Method guide

Nuvepro Task Intelligence Index

NTI weights a task-classification mix on a 1–5 scale. Learn the formula and its limits through illustrative examples, then assess the actual work in your enterprise.

Static worked examples. These are not audited industry benchmarks or a quarterly adoption series.

3.1

Mean example score

unweighted across eight example mixes

5M

Posting-task records

published corpus; reviewed 18 June 2026

8

Example mixes

not a sample of deployed enterprises

Q2 2026

Edition context

static worked examples

Illustrative task mixes and computed scores

The examples are ordered by their calculated score. The order does not measure industry readiness.

3.4

Technology

35% auto50% augment15% human

In this example, more tasks are assigned to automation candidates. Code generation and testing still need checks against requirements; architecture and release decisions need named owners.

Example automation candidates: Data entry, code documentation, test generation
Example human-assigned work: System architecture, client strategy, team leadership
3.3

Financial Services

31% auto52% augment17% human

In this example, AI supports much of the work. Transaction records and proposed decisions still need checks against the institution’s rules and approval authority.

Example automation candidates: Transaction processing, report generation, compliance checks
Example human-assigned work: Client advisory, risk judgment, regulatory interpretation
3.2

Consulting

26% auto56% augment18% human

The example assigns a large share to AI support. A consultant must still check source evidence, interpret findings and agree recommendations with the client.

Example automation candidates: Research compilation, slide formatting, data extraction
Example human-assigned work: Client facilitation, strategic framing, executive presentation
3.1

Retail & E-Commerce

28% auto49% augment23% human

The example combines digital operations with work involving inventory, stores and customers. Check the actual systems and operating responsibilities before changing the split.

Example automation candidates: Inventory tracking, pricing updates, customer ticket triage
Example human-assigned work: Merchandising strategy, vendor negotiation, in-store experience
3.1

Insurance

27% auto53% augment20% human

Document preparation and review may use AI, while authorized insurance owners retain coverage, underwriting and claims decisions. Test the specific workflow and evidence.

Example automation candidates: Claims intake, document processing, fraud pattern detection
Example human-assigned work: Complex claims adjudication, policyholder relationships, underwriting judgment
3.0

Healthcare

22% auto55% augment23% human

Separate administrative support from clinical decisions and physical care. The example is not evidence that a clinical or administrative task is safe to automate.

Example automation candidates: Appointment scheduling, medical coding, lab result routing
Example human-assigned work: Patient diagnosis, surgical procedures, bedside care
2.8

Energy & Utilities

21% auto50% augment29% human

Field access, operating conditions and safety authority shape the work. Evaluate proposed monitoring or planning support against those requirements.

Example automation candidates: Meter reading, grid monitoring, consumption forecasting
Example human-assigned work: Field repair, safety inspection, emergency response
2.7

Manufacturing

19% auto48% augment33% human

The example includes plant operations and hands-on work. A lower weighted score says nothing about business importance or whether a team is prepared for its responsibilities.

Example automation candidates: Quality data logging, inventory tracking, production scheduling
Example human-assigned work: Equipment repair, safety supervision, process engineering

How the NTI Is Calculated

A transparent, reproducible formula.

NTI Formula

NTI = (Automate% × 5.0 + Augment% × 3.0 + Human-Only% × 1.0) / 100

Automate weight

5.0

Tasks classified as automation candidates

Augment weight

3.0

Tasks classified for AI support

Human-only weight

1.0

Tasks assigned to people in the design

Worked example

Illustrative mix: (35 × 5 + 50 × 3 + 15 × 1) / 100 = 3.4

Data source

The published corpus review dated 18 June 2026 distinguishes posting-task records, occupational references and workflow records. They are separate evidence sources. The examples here demonstrate the formula; their percentages should not be presented as results from the complete corpus.

What the weights mean

The weights describe the proposed division of work. They do not measure whether an agent is correct, whether people can handle exceptions or whether the workflow is accepted for operation. Keep those assessments separate and inspect the underlying task decisions.

Tier comparisons change the modelling assumptions. Enterprise context can affect a design, but does not establish a fixed score increase or readiness. Review the assumptions in the capability-tier comparison in Explore before comparing results.

How to Use the NTI

Three audiences, three uses.

COO / CEO

Examine the work. Use a task map to identify where a proposed design differs from today’s process. A score difference does not reveal what a competitor has deployed.

CFO

Assess operating impact separately. Collect actual task volumes, review effort, correction rates and costs against a comparable baseline. A change in weighted score cannot be converted directly into money.

Analysts & Investors

Compare like with like. Before comparing scores over time, check that the task population, rules and evidence are comparable. A classification change is not proof of adoption.

Frequently Asked Questions

NTI summarizes a chosen task mix using weights of 5 for automation candidates, 3 for AI-supported work and 1 for human-assigned work. A higher score means that mix assigns more work to software or AI. It does not measure correctness, enterprise fidelity, human capability or deployment readiness.
Use (automate percentage × 5 + augment percentage × 3 + human percentage × 1) / 100. The percentages must sum to 100 and use the same task population. The weights are a modelling convention, not measured business value.
This page uses static illustrative task mixes to explain the formula. It does not publish the underlying reviewed records needed to establish representative industry benchmarks. The separately documented posting corpus was approximately 5M task records at the 18 June 2026 review; that coverage does not validate these example percentages.
This page is a static explanation of the method. No recurring quarterly refresh or comparable historical series is established here. For an enterprise project, version the task list, model assumptions and review date, and revisit them when the work changes.
The technology example scores 3.4 because its input mix is 35% automation candidates, 50% AI-supported tasks and 15% human-assigned work. That is arithmetic from the chosen assumptions, not a ranking of observed technology-industry readiness.
The manufacturing example rounds to 2.7 from its 19/48/33 input mix. The score describes that example, not every manufacturer. A safety-critical maintenance workflow needs its own evidence and authority checks regardless of the score.
Yes. Define the department’s task population and assess each task against the workflow’s systems, data and rules. Calculate the weighted score only after checking that the categories use the same population. Keep the task-level reasoning; the single number cannot replace it.
Capability tiers describe different assumptions about model access, enterprise context and tools. Comparing their scores can explore a proposed design, but it does not prove deployed capability or a particular gain. Read the scope of each view before comparing it with this page’s illustrative mixes.

Examine your enterprise’s task mix

Explore public role and workflow patterns in the data. Discuss the actual responsibilities, approved tools and preparation scope with Nuvepro and your workflow owners.