid robots also fall down\u2014the balance needed to stay atop two feet is harder to achieve than one might expect.\u201d<\/p>\n
Other issues include hand versatility and training data, where the authors mention the use of foundation models.<\/p>\n
10. Future of Mobility<\/p>\n
Here, McKinsey ponders the robust use of mobile technologies, including items like autonomous vehicles, electric vehicles, drones, urban air mobility solutions and other vehicles likely to be \u201cgrab and go\u201d human-controlled systems for a while longer, although why shouldn\u2019t e-scooters and e-bikes ultimately be autonomous?<\/p>\n
\u201cThe future of mobility in 2025 is rapidly evolving, driven by technological advancements and emerging demands for sustainability across autonomous vehicles (AVs), electric vehicles (EVs), drones, air travel, and micromobility,\u201d the team explains. \u201cThe integration of AI, advanced sensors, and connectivity is improving vehicle safety and efficiency, while shared-mobility models are reshaping urban transportation.\u201d<\/p>\n
11. Future of Bioengineering<\/p>\n
It wouldn\u2019t be a commentary on bioscience without a nod to Alphafold: in mentioning the advances in this field, the McKinsey writers craft a call-out to the winners of this prestigious credential, noting that \u201cthe 2024 Nobel Prize in Chemistry was shared by three researchers who have used AI to predict the structure of existing proteins and design new proteins.\u201d<\/p>\n
ChatGPT will tell you that it wasn\u2019t just for Alphafold, citing the inclusion of David Baker, but for shorthand, we know that Alphafold is the household name behind the idea that vaulted into the global consciousness as people realized what AI can do in bioscience.<\/p>\n
\u201cThe rapid evolution of these technologies, however, brings new ethical, regulatory, and societal challenges,\u201d write the authors of the McKinsey report. \u201cSuccessful adoption of bioengineering innovations depends on ensuring public acceptance and creating robust frameworks for responsible development and application. While the science underpinning many of these advancements is proved, achieving commercial viability and addressing societal concerns remain crucial for realizing the full potential of bioengineering technologies.\u201d<\/p>\n
12. Future of Space Technologies<\/p>\n
Remote sensing, earth observation, digital biosphere twinning \u2013 space technologies are key here, too.<\/p>\n
\u201cSpace is really accelerating,\u201d says Sir Martin Sweeting, professor of space engineering at the University of Surrey in a Royal Society report on the topic<\/a>. \u201cThe number of objects going into space has gone astronomical.\u201d<\/p>\nMcKinsey brings a level of detail to this, in the report, which states:<\/p>\n
\u201cLooking forward, the industry could face questions surrounding ownership and access rights to space, establish governance structures for safe operations, and coordinate efforts to manage space debris and traffic effectively. Additionally, the industry will likely have to navigate growing cyber risks and define the future landscape of satellite distribution across different orbits.\u201d<\/p>\n
We\u2019ll see how the risk\/reward math breaks down as the years go on.<\/p>\n
13. Future of Energy and Sustainability Technologies<\/p>\n
This is no small potatoes within the purview of any AI survey.<\/p>\n
I think McKinsey is right to bring up items like geopolitical tensions and macroeconomic uncertainties in the context of exploring how we\u2019ll address sustainability with AI.<\/p>\n
\u201cKey uncertainties are also shaping the trajectory of the energy transition,\u201d they write. \u201cBeyond the \u2018adoption problem,\u2019 defined as the complex set of barriers to scaling and commercializing new climate technologies, fundamental innovation challenges pose obstacles to developing breakthrough technologies that are cost-effective, reliable, and scalable.\u201d<\/p>\n
They also address secondary problems that build on the above challenges.<\/p>\n
\u201cThe need to rapidly build out critical infrastructure amid supply chain bottlenecks, labor shortages, and regulatory delays compounds these challenges. The availability and sustainable sourcing of key materials such as lithium, rare earth elements, and other critical minerals also pose potential obstacles to achieving global net-zero commitments.\u201d<\/p>\n
That\u2019s just some of what is in this long document. There are pages of information on each of these 13 trends. But I wanted to go through and survey this a little bit, because of how relevant it seems to our current goals. Stay tuned for more.<\/p>\n","protected":false},"excerpt":{"rendered":"Business growth concept art collage. Halftone hands of colleagues holding rising arrow chart. Teamwork, work efficiency, partnership concept.…\n","protected":false},"author":2,"featured_media":143218,"comment_status":"","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[20],"tags":[62,276,277,49,48,41258,61],"class_list":["post-143217","post","type-post","status-publish","format-standard","has-post-thumbnail","category-artificial-intelligence","tag-ai","tag-artificial-intelligence","tag-artificialintelligence","tag-ca","tag-canada","tag-enterprise-tech","tag-technology"],"_links":{"self":[{"href":"https:\/\/www.newsbeep.com\/ca\/wp-json\/wp\/v2\/posts\/143217","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.newsbeep.com\/ca\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.newsbeep.com\/ca\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.newsbeep.com\/ca\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.newsbeep.com\/ca\/wp-json\/wp\/v2\/comments?post=143217"}],"version-history":[{"count":0,"href":"https:\/\/www.newsbeep.com\/ca\/wp-json\/wp\/v2\/posts\/143217\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.newsbeep.com\/ca\/wp-json\/wp\/v2\/media\/143218"}],"wp:attachment":[{"href":"https:\/\/www.newsbeep.com\/ca\/wp-json\/wp\/v2\/media?parent=143217"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.newsbeep.com\/ca\/wp-json\/wp\/v2\/categories?post=143217"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.newsbeep.com\/ca\/wp-json\/wp\/v2\/tags?post=143217"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
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