Conduct (conduct.ai) Review 2026: Enterprise AI and Verdict

Conduct is AI enterprise system modernisation and operations software. Conduct has an ambitious proposition: use AI to help teams understand, modernise and run complex enterprise systems, beginning with SAP. The potential value is substantial where institutional knowledge and legacy customisation slow change, but public product and pricing detail remain limited enough that buyers should treat any engagement as a tightly scoped, evidence-heavy pilot.

This review answers the practical buying questions: what the product actually does, where it may create value, what remains unverified, how pricing works, and what a responsible pilot should measure. We separate observed public evidence from vendor claims and do not assign a numerical rating without repeatable authenticated testing.

Authentic homepage evidence from Conduct. The interface and claims may change after capture.

Conduct at a glance

What Conduct is designed to do

The product is designed around five buyer jobs:

• Map complex enterprise-system behaviour and dependencies
• Explain legacy configuration and customisation
• Support modernisation planning and execution
• Assist ongoing operations and incident investigation
• Preserve institutional knowledge around SAP landscapes

The important distinction is between a capability demonstrated on a website and a dependable operational result. A buyer should translate every claimed feature into a task, a source of truth, an acceptable error rate and a named owner. That makes a pilot comparable with the current process and prevents an attractive demo from becoming the success criterion.

How we evaluated Conduct

This is not a hands-on review. We reviewed the official positioning, publicly described capabilities and available commercial information, then designed a testing framework based on the risks of the category. We did not create a workspace, connect live company data or reproduce performance claims.

Our evaluation asks six questions:

1. Does the product solve a frequent, costly job rather than add another dashboard?
2. Can users inspect the evidence behind outputs and actions?
3. What permissions and sensitive data does it require?
4. How does it behave with missing, conflicting or adversarial inputs?
5. Can actions be approved, reversed, exported and audited?
6. Is the full cost justified by measured time, risk or revenue outcomes?

For teams evaluating AI software, our AI Tool Chooser can turn requirements into a more disciplined shortlist. If usage pricing is material, the AI Token Cost Calculator helps model scenarios before vendor negotiations.

Core features and buyer value

System understanding

The first challenge in enterprise modernisation is discovering what the current system actually does. An AI layer can help organise code, configuration, interfaces and process knowledge, but outputs must cite the underlying object and environment.

Modernisation support

Conduct positions itself around modernising enterprise systems, starting with SAP. A useful pilot should produce a dependency map, risk register and migration recommendation for a bounded process—not an open-ended transformation narrative.

Operational assistance

AI may help investigate incidents and answer system questions. Production advice should distinguish observation from change, enforce environment boundaries and require approval for consequential actions.

Knowledge capture

Retiring experts and fragmented documentation create material risk. Generated documentation can help, provided it is versioned, reviewed by system owners and refreshed when transports or configuration change.

SAP starting point

A category focus can create deeper context than a generic coding assistant. Buyers should confirm supported SAP products, versions, custom languages, integrations and migration targets.

Example Conduct workflow

Select one SAP business process with known custom code and interfaces. Conduct maps relevant objects, explains dependencies, identifies uncertain areas and proposes a modernisation sequence. Internal experts score completeness and correctness against a hidden reference, then validate that every recommendation links to inspectable evidence.

The workflow should be repeated with normal, edge-case and deliberately difficult inputs. Record completion, human edits, exceptions, failures and downstream consequences. Average quality can conceal a small number of expensive errors, so results should also be segmented by task and risk.

Conduct pricing in 2026

Conduct did not present a complete public price list during this review. Expect enterprise scoping based on systems, environments, data volume, integrations, deployment and services. Ask for a paid pilot with fixed deliverables, acceptance criteria, data boundaries and a clear path to export every artefact created.

Pricing was checked on 20 July 2026 and can change. Ask the vendor to separate platform, implementation, usage, connectors, storage, support and overage costs. Build low, expected and high-volume scenarios, include internal administration, and insist that renewal assumptions are visible. A discount on an unclear unit of consumption is not cost predictability.

Security, privacy and governance questions

Before connecting production data, request the current security pack, subprocessors, architecture, data-flow diagram, retention schedule, deletion process and incident terms. Confirm encryption, SSO, role-based access, audit logs, regional processing, model-provider terms and whether customer data trains shared systems.

Create separate permissions for reading, drafting and acting. Use service identities rather than personal credentials, and give every automated action an owner, limit and revocation path. Test prompt injection and poisoned source content where AI interprets untrusted text. Export and deletion should be demonstrated, not answered only in a questionnaire.

If the product influences public visibility, customer communication or generated answers, establish an external baseline with our LLM Visibility Checker and document what changed. Software can reveal or automate work, but it does not replace the authority signals created through relevant coverage and credible sources; that is where 1stpage Agency’s link-building services serve a different execution need.

Advantages

• Targets an expensive and under-served enterprise knowledge problem
• SAP focus may provide more useful context than generic AI assistants
• Could connect discovery, modernisation and operations
• Knowledge capture may reduce dependence on a few experts

Limitations and unresolved questions

• Public product and pricing detail is limited
• Enterprise-system errors can have high operational impact
• Integration and permissions are likely substantial
• Benefits depend on access to accurate system and process evidence

These are diligence items rather than automatic disqualifiers. The purpose of a pilot is to convert them into evidence, contractual commitments or a clear decision not to proceed.

Who should use Conduct?

Conduct is best suited to large organisations with complex SAP estates, scarce system knowledge and a defined modernisation or operations problem. The team should have a measurable baseline, an operational owner and enough representative work to test repeatably.

It is less suitable for small businesses, greenfield application teams or buyers looking for a self-serve AI coding assistant. In that case, a narrower tool, existing platform capability or improved manual process may create more value with less integration and governance overhead.

A practical pilot plan

Start with one bounded workflow and 30 to 100 representative cases. Include routine examples, edge cases, incomplete inputs and known failures. Keep a human-labelled reference set hidden from the system, then measure accuracy, completion, time saved, edit rate and serious-error frequency.

During week one, connect only a sandbox or read-only source. During week two, let users review suggested outputs. During week three, enable reversible low-risk actions if thresholds are met. Preserve the existing process as a control group. Interview both enthusiastic and reluctant users; adoption data without reasons is difficult to interpret.

Define stop conditions before testing. Examples include exposure of restricted data, actions outside scope, unsupported claims, unrecoverable changes or a serious error above the agreed threshold. At the end, calculate value after review time, exceptions, implementation, licences and retained tools—not before those costs.

Procurement checklist

• Obtain an itemised three-year cost model and renewal cap.
• Confirm contract definitions for users, assets, tasks, usage and overages.
• Map every integration, permission and data category.
• Require export formats, deletion timing and transition assistance.
• Review uptime, support severity, recovery and incident commitments.
• Agree pilot acceptance thresholds and who signs them off.
• Ask for references with similar scale, industry and workflow complexity.
• Document which vendor claims remain unverified.

Conduct alternatives

An alternative should be tested on the same input set and scored against the same outcomes. Feature counts are a weak comparison because two products may label a capability similarly while requiring very different implementation, review and governance effort.

For another view of how we separate product claims from buyer evidence, see our Nimt.ai review and Peec AI review. Those products serve different jobs, but the citation, pricing and pilot disciplines remain relevant.

Is Conduct worth it?

Conduct has an ambitious proposition: use AI to help teams understand, modernise and run complex enterprise systems, beginning with SAP. The potential value is substantial where institutional knowledge and legacy customisation slow change, but public product and pricing detail remain limited enough that buyers should treat any engagement as a tightly scoped, evidence-heavy pilot.

The strongest purchase case is a measured improvement in a costly recurring workflow. The weakest is a broad ambition to “use AI” without baseline data, owners or acceptable-error definitions. Enter commercial discussions with the pilot dataset and security questions prepared; that changes the conversation from feature theatre to operational evidence.

Final verdict

Conduct deserves consideration for the specific best-fit users identified above, but this research-based review cannot establish production reliability or return on investment. Shortlist it if the workflow is frequent and valuable, then require a controlled pilot, inspectable evidence, reversible actions and transparent total cost. Do not scale solely on vendor-reported outcomes or a curated demonstration.

Frequently asked questions

What does Conduct AI do?

Conduct positions its AI to help enterprises understand, modernise and operate complex systems, beginning with SAP.

Is conduct.com the reviewed company?

No. The product reviewed here is at conduct.ai.

Does Conduct publish pricing?

A complete public price table was not available; enterprise scoping is required.

Was this Conduct review hands-on?

No. It is a research-based first look using official public evidence. No authenticated workspace or production integration was tested.

Did Conduct pay for inclusion?

No commercial relationship was disclosed for this review, and no rating was assigned.

Tolu S.

Associate Director

Evidence-led product reviews and founder profiles for search, marketing, authority, and AI visibility teams.