Artificial Intelligence is moving from the testing phase to everyday business use across all industries. Companies are not just experimenting with AI but building it into their core operations. Gartner's research shows that 2026 will be when AI becomes standard business practice, moving beyond optional pilot programs. Companies that start preparing now will capture the biggest opportunities. Here are the seven AI trends that will matter most in 2026.
The AI window is closing fast. Most organizations will struggle with AI costs and security if they go it alone. Winners don't just deploy technology; they choose partners who've already navigated the financial pitfalls and operational chaos. Choose your AI partner based on their experience with the messy realities, not just their technical capabilities.
Top 7 AI trends for 2026
1. Infrastructure spending shifts to inference
Companies are rebuilding their data centers around AI inference (when trained AI models make predictions and decisions for real users), rather than training, reflecting one of the latest AI trends. This shift from just training new models reflects how AI moves into everyday business operations. The numbers make this clear: Gartner projects AI inference server spending will grow 42% annually through 2028, while training server growth remains 24%. Training happens once or periodically when building models. Inference happens continuously when those models serve users, process transactions, or make decisions. The volume difference is massive—a trained model might run millions of inference operations daily.
The diagram above illustrates that the Machine Learning pipeline flows from initial data preparation through training to model deployment. However, the real business value occurs in the final "inferencing and servicing" stage. This is where deployed models continuously process live enterprise data to generate predictions, recommendations, and automated decisions that drive business operations. While the earlier stages of the pipeline, such as data categorization, training, and model creation, represent one-time or periodic investments, the inference phase runs 24/7, processing millions of requests and requiring robust, scalable infrastructure.
The infrastructure requirements are different, too. Inference needs low latency and consistent availability. Training can be batched and delayed. This drives demand for specialized inference accelerators rather than the massive parallel processing systems used for training.
Power consumption creates immediate constraints. AI inference workloads consume 30-100 kilowatts per rack compared to 7-10 kilowatts for traditional servers. Most data centers weren't built for this load. Organizations must upgrade power and cooling systems or limit their AI deployments.
Companies addressing power constraints now avoid these bottlenecks. By 2028, Gartner estimates that over 80% of AI infrastructure spending will support inference workloads. Organizations that plan for inference-focused architecture today will deploy AI faster and at lower cost than those retrofitting later.
2. FinOps practices evolve to handle AI complexity
AI project budgets consistently miss their targets, representing one of the most concerning AI industry trends affecting organizations today. Gartner research reveals that generative AI initiatives can experience budget and cost estimate overruns of up to 1000%. This isn't an outlier; it's becoming the norm for organizations attempting AI implementations without proper cost controls.
The cost variations stem from AI's multifaceted nature. Projects involve infrastructure and cloud resources, model hosting and usage fees, data workloads, and application development. The dominant method of using GenAI models is through cloud providers. These services use pricing based on parameters that are difficult to estimate, such as input and output tokens. As models are updated and optimized, unit costs change frequently, adding uncertainty to budget planning.
Traditional IT cost management falls short because it wasn't designed for consumption-based AI services. Most organizations lack visibility into AI spending patterns or tools to predict costs accurately.
The financial impact is forcing change. By 2027, Gartner predicts that 60% of large enterprises will adopt and apply FinOps practices for their AI initiatives. This represents a shift from reactive cost management to proactive financial governance for AI projects.
The 2025 Gartner CIO and Technology Executive Survey found that 57% of respondents attach high importance to helping business areas understand the full life cycle costs of their technology investments. However, the 2023 Gartner Financial Governance and Sustainability Survey revealed that 69% of organizations with financial governance programs aren't using tools to optimize capabilities, and 79% aren't using tools for cost prediction.
Organizations implementing AI-specific FinOps practices early report better budget accuracy and lower overall costs than those using traditional IT financial management approaches.
3. Agentic AI transforms business operations
Organizations are rapidly adopting AI agents that can make decisions and take actions autonomously, making this one of the top AI trends transforming enterprise operations. Gartner predicts that by 2028, 33% of enterprise software will include agentic AI.
Agentic AI refers to goal-driven software entities authorized by organizations to make decisions and act semiautonomously or autonomously on their behalf. Unlike robotic process automation, agentic AI doesn't require explicit inputs or produce predetermined outputs. These entities can receive goal instructions, iterate on tasks, delegate work, and make variable outputs while augmenting human work.
The business case is compelling. By 2030, AI agents will autonomously make 15% of day-to-day supply chain decisions, freeing humans to focus on critical decisions. In customer service, AI agents handle complex workflows that previously required human intervention. Furthermore, AI will hold 67% of B2B procurement by 2030, requiring companies to structure their offerings as machine-readable data instead of relying on traditional marketing narratives.
Agentic AI systems use memory, planning, sensing, tooling, and guardrails to complete tasks and achieve objectives. They can work collaboratively in multi-agent systems to solve complex problems beyond individual agent capabilities, making them particularly valuable for manufacturing, logistics, and financial services.
The technology enables workforce empowerment by integrating into digital workplace environments. Workers can manage complex initiatives and discover organizational insights using natural language interfaces, making sophisticated AI capabilities accessible to non-technical users.
Organizations implementing agentic AI report improved automation in areas like procurement, where 40% of procurement teams are expected to have at least one AI agent by 2028. The technology's ability to iterate, learn, and adapt makes it suitable for dynamic business environments where conditions change frequently.
4. AI evaluation standards are emerging
Organizations need consistent ways to evaluate AI systems across vendors and use cases, reflecting one of the latest trends in AI technology toward standardization and accountability. In 2026, a Machine Intelligence Quotient (MIQ) will become the standard comparison tool for AI solutions. This composite scoring system will combine accuracy, efficiency, explainability, speed, and compliance metrics into a single score, replacing the current mix of narrow benchmarks that vary by vendor and make comparisons difficult.
The demand for standardized AI evaluation has grown as organizations adopt AI technologies across multiple business functions. Current evaluation methods focus primarily on language understanding through benchmarks like GLUE, SQuAD, and RACE, but these don't capture the full range of capabilities needed for business applications. The MIQ framework will be more comprehensive, incorporating metrics such as reasoning ability, ethical compliance, and adaptability alongside traditional performance measures.
Early versions of MIQ-style evaluation are already appearing in regulated industries. Healthcare organizations evaluate AI diagnostic tools based on accuracy and explainability requirements for regulatory compliance. Financial services assess AI models on processing speed plus adherence to regulatory standards. These industry-specific approaches are evolving toward cross-industry standards that enable consistent comparison of AI offerings.
Vendors must optimize AI solutions to perform well on MIQ evaluations to remain competitive. Organizations will prioritize AI solutions with high MIQ scores when making investment decisions, and enterprise clients will use MIQ leaderboard rankings as starting points before running their own evaluations for specific use cases.
The standardization extends beyond vendor selection. Regulators and standardization bodies are expected to adopt MIQ as part of compliance frameworks for AI deployment, making it a key criterion for solution approval. CIOs report that vendors with clear, standardized performance metrics are easier to evaluate and receive approval faster than those using proprietary or inconsistent evaluation methods.
5. AI enables ultra-lean team operations
AI enables smaller teams to achieve results that previously required much larger organizations, representing one of the most transformative AI technology trends reshaping business economics. AI-native companies generate $1.35M in annual revenue per employee, compared to $107K for traditional software companies—a more than 10x difference in productivity. This efficiency gain reflects how AI can automate work activities that traditionally consume 60-70% of employees' time.
The numbers demonstrate a clear shift in business economics. In 2020, reaching $30M in annual recurring revenue meant building a 250-person company. In 2025, AI-native businesses are achieving the same milestone with just three people. These companies use AI for market research, customer support, content creation, and product development, allowing humans to focus on strategy, oversight, and tasks requiring creativity or judgment.
By 2030, some billion-dollar companies will operate with teams of just 3-20 people. Thirty-six out of 84 newly valued billion-dollar unicorns in 2024 are AI-native companies, with the top 30 startups averaging 40x revenue multiple valuations. These organizations showcase extraordinarily efficient growth rates, averaging $27.5M in ARR within four years.
The productivity gains come from hybrid teams that combine human workers with agentic AI systems. Some teams report a 2.4x increase in productivity when using AI-augmented workflows. With over 76% of a startup's operating costs going to headcount, lean AI-native teams can reduce this expense and reallocate resources to revenue-generating investments.
Capital-efficient startups using this model cut their burn rate and achieve strategic milestones more quickly, enabling operations that shorten the path to positive cash flow. This reduces investor risk and increases the likelihood of earlier, higher-valuation exits. AI leaders invest 10% and 50% of their technology spending into AI initiatives and reinvest savings into new opportunities.
Traditional companies with large workforces will need to adopt AI-augmented processes to remain competitive against these nimble, efficient teams that can iterate and scale without adding headcount.
6. AI engineers replace data scientists
The job market for AI professionals is shifting toward production-focused roles, reflecting broader trends in AI adoption and implementation strategies. By 2027, there will be three times more AI engineer positions than data scientist roles as organizations move from building custom Machine Learning models to deploying and optimizing pre-trained AI systems.
This shift reflects how organizations actually use AI technology. The rise of generative AI has moved the focus from development to production validation of AI applications. AI engineers ensure production readiness and maintain continuous feedback loops across experimentation, development, testing, and deployment phases. Meanwhile, the extensive pretraining of generative AI models reduces the need for building custom Machine Learning applications from scratch.
LinkedIn's "2025 Jobs on the Rise" list shows AI engineer as the fastest-growing job title in 15 countries, ranking number one in the US, the UK, and the Netherlands. The Gartner Software Engineering Survey for 2025 found that the AI engineer was the second most in-demand role, with 57% of leaders planning to hire or increase hiring.
The role requires skills different from those of traditional Data Science. Instead of statistical modeling and algorithm development, AI engineers focus on model selection, rigorous evaluation, building prompt libraries and retrieval-augmented generation pipelines, ensuring model observability, and mitigating AI risks. This represents a shift from custom model creation to system integration and optimization.
There will be subspecialties within AI engineering. Data scientists with software engineering skills are well-positioned for specific AI engineer roles like evaluation design, model selection, and fine-tuning. Software engineers can transition into prompt development, application orchestration, and user experience design for AI systems. Data engineers fit naturally into developing complex data pipelines for unstructured data processing.
Given the talent shortage and specialized skills required, many organizations will need to partner with reliable technology providers that can supply experienced AI engineers and development teams. These partnerships become essential for companies that lack the internal resources to build AI capabilities quickly enough to remain competitive.
7. Multimodal AI becomes the standard interface
Artificial Intelligence is moving beyond text-only interactions to process multiple data types simultaneously, representing one of the latest trends of AI that's changing human-computer interaction. Multimodal AI models can understand and generate content across text, images, audio, and video within a single system, representing a significant shift in how humans interact with AI technology. Multimodal model releases increased by 1,150% over two years.
The business applications are immediate and practical. Field engineers can photograph malfunctioning equipment and receive spoken diagnostic instructions. Clinicians can attach X-rays to notes and get structured report drafts. Analysts can combine charts, transcripts, and audio clips in a single query. This eliminates switching between different AI tools for different content types.
Consumer adoption reflects this utility. By 2028, 80% of digital workers will use multimodal interfaces with AI, significantly improving task efficiency and workplace accessibility. Users no longer need to describe visual problems in text when they can simply show them to the AI system.
The infrastructure supporting multimodal AI is scaling rapidly. Large-scale multimodal model releases grew from 2 in 2022 to 25 in 2024. As a result, major technology companies are investing heavily in systems that simultaneously process diverse data types.
Multimodal capabilities reduce friction in human-AI interaction by allowing people to communicate naturally using whatever combination of text, voice, images, or video best conveys their intent. Rather than forcing users to adapt their communication style to AI limitations, multimodal systems adapt to human communication preferences. This makes AI easier to use for more employees without special training, helping companies get better results faster while spending less on implementation.
What this means
These AI trends will reshape how people work and businesses operate over the next two years. Organizations that invest in AI-optimized infrastructure, implement proper cost management, secure their AI systems, and prepare for automated purchasing will perform better than those that don't.
The changes are already happening around us. Infrastructure spending is shifting toward inference workloads. AI project cost overruns are forcing companies to adopt specialized financial management. Security breaches involving AI agents are increasing. Evaluation standards are emerging in regulated industries. Small AI-augmented teams are outperforming larger traditional organizations. Job postings for AI engineers are growing faster than data scientist roles.
Planning decisions made in 2026 will determine competitive positioning through 2030. Companies have roughly two years to build these capabilities before they become standard business requirements. Organizations that recognize this timing and prepare accordingly will benefit from AI adoption. Those who wait will play catch-up while competitors move ahead with established AI capabilities and streamlined operations. The question isn't whether these AI trends will happen; the data shows they're already underway. The question is whether your organization will be ready.
Have a question?
Speak to an expert
