Gartner, Inc., a business and technology insights company, revealed its top strategic predictions for 2026 and beyond. Gartner’s top predictions span three categories: talent in the AI age, sovereignty and insidious AI.
The risks and opportunities of rapid technology change are increasingly affecting human behavior and choices,” said Mr. Daryl Plummer, VP, Distinguished Analyst, Gartner Fellow and Chief of AI Research for the Gartner High Tech Leaders and Providers practice. “To properly prepare for the future, CIOs and executive leaders should prioritize behavioral changes alongside technological changes as first-order priorities.”
Gartner’s top 10 strategic predictions for 2026 and beyond include:
Through 2027, GenAI and AI agent use will create the first true challenge to mainstream productivity tools in 30 years, prompting a $58 billion market shakeup
GenAI changes will allow organizations to prioritize requirements to GenAI innovations that accelerate work completion. Legacy formats and compatibility will decline in importance, reducing barriers to entry and resulting in new competition from a wide array of vendors.
The cost and packaging of everyday GenAI is likely to change over time, with vendors moving fee-based features into a no cost tier, potentially making no cost products suitable for more users.
By 2027, 75% of hiring processes will include certifications and testing for workplace AI proficiency during recruiting
Within the next two years, expect to see many organizations implementing practical AI proficiency assessments in their hiring processes. These standardized frameworks and targeted surveys allow companies to understand candidate proficiency and close gaps in AI skills within their workforce. This trend will be especially pronounced for jobs where information capture, retention, and synthesis are major components.
As generative AI (GenAI) skills become increasingly correlated with salaries, motivated candidates will place a significantly higher premium on acquiring AI skills and need to demonstrate those abilities to solve problems, improve productivity, and make sound decisions.
Through 2026, atrophy of critical-thinking skills, due to GenAI use, will push 50% of the global organizations to require “AI-free” skills assessments
As enterprises expand their use of GenAI, hiring practices will begin to differentiate sharply between candidates who can think independently and those who lean too heavily on machine-generated output. Recruitment will increasingly emphasize the ability to demonstrate problem-solving, evidence evaluation and judgment without AI assistance.
This shift will lengthen hiring processes and intensify competition for talent with proven cognitive capabilities. In high-stakes industries, such as finance, healthcare, and law, the scarcity of such talent will raise acquisition costs and force companies to develop new sourcing and assessment strategies. Specialized testing methods and platforms designed to isolate human reasoning ability are likely to emerge, creating a secondary market for AI-free evaluation tools and services. Enterprises that successfully integrate AI-free evaluation into their broader talent strategies will protect the “human edge” in decision quality and adaptability, providing an advantage that will compound as GenAI reshapes the competitive landscape.
By 2027, 35% of countries will be locked into region-specific AI platforms using proprietary contextual data
The AI landscape will fragment as technical and geopolitical factors force organizations to localize solutions, responding to strict regulations, linguistic diversity, and cultural alignment. Universal AI solutions will fade as regional differences grow.
Multinational companies will face complex challenges deploying uniform AI across global markets and will have to manage multiple platform partnerships, each with unique compliance and data governance demands. Buyers will prioritize regional platforms that offer strong performance and local compliance, while vendors will forge alliances with sovereign cloud providers and open-source models to remain competitive.
Global model vendors must prove their contextual value or risk losing market share, especially in regulated or culturally sensitive sectors.
By 2028, organizations that leverage multiagent AI for 80% of customer-facing business processes will dominate
A hybrid AI model, where customer relationship management (CRM) AI handles routine tasks and humans focus on complex, emotionally charged interactions, will become the industry standard. Moreover, customers will still choose between full self-service assisted by AI interactions such as performing a transaction or learning more about a product while also choosing a human, assisted by AI to help them with things such as resolving a complex situation or billing dispute.
Organizations that fail to adopt multiagent AI for their CRM organizational processes risk losing competitive advantage as customer expectations for low effort, rapid service become the norm. Moreover, customers who find low-effort experiences often stay with the supplier/brand because of the better experience.
By 2028, 90% of B2B buying will be AI agent intermediated, pushing over $15 trillion of B2B
spend through AI agent exchanges
In this new ecosystem, verifiable operational data becomes a currency, fueling a data feed economy where digital trust frameworks and verifiability are prerequisites for participation. Products designed with composable microservices, API-first, cloud-native, headless architectures will establish a significant competitive moat. New commercial models will emerge, featuring high-frequency, frictionless sales powered by AI agents that can radically compress the sales cycle for a large range of business and technology purchases.
By the end of 2026, “death by AI” legal claims will exceed 1,000 due to insufficient AI risk guardrails
Rising wrongful death incidents of AI-related safety failures, or “death by AI,” will lead to increased regulatory scrutiny and control, recalls, involvement of law enforcement agencies, and higher litigation costs.
As regulatory scrutiny intensifies, organizations will face pressure not only to meet minimum legal obligations but also to prioritize safety and transparency in their business systems through the use of AI guardrails. Somewhat paradoxically, companies will likely showcase either their AI use or lack thereof to differentiate themselves from competitors and mitigate the risk of potential litigation.
The impact of AI and decision governance failures will vary by geography due to differences in legal and regulatory systems, exposing organizations to varying risks and liabilities.
By 2030, 22% of monetary transactions will be programmable to include terms and conditions
of use, to give AI agents economic agency
Programmable money is enabling new business models by allowing machine-to-machine negotiations, automated commerce, market discovery and data asset monetization, fundamentally reshaping industries such as supply chain management and financial services. Real-time programmable transactions deliver liquidity and efficiency gains by reducing friction, improving liquidity and lowering operational costs, which supports the rise of autonomous business operations.
The rise of machine customers, such as AI agents with economic agency, will increase demand for programmable financial infrastructure, create new markets, facilitate autonomous financing and enable products that automatically adapt to changing needs. As a result, stablecoins, deposit tokens and tokenized real-world assets are evolving into mainstream financial instruments for enterprise use.
However, fragmented standards and a lack of interoperability across programmable money platforms and blockchain infrastructures will inhibit market growth and prevent AI agents and machine customers from acting as true economic actors. Security vulnerabilities in programmable money storage, access control and transaction integrity will erode trust and prompt new regulatory frameworks to govern their use.
By 2027, the cost-to-value gap for process-centric service contracts will be reduced by at least 50% due to agentic AI reinvention
AI agents will evolve to discover tacit knowledge. and interactions with them will then become the process itself. Hidden knowledge utilized by these agents will lead to new value assets. Continuous innovation-based pricing will not be limited by labor as standardized workflows are replaced by context-driven orchestration.
By 2027, fragmented AI regulation will grow to cover 50% of the world’s economies, driving
$5 billion in compliance investment
AI transformation is being built on AI governance. With more than 1,000 AI laws proposed last year, no two have a consistent definition of AI. AI governance can become an enabler or a barrier. While technology helps, AI literacy unlocks power. To stay safe, technology leaders will be relied on to build a perpetual “laws and regulations” mind map.
AI governance programs, with dedicated headcount and specialized software, will become the norm to manage new and evolving AI risks independent of security. These risks will be driven by both regulatory and business requirements.
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