Get in Touch

5-STEP DELIVERY MODEL

The ConnexR AI Delivery Model: Five Governed Stages From Strategy to Production

A structured AI implementation methodology that takes enterprise AI from business case to controlled production release and ongoing operation, with clear decisions, defined deliverables, and one accountable delivery organization at every stage.

Discuss Your AI Program Explore the Five Stages
  • Named Delivery Lead
  • Documented Stage Decisions
  • Controls Designed From Discovery Onward
  • Operations Under Agreed Service Scope
Enterprise Delivery Portrait

KEY METRIC ACCREDITED

5 Stages

Discovery · Architecture · Build · Deploy · Optimize

Outcome Overview

Enterprise AI Programs Are Won or Lost Between the Stages

Most AI delivery methods look similar on paper. The difference shows up at the transitions: when a strategy meets real data and systems, when a pilot meets security review, and when a new system meets the people who must operate it. Context, assumptions, and ownership are easily lost at each of those points, and that is where timelines and value most often slip.

ConnexR manages those transitions deliberately. One accountable delivery organization coordinates the specialists each stage needs, under a named delivery lead, with shared documentation and controlled transitions between stages. The model also connects to how value is measured: Discovery sets the efficiency baseline and business case, Deploy confirms operational performance, and Optimize tracks realized benefits and ongoing costs.

Methodology

Five Stages, Each Ending in a Clear Decision

Every stage answers one question the business needs settled before it commits further investment.

One Question Settled at Every Stage

01 START HERE

Discovery

Answers:

Is this worth solving, who owns it, and can the data support it?

Every Stage Closes with a recorded decision

Findings also flow backward: test results can refine design thresholds, and production insight shapes the next use cases.

  1. Proceed
  2. Rework
  3. Pause
  4. Stop

How Time Is Structured

  1. The first measurable result from the first use case in production or controlled rollout. Target timing is agreed in Discovery.

  2. The end of the first Deploy stage, planned against scope, data readiness, and integration effort.

  3. Further use cases and functions delivered in phased releases, often with stages overlapping across use cases.

  4. The Optimize stage, provided under a separate managed-service agreement with its own term and scope.

THE DELIVERY GAP

Why AI Programs Stall Between Pilot and Production

%+

Gartner forecasts that over 40%…

Source: Gartner press release, June 25, 2025…

The obstacles are rarely about the algorithm…

  1. Roadmaps that are not tested against data availability, system integration, and operating requirements often need to be reworked once delivery begins, which delays funding decisions and resets expectations.

  2. Proofs of concept built on curated data, outside security review, and disconnected from live systems can show promise yet require significant rework before they can run in production.

  3. When privacy, security, explainability, and human oversight are reviewed just before launch, design changes become more expensive and releases can be delayed.

  4. Without an agreed owner for performance, cost, and user adoption, issues can go unaddressed after launch and the value the business case expected becomes harder to realize.

How ConnexR Delivers Enterprise AI, Stage by Stage

Our AI implementation methodology is use-case dependent: the stages stay constant, while the techniques inside them are chosen for the problem. Each stage has a defined objective, deliverables, the input we need from you, and an acceptance decision.

Objective:

Confirm the problem is worth solving, who owns the outcome, and whether data and systems can support it.

Activities:

  • Business ownership and success measures agreed with the sponsor
  • Baseline of current performance (see Efficiency Gained)
  • Data access, quality, and permissions review
  • Feasibility assessment and use-case prioritization
  • Initial risk assessment: privacy, security, regulatory, and model risk

Deliverables:Prioritized roadmap, business case with benefit rules (see Average ROI), baseline report, initial risk register

Your input:A business owner, process experts, sample data, system documentation, and current performance data

Acceptance decision:The business sponsor, with IT and risk stakeholders, agrees the value target, data path, and whether to proceed

Objective:

Design a solution that can operate securely, reliably, and economically in your environment.

Activities:

  • Approach selection for the use case: rules, machine learning, generative AI, agents, or integration. Training, fine-tuning, or retrieval only where the use case warrants it
  • Integration and data design with your systems of record
  • Evaluation criteria and acceptance thresholds
  • Human oversight design: review points, escalation paths, and permission boundaries
  • Operating requirements, support model, and run-cost assumptions; controls designed from the risk register

Deliverables: Solution architecture, evaluation plan, control and oversight design, run-cost model

Your input:Enterprise architecture standards, security and compliance requirements, and reviewers from IT and risk

Acceptance decision: Architecture, security, and business owners approve the design and acceptance criteria

Objective:

Produce an evaluated release candidate.

Activities:

  • Iterative development in controlled development and test environments, using representative, authorized enterprise data, masked or synthetic where appropriate
  • Integration with target systems and workflows
  • Evaluation against agreed criteria. For generative AI and agents, examples include grounding in approved sources, task success, permission boundaries, correct human escalation, latency, cost per transaction, and adversarial testing
  • User acceptance testing with business users

Deliverables: Release candidate, evaluation report and test evidence, technical and operational documentation

Your input:Subject-matter experts for testing, test data provisioning, and environment access

Acceptance decision:Business and technical owners confirm the release candidate meets the agreed thresholds

Objective:

Move into production in a controlled, reversible way.

Activities:

  • Release approval through your change process
  • Phased rollout suited to the use case: shadow mode, limited release, then wider release
  • Rollback and fallback to manual handling, tested before release
  • Monitoring and alerting in place from day one
  • User readiness: training, updated procedures, and support channels

Deliverables:Production release, runbooks, monitoring dashboards, agreed support model, trained users

Your input:Change approvals, production access, and a business readiness lead

Acceptance decision: Business and IT operations owners give operational acceptance based on production performance against the baseline

Objective:

Sustain and improve performance and value within the agreed service scope.

Activities:

  • Monitoring of performance, quality, and run cost
  • Periodic review of realized benefits and ongoing costs (see Average ROI)
  • Investigation when performance changes
  • Changes to models, prompts, retrieval sources, or workflows evaluated before release
  • Prioritized backlog of the next use cases

Deliverables:Performance and value reports at the agreed cadence, evaluated change releases, roadmap updates

Your input:Benefit owners, user feedback, and participation in the governance forum

Acceptance decision: Periodic service and value reviews decide whether to continue, expand, adjust, or retire the solution

ONE FRAMEWORK, EVERY SERVICE

A Common Delivery Framework That Respects Each Service’s Own Workflow

The five stages give every engagement the same decision points, documentation, and controls. Each service then applies its own specialized workflow inside that framework.

Custom AI Solutions

Where the framework applies:

Where the framework applies:

Specialized workflow it keeps:

The full lifecycle of a custom AI solution, from use-case discovery to production operation.

AIOps & Managed IT Operations

Where the framework applies:

The transition into managed operations: scope, service levels, and integrations are defined in Discovery and Architecture; onboarding and instrumentation are delivered and accepted through Build and Deploy.

Specialized workflow it keeps:

The continuous operating cycle of monitoring, detection, response, remediation, and reporting runs within Optimize under the service agreement.

Digital & AI Transformation

Where the framework applies:

Each initiative within the transformation portfolio.

Specialized workflow it keeps:

Program-level work: value discovery across functions, operating model design, a center of excellence, and adoption at scale.

Cloud & AI Infrastructure

Where the framework applies:

Platform and migration programs, including decision points and acceptance.

Specialized workflow it keeps:

Landing-zone design, migration waves, resilience testing, and cloud cost management.

Enterprise Systems Modernization

Where the framework applies:

Each modernization wave across ERP, CRM, and experience platforms.

Specialized workflow it keeps:

Incremental platform releases, ecosystem integration, and master data governance.

Where You Can Start

Entry point Stages Best for Readiness validation
AI Strategy and Discovery Discovery Leaders defining where AI should apply and what it is worth Not required
Pilot to Production Architecture → Deploy A working pilot that has not reached production Review of the pilot’s data, evaluation evidence, security, integration, and ownership; gaps are closed before proceeding
Full Delivery Program All five Multiple use cases across functions Not required
Managed AI Operations Optimize AI or automation already in production Operational readiness review of documentation, monitoring, access, and known issues before service acceptance

BUSINESS IMPACT

What Disciplined Delivery Changes for the Business

  1. Production requirements are designed in from Architecture, so less rework is needed between an approved pilot and a controlled release.

  2. Initiatives that pass the Build gate have already met agreed thresholds, controls, and integration requirements, which improves their readiness for production.

  3. Early risk assessment, recorded gate decisions, and tested rollback reduce late redesigns and launch-day surprises.

  4. Reusable data pipelines, evaluation assets, and control patterns can lower the effort required for later use cases.

Answers for Your Leadership Team

Technology Leaders

Architecture, Security, and Operations

Solutions are designed to your architecture standards and integrate with your systems of record.

Access, logging, and fallback behavior are specified in Architecture and tested before release.

Support hours, service levels, and reporting are defined in the service agreement.

AI & Data Leaders

Evaluation and Change Control

Evaluation criteria and thresholds are agreed before build, including generative AI and agent-specific tests.

Representative, authorized data is used in controlled environments, masked or synthetic where appropriate.

Changes to models, prompts, retrieval, or workflows are evaluated before release.

Procurement

Responsibilities and Commercials

Responsibilities are documented: ConnexR delivers and operates within the agreed scope; you provide owners, data access, approvals, and apply your own compliance obligations.

Implementation and managed operations are contracted separately, each with its own scope and term.

Commercial models are proposed to fit scope and risk.

Why connexr

Delivery Practices You Can Inspect

  • A Named Lead and a Clear Responsibility Model

    A named delivery lead coordinates the specialists each stage needs, with responsibilities documented for ConnexR and your teams from the first week.

    You receive

    You receive: A responsibility matrix and a single point of accountability

  • Recorded Decisions at Every Stage

    Each stage closes with a documented decision, the evidence behind it, and any conditions for the next stage.

    You receive

    You receive: Stage-gate records you can share with steering committees and auditors

  • Controls That Start in Discovery

    Risks identified in Discovery become controls designed in Architecture, which are then tested in Build, confirmed in Deploy, and monitored in Optimize.

    You receive

    You receive: A risk register, control design, and evaluation evidence for each release

  • Build-and-Run Heritage Since 2012

    ConnexR draws on RSA Tech Group’s managed services practice for the runbooks, incident procedures, and service reporting that production AI needs.

    You receive

    You receive: Runbooks, escalation paths, and service reporting under the agreed scope

FAQ’s

Enterprise AI ROI: Questions Finance Leaders Ask

01 What are the stages of your AI implementation methodology?
Five: Discovery confirms value, ownership, and feasibility; Architecture designs for secure, economical operation; Build produces an evaluated release candidate; Deploy moves it into production in a controlled way; and Optimize sustains performance and value under an agreed service scope. Each stage ends with a recorded decision.
02 How long does an enterprise AI implementation take?
It depends on scope, data readiness, integration, and approval cycles. We distinguish initial measurable value, first production release, and broader implementation, and agree target timing for each during Discovery. Ongoing operations are contracted separately.
03 We already have a pilot. Do we need to start with Discovery?
No. We review the pilot’s data, evaluation evidence, security, integration, and ownership, close any gaps, and continue from the appropriate stage. Work that already meets the criteria is not repeated.
04 How do you move AI from pilot to production?
By designing for production conditions early: authorized data, agreed evaluation thresholds, integration with live systems, human oversight, and a phased rollout with tested rollback. The release is accepted only when production performance meets the agreed baseline.
05 What happens after go-live?
Under a managed-service agreement, we monitor performance, quality, and cost, review realized benefits, investigate changes, and release evaluated updates. Support hours, service levels, and reporting cadence are defined in that agreement.
06 How are AI risk and compliance responsibilities shared?
Risks are identified in Discovery, controls are designed in Architecture, and both are tested and monitored through later stages. ConnexR designs and operates the agreed controls; your organization determines which regulations apply and makes the final compliance decisions for its own use of the solution.

Explore More Proven Outcomes

  • Proven Outcome

    Efficiency Gained

    How AI workflow automation reduces manual handling time, and how we measure it.

    Read Efficiency Gained
  • Proven Outcome

    Average ROI

    How we measure return as eligible benefits against the full cost of ownership.

    Read ROI Methodology

Discuss Your AI Program

We start by reviewing what you have, not repeating it.

Discuss Your AI Program