Gartner has announced its strategic predictions for 2026 and beyond, sending a clear message to CIOs and executives: Artificial Intelligence is no longer just a technological add-on; it’s a behavioral shift that will reshape how we work, purchase, and manage risk. Their ten forecasts are grouped into three themes — talent in the AI era, sovereignty, and insidious AI — and they anticipate a decade characterized by AI agents mediating transactions, fragmented regulatory frameworks, and a “return to the human” in skills assessment.
Below are the key insights and why they matter for organizations.
1) Productivity shake-up: $58 billion at stake (by 2027)
Gartner predicts that the combination of GenAI and AI agents will challenge— for the first time in 30 years— the dominance of traditional productivity tools, leading to a reconfiguration of a $58 billion market. Legacy compatibilities and “traditional” formats will lose ground to the speed to complete work, lowering entry barriers and attracting new competitors. Additionally, functions currently paid for will migrate to free layers, expanding the user base.
Implication: CIOs will need to diversify their toolsets and measure success by time to results, not just checklist of features. License and data governance will become more dynamic.
2) HR changes script: 75% of hiring processes include AI certifications by 2027
In two years, three out of four hiring processes will incorporate AI competency tests. ACEGenAI literacy will correlate with salary levels, and companies will use standardized frameworks to measure candidates’ ability to capture information, synthesize it, and make decisions.
Implication: A certification market and practical tests will emerge. Internal training should evolve from “user courses” to applied mastery (task automation, assisted writing, analysis).
3) The “think without AI” comeback: 50% of organizations will conduct “AI-free” tests in 2026
Intensive use of GenAI could atrophy critical thinking skills, and 50% of organizations will require “AI-free” evaluations to verify reasoning, judgment, and problem-solving without machine assistance— especially in finance, healthcare, and legal.
Implication: Longer and more costly selection processes for critical roles. AI-free testing platforms will emerge to isolate human capability. Talent’s unique value will shift from mere skills to quality of judgment.
4) Sovereignty and fragmentation: 35% of countries anchored to regional AI platforms by 2027
The combination of regulation, languages, and cultural contexts will drive region-specific AI platforms, fed with proprietary contextual data. Multinational companies will have to manage multiple alliances and data governance structures.
Implication: An architecture of multicloud/multimodels becomes essential, with localization clauses and sovereignty. Portability and interoperability will shift from “desirables” to mandatory requirements.
5) Customer service: companies adopting multi-agent AI in 80% of processes will dominate by 2028
The hybrid model—AI for repetitive tasks and people (assisted by AI) for complex or emotional interactions—will become standard. Customers will choose between agent-driven self-service or human interaction supported by AI.
Implication: Companies that industrialize multi-agent AI in CRM and operations will gain competitive advantage. Success metrics will extend beyond NPS to include perceived effort and first-contact resolution.
6) B2B purchasing: 90% of spend to be mediated by agents (over $15 trillion) by 2028
B2B procurement will be mediated by AI agents in nine out of ten cases, channeling more than $15 trillion in agent-to-agent exchanges. Data verifiability will become a currency, and composable, API-first, cloud-native products will create competitive gaps.
Implication: Shorter sales cycles and marketplaces for agents will arise. Businesses will need to publish verifiable signals (performance, SLAs, ESG) to be “machine buyable”.
7) Legal risk: over 2,000 lawsuits for “death by AI” by late 2026
Claims related to AI safety failures will rise, leading to increased inspections, recalls, police interventions, and litigation. Companies will attempt to differenciate themselves by using AI with safeguards and, in some cases, limiting its use.
Implication: Priority will be on functional safety, traceability, and testing; third-party audits and algorithmic decision documentation will increase, with geographic variability.
8) Programmable money: 20% of transactions will include embedded terms of use by 2030
Programmable money will enable machine-to-machine negotiations, automated commerce, and data monetization. Growth in stablecoins, tokenized deposits, and tokenized real-world assets will facilitate enterprise transactions. AI agents with economic agency will be able to pay, collect, and condition transactions.
Implication: Opportunities will emerge in supply chains and financial services; challenges related to interoperability, fragmented standards, and security in custody and access controls.
9) Process-centric contracts: cost-value gap will halve by 2027
Agent-driven AI will uncover tacit knowledge and turn it into assets, replacing standardized flows with contextual orchestration. Prices will trend towards continuous innovation rather than hourly or milestone-based.
Implication: Measuring delivered value will surpass effort counting. Changes in pricing, SLA, and KPI will focus on outcomes.
10) Fragmented regulation: 50% of economies covered, with $5 billion in compliance costs by 2027
As hundreds of AI laws are in progress, each country or region is taking its own path. AI governance will become a permanent program, utilizing specialized software and dedicated teams.
Implication: The dynamic regulatory landscape will be managed by the CIO alongside Risk and Legal. AI literacy will be as critical as technology to turn governance into an enabler instead of a burden.
What to do now: a 5-line action plan for CIOs and leaders
- Redesign the productivity portfolio: comparative pilots between suites with native GenAI and new “agent-ready” tools; negotiate license models with free tier/enterprise options.
- Develop a dual talent strategy: AI competency certifications and “AI-free” tests for critical roles; internal retraining paths with productivity metrics.
- Sovereign, multi-model architecture: localization, data residency, and portability by geography; catalogs of models and agents with shared governance.
- Safeguards and accountability: functional safety, auditing, explainability, and fail-FS simulations; responsibility mapping with vendors and insurers.
- Prepare for buyer agents: expose verifiable data (catalogs, prices, SLAs, inventories) via APIs and signed schemas; craft policies for programmable money and autonomous contracts.
Frequently Asked Questions
How do AI agents differ from “classic” assistants?
Agents don’t just generate text or responses; they act on tools and make decisions with objectives, memory, and validation. In B2B, they will be intermediaries that negotiate and close purchases.
Why does Gartner discuss “AI-free” testing if AI boosts productivity?
Because competitive advantage will rely on judgment and discernment in ambiguous situations. Separating human capacity from AI assistance helps in hiring profiles with cognitive autonomy.
What does AI sovereignty mean for a multinational?
Managing multiple platforms and sovereign clouds, with data governance and compliance by country/region. It requires portability, interoperability, and contracts that prevent lock-in.
How will programmable money affect my business?
It will enable transactions with embedded conditions and system-to-system automation, reducing friction and costs. It’s essential to revisit operational risks, custody, access controls, and regulations.
Sources
Gartner, “Top Strategic Predictions for 2026 and Beyond” (Gartner IT Symposium/Xpo 2025).