The State of Task Intelligence 2026
The first annual benchmark on how AI is reshaping work at the task level. Not tool adoption surveys. Not role-level projections. A ground-up analysis of what AI can actually do with each task across every major industry.
Based on 1.8 million classified tasks, 894 occupations, 3,126 business workflows, and 81 industries. People Path and Process Path combined.
1.8M
Tasks classified
automate / augment / human-only
722
Occupations scored
across 8 industries
3,126
Workflows mapped
People Path + Process Path
3.0
Average NTI
Tier 1, Q2 2026 baseline
Task Intelligence: Two Paths
AI readiness is a people problem and a process problem. Task Intelligence covers both.
People Path
Maps every role in the organization into its component tasks. Classifies each task as automate, augment, or human-only. Produces role-level readiness scores, workforce redesign plans, and training prescriptions.
Process Path
Maps every business workflow into its step-by-step tasks. Classifies each step as automate, augment, or human-only. Produces workflow redesign plans, automation investment priorities, and integration maps.
Most AI readiness tools, skills taxonomies, and L&D platforms cover only the People Path. The Process Path is what determines operational ROI. Both are required for a complete picture of where AI creates value in an organization.
Five Key Findings
What the data shows about the state of AI readiness in 2026.
25% of all tasks can be automated today
Across 1.8 million classified tasks spanning 894 occupations and 81 industries, 25% can be fully automated with current AI, 50% should be augmented with human-AI collaboration, and 25% must remain human-only. This global split holds remarkably consistent across industries, with manufacturing the outlier at 33% human-only.
Task Intelligence covers two paths, not one
Most AI readiness tools measure the People Path: roles, skills, and training gaps. Task Intelligence covers both paths. The People Path maps roles into tasks and classifies each for AI. The Process Path maps business workflows into steps and classifies each for AI. Organizations that only audit roles miss the operational half of the picture.
Enterprise AI more than doubles automation potential
Today's AI (Tier 1) produces an average NTI of 2.2 across all industries. Enterprise AI with company-specific data, integrated systems, and trained agents (Tier 3) raises the average to 3.7, a 68% increase. The gap between what generic AI can do and what enterprise AI can do is the actual transformation opportunity.
Enterprise SaaS covers 89+ workflows and classifies none for AI
Atlassian and HubSpot alone account for 89 named business workflows spanning 500+ discrete task steps. Neither platform classifies a single task as automate, augment, or human-only. The same pattern holds across Epic, Veeva, Guidewire, Procore, and Toast. Platforms own the workflows. Nobody owns the classification layer.
95% of GenAI projects fail to show measurable ROI
MIT research finds 95% of enterprise GenAI projects cannot demonstrate measurable ROI. The root cause is not the AI. It is the absence of task-level analysis before deployment. Organizations deploying AI at the tool level, without classifying which tasks should actually be automated, are funding experiments rather than transformations.
The Global Task Split
1.8 million tasks classified. This is what the data says about all work.
25%
Automate
Tasks AI handles end-to-end with no human in the loop. Data entry, report generation, transaction processing, code documentation.
50%
Augment
Tasks where human judgment and AI capability combine. Analysis, drafting, decision support, quality control, stakeholder communication.
25%
Human-Only
Tasks requiring full human presence. Clinical care, safety supervision, physical operations, novel judgment, relationship management.
These are Tier 1 (Today's AI) classifications. Enterprise AI with company-specific data and integrated systems (Tier 3) shifts approximately 15% of human-only tasks into augment, and 10% of augment tasks into automate. See the /explore tier toggle to compare Tier 1 vs Tier 3 by industry.
NTI by Industry: Q2 2026
Task Intelligence Index scores. Scale 1.0 (human-only) to 5.0 (fully automatable). Tier 1 baseline.
Technology
Highest automation potential. AI-generated code and automated testing anchor the score.
112 occupations scored
Financial Services
High augmentation. Structured data enables AI; regulatory judgment anchors humans.
98 occupations scored
Retail & E-Commerce
E-commerce drives automation. Physical retail tasks hold the human floor.
76 occupations scored
Consulting
Highest augmentation rate. Every task benefits from AI; value comes from human judgment on AI output.
64 occupations scored
Insurance
Document-heavy workflows drive automation. Complex claims keep the human floor elevated.
58 occupations scored
Healthcare
Most occupations of any industry. Administrative tasks automate; clinical care stays human.
134 occupations scored
Energy & Utilities
Safety-critical field work limits automation. Remote monitoring is the primary AI opportunity.
72 occupations scored
Manufacturing
Lowest NTI. Physical tasks dominate. Augmentation, not automation, is the near-term opportunity.
108 occupations scored
The Enterprise AI Gap
Today's AI vs enterprise AI. The difference is the transformation opportunity.
Tier 1: Today's AI
2.2
Average NTI when using generic AI models without company-specific data, policy context, or system integration. This is the baseline for most organizations today.
Tier 3: Enterprise AI
3.7
Average NTI when AI agents have full company context: internal data, integrated systems (ERP, CRM, HRIS), trained on company workflows, and scoped to organizational policies.
The 1.5-point gap is the transformation opportunity
The difference between Tier 1 and Tier 3 (2.2 to 3.7) represents the additional automation potential that becomes available when AI has organizational context. A company at Tier 1 is using AI as a tool. A company at Tier 3 has built AI into its operations. The path between them starts with task classification. You cannot build Tier 3 AI without knowing which tasks it is being built to handle.
The Platform Gap
Enterprise SaaS owns the workflows. Nobody classifies the tasks inside them.
Nuvepro mapped 3,126 workflows across 1,500+ enterprise platforms and AI vendors. The same pattern appears everywhere: platforms define the process steps, add AI copilots or agents as features, but never classify individual tasks as automate, augment, or human-only. The classification layer is missing in every major platform category.
| Platform | Workflows | Task Steps | Classifies for AI? |
|---|---|---|---|
| Atlassian (Jira, JSM, Confluence, Rovo) | 45 | ~250 | No |
| HubSpot (Marketing, Sales, Service, Ops, Commerce) | 44 | ~260 | No |
| Epic Systems (Revenue Cycle, Clinical, Administrative) | 12 | ~80 | No |
| Salesforce / ServiceNow / Workday | 30 | ~180 | No |
| Guidewire / Duck Creek (Insurance) | 10 | ~60 | No |
| Toast / Shopify (Hospitality, Retail) | 14 | ~90 | No |
ERP / CRM / HCM
SAP, Workday, Salesforce, Oracle
Automate repetitive tasks via workflow rules. No classification layer.
Process Mining
Celonis, UiPath, ServiceNow
Map what processes exist. Do not classify which tasks AI should handle.
Vertical SaaS
Epic, Guidewire, Toast, Veeva
Add AI agents to existing workflows. Classification is left to the customer.
Market Context
What the broader research says about the state of enterprise AI.
95%
of enterprise GenAI projects fail to show measurable ROI
MIT Research
21%
of enterprises meet full AI readiness criteria
Morgan Stanley
74%
of companies cannot keep up with AI skills demand
Josh Bersin
5%
of employees use AI in transformative ways
SHL Research
$5.5T
projected cost of the global skills gap by 2026
IDC
56%
wage premium for workers with demonstrated AI skills
PwC
The pattern across all market research is consistent: enterprise organizations are spending on AI at scale, but most are not seeing transformation-level results. The disconnect is not the quality of the AI models. It is the absence of task-level clarity before deployment. Organizations that start with task classification before tool deployment are the ones closing the 95% failure gap.
Methodology
How Nuvepro classifies tasks and calculates NTI scores.
Data sources (8 parallel)
- O*NET: 18,484 government-classified tasks across 894 occupations
- Real job postings: 535,000 tasks from 2,400+ companies (Common Crawl)
- Workflow databases: 235,000 tasks, APQC-aligned
- Canonical task decompositions: 1.6M tasks from 4,372 roles
- AI-generated decomposition, market research, web search, audit history
NTI formula
NTI = (Auto% x 5.0 + Augment% x 3.0 + Human% x 1.0) / 100
Weights reflect degree of AI involvement. Scale: 1.0 (fully human-only) to 5.0 (fully automatable). Published scores use Tier 1 (today's AI). Tier 3 scores are 0.5 to 1.5 points higher.
Classification criteria
Automate signals
Repetitive, rule-based, data-heavy, low ambiguity, structured input/output, no novel situations, measurable success criteria.
Augment signals
Requires context, some judgment, stakeholder interaction, variable input quality, benefits from AI drafting with human review.
Human-only signals
High ambiguity, interpersonal judgment, novel situations, physical presence, accountability and liability, ethical reasoning.