Annual Research Report · January 2026

Unmet
AI Needs. The agentic era has arrived.
The gaps haven't closed.

Now in its second year, this global research of AI leaders and practitioners across industries reveals what's still broken as organizations push beyond productivity tools toward a mission-critical agent workforce.

413
Respondents surveyed
5
Critical unmet needs
84%
Have or plan Tier 0/1 agents
96%
Hit unexpected costs
7.3mo
Avg. idea to production

The rules just changed.

In 2024, the challenge was getting generative AI into production with enough confidence to matter. In 2026, organizations are deploying agents into mission-critical, externally-facing workflows, with near-zero tolerance for failure. The pain points haven't disappeared. They've become existential.

Financial Services 29% Insurance 19% Telecommunications 14% Healthcare 8% Retail 8%

Who we surveyed.

Leaders and practitioners with direct responsibility for agentic AI development across highly regulated industries. 62% practitioners, 38% leaders. Companies from 100 to 20,000+ employees. All with real agents in the pipeline, not just opinions about AI's future.

Practitioners 62% Leaders 38% AMER 68% EMEA 26% APAC 6%
Year-Over-Year Shift

From Generative AI experiments
to an agent workforce.

Last year we mapped four unmet needs in the generative AI lifecycle. This year, the industry has shifted to agentic AI, and the same core pain points have returned with higher stakes, harder requirements, and less margin for error.

2024: Generative AI Era
4 unmet needs identified
Teams struggled to move models into production with confidence. Average time from idea to production: 7.5 months. Only ⅓ felt they had the right tools. The pain was real but the stakes were manageable.
CONFIDENCE & OBSERVABILITY GENERATIVE AI APP EXPERIENCE IMPLEMENTATION & INTEGRATION COLLABORATION
EVOLVED TO
2026: Agentic AI Era
5 unmet needs, higher stakes
Agents are entering Tier 0 mission-critical deployments: customer-facing, regulated, and unsupervised. Average time to production: 7.3 months. The pipeline is still slow. The consequences of failure are not.
MISSION-CRITICAL DELIVERY NON-PUBLIC CLOUD COST CONTROL SPRAWL & LOCK-IN MANAGING AGENT FLEETS
7.3mo
Still the timeline
In 2024, generative AI took 7.5 months. In 2026, agentic AI takes 7.3 months. Two years of industry advancement. Two weeks saved. The build to deployment pipeline hasn't been fixed.
75%
Believe in complex agents
Three-quarters of respondents believe agents have the potential to tackle multi-functional, multi-agent workforces and human collaboration, not just simple chatbots.
71%
External users incoming
71% have or plan to have external users (customers and partners) interacting with their agents. Low-risk internal tools are just the beginning.
The 5 Unmet Needs

What's standing between
ambition and reality.

These aren't edge cases or wishlist items. They are the defining blockers for enterprise agentic AI in 2026, validated by 413 practitioners and leaders doing this work right now.

Unmet Need #1
Delivering mission-critical agents
A third (34%) of respondents will have Tier 0 agents (mission-critical, near-zero tolerance for failure) deployed by end of 2026. With 71% planning external users, organizations are moving well beyond low-risk internal assistants. The problem: the infrastructure, tooling, and trust to support this simply isn't in place yet.

Top three blockers: security risks (73%), legacy system integration (56%), and high operational costs (43%).
34%
Will deploy Tier 0 mission-critical agents in 2026
98%
Have specific requirements for accuracy, auditability, cost, or latency
Barriers to delivering mission-critical agents
Concerns about security risks
73%
Integration with legacy systems
56%
High dev or operational costs
43%
Agent-human handoffs
38%
Stakeholders don't trust agents
38%
Regulatory requirements
36%
Unmet Need #2
Support outside of public cloud
This isn't a preference; it's a mandate. 64% must build and deploy agents on-premises. 18% need edge deployments. Geopolitical pressure, data sovereignty laws, and security requirements are actively reversing the long trend toward hyperscaler adoption.

Yet 95% of hyperscaler users have concerns about their provider's agentic AI support, and only 11% are "very satisfied." Mixed and private environments are the new normal.
85%
Definitely or probably require sovereign cloud for agentic AI
78%
Will deploy agents in air-gapped environments
Top hyperscaler concerns (of the 95% who have them)
High costs / surprise bills
62%
Potential vendor lock-in
59%
Lack of data sovereignty
39%
Limited tool / model choice
31%
Inadequate compliance support
28%
Too many services to manage
25%

"Only 11% of hyperscaler users are 'very satisfied' with their provider's agentic AI support. The remaining 89% are working around limitations that compound with every new agent deployed."

2026 Unmet AI Needs, DataRobot × Dimensional Research
Unmet Need #3
Cost control & predictability
The cost problem is worse than most budgets anticipated. 72% of respondents have experienced higher operating costs than expected. And it's not a build problem; it's a run problem: 71% report that ongoing operations cost more than initial development.

28% specifically cite inference costs as a barrier to ROI. A third can't even understand their bills. Running agents in owned environments or neo-clouds is emerging as a cost-control strategy, but it requires platform flexibility most vendors don't offer.
72%
Experienced higher operating costs than expected
57%
Say costs per interaction grow as agents scale, not shrink
Types of unexpected costs encountered
High token costs
50%
Compliance audit costs
43%
Excessive GPU hours
42%
Labor for maintenance & debug
39%
Hard to understand billing
33%
Unexpected service fees
29%
Unmet Need #4
Avoiding sprawl & lock-in
Organizations don't have too few AI tools; they have too many, too fragmented. The average agentic AI stack has 12.6 distinct tools. Nearly a third manage more than 10 software components they must stitch together by hand. 73% say they spend too much time on infrastructure plumbing and not enough building business solutions.

Vendor lock-in compounds the problem: 39% are trapped by long-term contracts, 37% have vendors that block portability. And 92% agree vendor neutrality is critical, yet hyperscaler platforms are built to resist it.
12.6
Average distinct tools in an agentic AI stack
92%
Agree vendor neutrality is critical for agentic AI
Barriers to changing tools in the agentic AI stack
Desired alternative too expensive
47%
Long-term vendor contracts
39%
No tools for desired function
39%
Vendor blocks portability
37%

"73% say they spend too much time on infrastructure plumbing and not enough time creating business solutions. That's not a tooling gap; that's a platform failure."

2026 Unmet AI Needs, DataRobot × Dimensional Research
Unmet Need #5
Managing fleets of agents
Building one agent is the beginning. Today 34% already have more than 10 agents in production. Within two years, 66% expect to cross that threshold. The top 5% are already managing 100+. And 61% say governance will be the biggest challenge as they scale toward agent fleets.

80% expect agents to work in coordinated, multi-agent workforces, raising deep questions about how autonomous decisions get traced, audited, and governed. Current RBAC and catalog-based approaches aren't built for this world.
94%
Experienced "Day 2" operational failures after deployment
66%
Will have 10+ agents in production within two years
Day 2 operational failures experienced post-launch
Hallucination in production
59%
API failures or rot
45%
Cost spirals or loops
43%
Goal drift
40%
Jailbreaking
24%
Challenges anticipated when scaling to 100+ agents
Governance
61%
Handoff orchestration
49%
State management
40%
Inference costs
40%
The DataRobot Response

Built for agents that
actually matter.

The five unmet needs in this report aren't abstract problems; they're the daily reality of teams building enterprise agentic AI today. DataRobot was designed to close every one of them. Not with point solutions. With a platform purpose-built for the full agentic lifecycle needed to run an agent workforce in mission-critical environments.

Build agents that drive your business

Agent development that exposes enterprise context: structured and unstructured data, memory, specialized models and libraries, with every action tested and evaluated for safety and reliability before it touches production.

Run with total confidence

Full governance, monitoring, and mitigation in the enterprise ecosystem. Complete support for mission-critical non-functional requirements: identity, authorization, scaling, sovereign cloud, and air-gapped environments.

Do it on your own terms

Meet enterprise requirements and developer preferences exactly as they are: compute, storage, data, APIs, developer tools, identity, observability. No lock-in. No forced migration. No compromises.

Two Categories of Agents We Enable
Category 1

Reimagine processes agents

Modernize and automate existing processes across departments. Agents that drive efficiencies across the business, replacing brittle, manual workflows with intelligent, adaptive automation that scales across the enterprise.

Category 2

New frontier agents

Invent and deploy previously impossible use cases. Agents that unlock entirely new ways of doing business, creating capabilities and competitive advantages that didn't exist before and can't be easily replicated.

Ready to close the gaps?

See how DataRobot helps enterprises build, run, and govern a mission-critical agent workforce, on their own terms.

Book a Demo →

© 2026 DataRobot Inc. All rights reserved. · Research conducted by Dimensional Research, January 2026.