The technology story of 2026 has moved off the screen and into the physical world. Innovation is accelerating in the power grids and chips that underpin the data center boom; in the intelligent robots that embody AI; in the agentic systems discovering new chemical compounds; and in the launch pads sending thousands of satellites into orbit.
AI needs energy to scale. That’s one reason energy technologies alone drew nearly $200 billion in investment in 2025, among the highest capital influx in any technology domain. And spending on AI infrastructure doubled in a single year. These developments show that the defining questions today are not only about what technology can do. They are also about who can build the hardware and assemble the skilled workforce to deploy AI in the real world. At the same time, huge leaps were made in cybersecurity and software development—illustrating that AI is accelerating the digital frontier, too.
McKinsey’s Technology Trends Outlook 2026 examines 14 technology trends that define 2026, expanding our coverage from last year to include two new fast-emerging domains: agentic software development and AI for scientific discovery and engineering. For easier navigation, we group the trends into three broader categories: AI revolution, compute and connectivity frontiers, and cutting-edge engineering. The lines between these domains are blurring, and much of the innovation is happening in the gaps.
Machines are being given more autonomy
AI has already transformed screen-based workflows—generating answers, drafting documents, and summarizing calls—and is rapidly advancing into an agentic era, in which it will complete many end-to-end digital tasks on its own. Agentic AI is becoming the connective tissue of the enterprise, with agents working alongside humans, changing not just tools but operating models. Physical AI is the next frontier. Making the jump to the real world, AI is adding perception, reasoning, and action across robotics, mobility, and wearables. General-purpose robots are being trained to learn about their environments so they can execute complex tasks and navigate unpredictable environments. Vehicles can make real-time decisions without drivers. Industrial systems can produce complex goods inside “dark factories” with no humans present. And immersive-reality headsets are interacting in real time with both wearers and the outside world. Physical AI is arriving first in manufacturing and logistics, where the economics are clearest. But the trajectory points well beyond the factory floor, toward hospitals, construction sites, farms, and city infrastructure.
Hardware and software are being codesigned for differentiated AI workloads
General-purpose chips have long powered everything from laptops to data centers. AI changed that. Training models and then running them at scale (what’s known as inference) demands something more specialized: chips optimized for specific workloads. Inference is overtaking training as the dominant AI workload. As model architectures continue to evolve, new application-specific chips are being designed to deliver inference on those models more efficiently, providing more output per watt at a lower cost. This is critical, as data centers’ energy demand is increasingly straining power grids. Hyperscalers are investing heavily to build data centers and have much to gain from faster, higher-performance chips. Thus, Amazon, Google, Meta, and Microsoft are increasingly partnering with semiconductor firms to codesign custom silicon tailored to their AI models—and some are exploring ways to offer these chips to outside customers as competitive products. (In the chip industry, the customer is becoming an alternative supplier.) But these new-format chips are not just affecting the semiconductor sector. They are changing how physical AI infrastructure is designed and transforming the business models of the equipment makers and energy suppliers that support these build-outs.
AI is hungry, and the grid is not ready
The race to deploy AI at scale has run headlong into a constraint that hyperscaler ingenuity cannot entirely solve: power. US data centers running AI workloads alone are projected to consume as much electricity by 2030 as California does today.3 Globally, the numbers are even larger. The problem is not just how much power AI needs but how hard it is to deliver. Data centers can be built faster than the transmission lines, substations, and transformers needed to power them can be supplied. Supply chain constraints are often to blame. In many markets, transformers now carry lead times of more than two years. More than 2,500 gigawatts of energy projects are stalled in grid queues worldwide, waiting for connections that may be years away.4 For enterprises, securing reliable compute power is becoming as much a competitive advantage as securing talent or capital.
When measuring talent demand, we see signals that some trends are maturing and selectively scaling. In connectivity, cybersecurity, energy, life sciences, and mobility, more than half of job listings were for non-R&D roles such as general and administrative, operations, and sales and marketing. While these trends are still driven by innovation, deployment is underway. Companies are now applying these technologies in use cases with commercial viability. Meanwhile, in all four AI-related trends plus application-specific semiconductors, over 75 percent of jobs posted were in the R&D category, illustrating just how early these sectors are, despite rapid growth in the past few years.
Introduction to the Future of Business
The business landscape is undergoing a transformation driven by:
- Technological disruption
- Changing workforce dynamics
- Increasing demand for sustainability
- Global digital connectivity
In 2026, businesses will focus heavily on:
- Data-driven decision making
- Automation and efficiency
- Personalized customer experiences
- Agile and resilient business models
Companies that embrace business innovation strategies will lead the next wave of growth.
Rise of Artificial Intelligence in Business
One of the most significant innovations in business 2026 is the rise of artificial intelligence.
How AI is Transforming Business:
1. Automation in Business
AI automates repetitive tasks, improving efficiency and reducing operational costs.
2. Predictive Analytics
Businesses use AI to forecast trends, customer behavior, and market demand.
3. Personalized Customer Experiences
AI enables hyper-personalization in marketing, enhancing customer experience trends.
4. Intelligent Decision-Making
AI supports data-driven decision making, helping leaders make informed strategic choices.
5. AI-Powered Customer Support
Chatbots and virtual assistants provide 24/7 customer service.
The integration of AI is not just an option, it is becoming a necessity for businesses aiming to scale and innovate.
Brand building with AI: human connection through machine selection
Next year, purchase decisions will be increasingly mediated by Generative AI and agents, who will recommend brands and their content. But (for now) technology doesn’t buy things, people do. So, the CMO’s job is to build brands that people love, and to be more present so that models prioritise their brands.
Salience alone won’t make you algorithmically preferred. Three-quarters (74%) of those who use AI assistants (Kantar’s Connecting with the AI consumer report) regularly seek out AI-driven recommendations. Enter Generative Engine Optimisation (GEO), the new SEO. GEO gets your brand cited and trusted by LLMs. If the model doesn’t know you, it won’t choose you, so prepare to create clear, structured, machine-legible and relevant content. Models must be primed with the meaning of your brand and what you offer (e.g. recipes, how-to content).