The strongest forces shaping the near-term technology landscape are wider AI adoption, autonomous agents, AI-native software, AI infrastructure, physical AI, cybersecurity, quantum computing, specialized models, digital trust, and connected services. Agentic AI means foundation-model-based systems that can autonomously plan and execute multistep workflows.
For business leaders, these forces affect planning, investment, and the services people use each day. Their maturity varies: some already support routine work, while others remain in early trials. Practical value depends on the task, the risks, and the ability to support a system over time.
This report weighs real deployment alongside new capabilities. It looks at how technology can change work and everyday life without treating every trend as equally ready. The useful question is not only what is new, but where it can solve a real problem responsibly.
What Is Changing as AI Adoption Moves Into Everyday Work?
AI use has spread quickly, but adoption rates reveal different stories about organizations and individual users. In 2025, 88% of organizations reported using AI, while generative AI reached 53% of people within three years.
Those measures describe different kinds of adoption. Organizational use can include one team or a limited task, while population adoption counts individual users. Generative AI spread faster than personal computers or the internet, but broad access does not mean every user relies on it daily.
In 2026, U.S. generative AI adoption stood at 28.3%, ranking 24th among countries. That ranking measures user adoption, not investment or research capacity. U.S. private AI investment reached $285.9 billion in 2025, compared with $12.4 billion in China.
Talent movement tells another story. The number of AI researchers and developers moving to the United States fell 89% from 2017 to 2026, including an 80% drop in the last year. Investment and talent flows matter, but neither proves that deployments deliver useful results.
Adoption, investment, and access to experienced talent are related but distinct measures of readiness.
Technology trends examples include generative AI for writing, autonomous workflows, AI infrastructure, and cybersecurity tools. Their maturity differs: a writing assistant may support a person today, while an automated workflow needs stronger checks before it acts.
A review of worker and workplace AI found that successful coexistence needs technical, human, and conceptual skills, with ongoing reskilling. The workplace AI coexistence study also describes AI as a way to support worker abilities, alongside concerns about job loss.
Treat adoption, investment, and access to experienced talent as related but distinct measures of readiness.
Can AI Agents Move From Pilots Into Production?
AI agents can move beyond demonstrations when they own a clearly bounded workflow with a human accountable for the outcome. Agentic AI plans and executes several steps, such as gathering information, coordinating a handoff, and preparing an action for approval.
In 2025, 11% of organizations had agents in production, 38% were piloting them, and 42% were developing a strategy. Another 35% had no strategy. The survey covered 500 U.S. technology leaders, so its results describe that group, not every organization.
A pilot tests whether an agent can perform a task. Production use connects the agent to real work and requires ongoing oversight. For example, an agent might collect order details, flag missing information, and prepare a change for a person to approve.
Demand for these skills rose quickly: job postings for agentic AI increased 985% from 2023 to 2024. Yet more than 40% of agentic AI projects are projected to be canceled by the end of 2027. Interest alone cannot make a workflow dependable.
Before deployment, teams need clear limits, an owner, and a way to spot errors. A practical readiness review should check whether the agent:
- Has one defined, bounded task.
- Stops for approval at key points.
- Has been tested against failure cases.
- Has an accountable owner.
- Can be monitored for results over time.
These checks help teams separate a useful tool from an agent that can take unwanted steps. They also make it easier to learn from a limited trial without giving the system broad access.
Start with a reversible task where an incorrect action can be detected and contained.
How Is AI Changing Software and the Way Teams Build?
A developer can increasingly describe an intended result and use AI to help produce the code and supporting components. People still review the work, set rules, and decide whether changes belong in a live system.
This shift changes development from writing each component by hand toward guiding, testing, and maintaining software with AI assistance. It can speed experimentation, but teams still need to understand what the software does and how it handles data.
AI-native development platforms
AI-native development platforms use AI throughout the software process, from turning a request into code to helping maintain a product. They can draft tests, explain code, and suggest fixes. Human review remains important when a change affects sensitive data or business operations.
Specialized platforms and models
Different computing needs call for different tools. AI supercomputing platforms support large workloads, while domain-specific language models focus on a field or task. Application-specific semiconductors target particular computing jobs, and advanced connectivity moves data between systems.
Platform choice increasingly depends on the workload, not on one model alone. Gartner’s 2026 strategic technology trends include AI-native development platforms, AI supercomputing platforms, confidential computing, multiagent systems, domain-specific language models, physical AI, preemptive cybersecurity, digital provenance, AI security platforms, and geopatriation.
McKinsey’s 2025 Technology Trends Outlook covers 13 frontier areas, showing that change reaches well beyond software. Its scope includes agentic AI, application-specific semiconductors, advanced connectivity, cloud and edge computing, digital trust, quantum technologies, robotics, mobility, bioengineering, space, and energy and sustainability technologies.
For teams, this range means software innovation can depend on research in computing, networks, and hardware. A platform that suits a text task may not suit a fast control system or a specialized industry workflow.
Keep human review in the loop wherever generated changes affect sensitive data or critical operations.
Why Are Cloud and Data Centers Becoming Strategic Infrastructure?
AI infrastructure is a strategic decision because compute capacity, data location, and electricity supply now shape what organizations can deploy. Where computing happens affects cost, speed, control, and the kinds of services a team can support.
The United States hosted 5,427 data centers in 2026, more than ten times the number in any other country. Their growth places more weight on site planning, grid access, and the long-term cost of running servers.
Cloud choices for AI workloads
Public cloud offers computing capacity from a provider, which can help when demand changes often. Private cloud gives an organization more control over its environment. Hybrid cloud combines private and public systems, while multicloud uses more than one provider.
Each approach has trade-offs. Data sensitivity can favor tighter control, while changing workloads can favor flexible capacity. Portability matters when teams may move data or software, and edge computing can help when a system must respond near its data source.
Edge computing means processing data near the place where it is generated. A factory sensor may need a quick local response, while a large model may need remote computing resources. Some organizations use both, placing each workload where it fits best.
Electricity and site constraints
Power availability can limit data-center growth even when computing equipment is ready. Data-center servers used an estimated 7% of U.S. commercial-sector electricity in 2025. Long-term plans must account for the supply and cost of power as well as server capacity.
The U.S. Energy Information Administration projected data-center server electricity use of 446 billion to 818 billion kilowatthours by 2050. That is server use, not the full electricity use of every part of a data center.
The same projection put data-center servers at 22%-33% of commercial-building electricity use by 2050. The range shows how much future demand can vary. It also makes site choice and power planning part of technology strategy, not just facilities work.
Cloud and edge decisions also affect how quickly data moves between users, devices, and models. Workloads that need fast responses may benefit from local processing, while large or variable workloads may suit cloud capacity.
Include power availability and workload location in infrastructure planning, not only computing capacity.
Where Is Physical AI and Robotics Proving Useful?
Physical AI is most useful where machines can sense a real environment and respond within well-defined operating limits. It includes AI-enabled systems that act in the physical world, from warehouse robots to autonomous vehicles and smart devices.
Factories and warehouses use robotics for repeated tasks, while equipment monitoring can help flag unusual conditions. Autonomous vehicles provide another example, and connected devices can adjust to changes in a room or work site.
Test results can look different in a simulation than in a home. Robots succeeded in 12% of real household tasks, while robotic manipulation in RLBench software simulations reached 89.4%. A simulated task has controlled rules; a real room has clutter, varied objects, and changing conditions.
Waymo reached approximately 450,000 weekly trips across five U.S. cities in 2025. That figure shows a real autonomous service operating at scale in specific places. It does not show that autonomous vehicles can work everywhere or under every road condition.
Physical deployments need checks that match the place and task. Teams can assess:
- Safety boundaries for people and equipment.
- Human oversight during normal work and exceptions.
- Reliable sensors in expected conditions.
- Integration with legacy systems.
- A clear plan for handling errors.
A robot may work well on a clear factory floor but struggle with a blocked path or a sensor fault. The same device can behave differently as lighting, surface, or traffic changes. Those details shape what a system can do safely.
Test a system in the conditions where it will actually operate, not only in a simulation.
How Should Organizations Manage AI Cybersecurity and Trust?
AI can help detect threats, but it also creates new paths for mistakes and abuse when systems have access to data and tools. Every connection to an account, cloud service, or automated action can change an organization’s risk.
Documented AI incidents rose to 362 in 2025, up from 233 in 2024. AI can support security work, but weak controls around identity and data access can expose systems to misuse.
A 2026 Thales survey included 3,120 security and IT management professionals across 20 countries. Thirty percent reported a significant increase in budgets specifically for AI security. Spending does not by itself fix poor data quality or weak governance.
Respondents named cloud storage as an attack target at 35%, cloud-delivered applications at 34%, and cloud management infrastructure at 32%. These findings show that risk can sit in the services AI systems use, not only in the model itself.
Cloud encryption remains uneven. In 2026, only 7% of organizations said they encrypted more than 80% of sensitive cloud data, while 29% said they encrypted more than 60%. The average share encrypted was 47%, down from 51% in 2025.
Data problems can also block adoption. Poor data quality or initial data governance was cited as an inhibitor by 65% of respondents, and security risks from exposed data by 61%. A system cannot give reliable results if its data is inaccurate or poorly managed.
Practical security checks include:
- Limit access by role.
- Protect sensitive data.
- Log agent actions.
- Require human approval for consequential actions.
Gartner forecasts that 25% of enterprise security breaches will be linked to AI-agent abuse by 2028. That forecast makes identity and permissions important parts of AI governance, especially when software can take actions on its own.
Give every agent an owner who can review its permissions and activity.
What Could Quantum Computing Add to Business Computing?
Quantum computing is a specialized area to explore for problems that may benefit from new computational approaches, not a general replacement for conventional computers. Its promise is specific: selected tasks may gain from a different way to process information.
In 2026, 98% of respondents were considering how AI and quantum computing affect each other. That interest reflects active research and planning, not proof that quantum systems already outperform classical computers in business.
Hybrid quantum-classical computing
Hybrid quantum-classical computing combines conventional systems with quantum processors. A classical computer can handle many parts of a task, while a quantum processor works on a selected part that may suit it.
Error correction remains a major requirement. Quantum processors can make errors, so systems need ways to detect and correct them. Better error control can help researchers test larger or more useful tasks, but it does not guarantee a commercial gain.
Four 2026 directions include hybrid quantum-classical computing, industrial use cases, error correction, and cybersecurity. These areas span technical work and early planning for how quantum tools might affect current systems.
Early industry pilots
Finance, pharmaceuticals, and logistics are early industries for quantum-computing pilots. Finance teams may explore portfolio optimization, drug researchers may test molecular simulation, and logistics firms may study operations optimization.
In a 2026 survey, 57% of respondents believed quantum computing would enhance machine learning, and 54% expected it to enable advanced simulations of complex systems. Those are expectations, not proof of a useful edge in daily business.
A pilot can help a company learn whether a specific workload fits the technology. It should compare results with a classical approach, since conventional computing remains the standard for most business tasks.
Evaluate a pilot against a specific workload and a classical baseline before treating it as a business advantage.
How Strong Are Today’s AI Models, and How Should Benchmarks Be Read?
AI capabilities are advancing, but a benchmark score is evidence about a particular test rather than a guarantee of everyday performance. A score has a scope: it measures how a model did under specific test conditions.
Frontier AI models gained 30 percentage points in one year on Humanity’s Last Exam. That change signals rapid progress on the exam, but it does not mean every model improved equally on every task.
Capability gains and model comparisons
As of March 2026, Arena Elo ratings were 1,503 for Anthropic, 1,495 for xAI, 1,494 for Google, and 1,481 for OpenAI. Alibaba scored 1,449, and DeepSeek scored 1,424.
Those ratings are a dated snapshot, not a permanent ranking. They compare model performance in the Arena setting at that time. Results can change as models are updated or as new systems enter the comparison.
As of March 2026, the top closed AI model led the top open model by 3.3%. In August 2024, the gap was 0.5%. These figures compare the leading models in each group at those dates, not every closed and open model.
What benchmark scores can miss
Benchmark questions can be invalid, which can distort a score. Invalid-question rates ranged from 2% on MMLU Math to 42% on GSM8K. A high rate can make results harder to interpret because some questions may not test the intended skill.
A benchmark also may not match a workplace task. A test can measure a narrow answer, while a real job may require good judgment, current information, or a response that fits company rules.
For example, a strong score on a math test does not show whether a model can explain a cost change in a clear report. Teams need to judge the task the model will handle, not just its place in a ranking.
Check what a benchmark measures and how its questions were validated before using it to compare models.
Which Emerging Models and Systems Are Next?
Several emerging systems focus on improving how AI handles structured quantities, spoken interaction, context, and the world around it. Some remain early, so their names describe areas of work rather than broad, proven use.
Gartner’s 2027 strategic technology trends include large quantitative models, speech reasoning models, world models, AI context platforms, AI coding agent harnesses, AI agent identity, autonomous exposure remediation, SovereignTech, disposable applications, and wetware as a service.
Models that reason about numbers, speech, and worlds
Large quantitative models focus on numbers and structured data, which can support forecasting or operational planning. Speech reasoning models aim to understand spoken input and respond through voice. World models seek to represent how settings and actions may change.
AI context platforms help systems keep relevant information about a user, task, or work setting. Gartner predicts that by 2030, large quantitative models will replace large language models in 25% of strategic forecasting and operational modeling tasks.
Gartner also predicts that by 2030, speech reasoning models will handle more than 70% of enterprise voice interactions autonomously. These are forecasts for a future period, not current adoption rates.
Identity, remediation, and digital control
These trends describe different changes, not one new kind of model. AI coding agent harnesses organize how coding agents work, while AI agent identity gives an agent a distinct identity for access and tracking.
Autonomous exposure remediation aims to find and fix security weaknesses. SovereignTech focuses on control over technology and data. Disposable applications are short-lived tools built for a task, while wetware as a service connects biological systems or processes with technology.
These categories vary in maturity. Some build on current enterprise software, while others remain in research or early trials. A category label alone does not show that a product is ready for broad use.
New tech can attract attention before its practical limits are clear. Organizations can separate current use from experimentation by testing what a system can do, what it needs, and where people remain responsible.
Treat a long-range forecast as a planning signal, then validate its assumptions against your own use case.
Which Technology Trends Are Already Changing Everyday Use?
Technology is already part of everyday decisions, from AI-assisted product research to tools used for learning and connected services. Convenience matters, but a useful service also needs to fit the person and protect the data it uses.
By early 2026, generative AI tools had an estimated annual value of $172 billion to U.S. consumers. In 2026, four in five university students used generative AI, while 22% of shoppers said they used AI search tools such as ChatGPT for product research.
AI search can help compare products, while generative AI can support learning and everyday tasks. Internet of Things devices connect items such as sensors and appliances. Edge computing processes data near where it is made, which can support local monitoring.
Connected systems also rely on advanced connectivity. 5G, 6G, and low-Earth-orbit satellite networks are part of this wider technology landscape, though not every service is widely deployed. Each can shape how devices share data across places and distances.
The best tech for a specific use case is the option that fits the task and the user’s limits. Good tech advice compares measurable usefulness and risk, not novelty alone. Consider these checks before choosing a service or device:
- The problem it solves.
- The data it needs.
- Its reliability for the task.
- How it protects privacy.
- Whether people can access and use it.
Try a new tool on a low-risk task before relying on it for a consequential decision.
These checks apply to a study tool, a smart device, or an AI search service. A useful trial can show whether the technology saves effort without creating new problems for the person using it.
Conclusion
The most useful technology choice is the one that solves a defined problem and can be deployed responsibly. Maturity differs by use case, and trust, infrastructure, and human oversight shape whether new capabilities create value.
Business leaders can begin with one important workflow and define a measurable outcome. Then they can assess the data, security, and operating needs before investing in a new system.
Choose one workflow, set a measurable goal, and review the risks before committing resources.
