CxO™ DELIVERABLE
AI Implementation Planning
AI implementation planning is the work of turning an approved AI use case into a build-ready plan: the data, connectors, model choice, human checkpoints, security controls, tests and success measures. DAG Tech's fractional chief AI officer writes the plan so whoever builds it can move fast and get it right the first time.
Custom proposal · Last updated October 2026
The Short Answer
What Is AI Implementation Planning?
AI implementation planning produces an engineering-level plan that specifies how each approved AI use case will be built, connected to your systems, secured, tested, rolled out and measured. The roadmap says what to build. The implementation plan says how. AI projects rarely stall because of the model. They stall on the plumbing around it: no clear system of record for the data, a legacy system with no usable API, undefined handoffs between the AI and a person, no baseline to measure against, or a security review that happens at the end and sends the work back. A good plan settles those questions on paper, when changes are cheap. It also keeps rules-based logic, such as math, dates and approvals, in normal code and saves AI for the work that needs judgment, like reading, summarizing and drafting.
In a CxO engagement this deliverable follows the AI strategy and roadmap, one use case or a small group at a time. Your fractional chief AI officer maps the current workflow, inventories the systems and data, and designs the target state. When an integration path is not available, the plan treats it as a finding with costed options, not a surprise mid-build. The CAIO then recommends who should build it: your team, another provider, or DAG Tech's AI consulting team when we are the best fit.
At a glance
SPEC SHEET- DELIVERABLE TYPE
- Engineering-level implementation plan
- ALSO KNOWN AS
- AI implementation plan, AI solution design, AI project plan
- PRACTICE AREA
- Artificial Intelligence (CxO)
- BEST FOR
- Approved use cases about to move from idea or pilot to production
- WHAT WE NEED
- Process owners, system admins, the roadmap or use case brief
- NEXT STEP
- Build by your team, a provider or DAG Tech
Scope
What We Plan
Each plan covers the same sections, so use cases can be compared and handed to any capable builder.
Workflow breakdown
Each step of the current process, who does it, how long it takes, and whether it is rules-based or needs judgment.
Systems and data inventory
Systems of record, the data objects involved, who can access them, and shadow sources like spreadsheets and shared mailboxes.
Connectors and APIs
Interface types, authentication, data direction, rate limits, failure handling and how the model reaches tools and data, such as Model Context Protocol.
Solution design and model choice
Off-the-shelf or custom, hosting, grounding in your data, prompts and tools, guardrails and run cost.
Human checkpoints
Where a person reviews or approves, set by risk level, with audit trails and fallback paths.
Security and compliance
Data classification, least-privilege access, secrets handling, vendor terms and alignment to frameworks such as HIPAA, CMMC or the NIST AI RMF.
Testing and rollout
A test set of real examples, numeric acceptance criteria, environments and a staged rollout plan.
Measurement and monitoring
KPIs tied to the baseline, quality and drift monitoring, and a review cadence after go-live.
The Deliverable
What You Receive
- A current-state workflow map with baseline times and volumes
- A systems, data and connector inventory with access requirements
- A target-state solution design with model and hosting choices
- A human checkpoint and security control plan by risk level
- A phased build plan with test cases and acceptance criteria
- A measurement plan and cost estimate to build and run
How It Works
How It Runs
- STEP 01
Kickoff
We confirm the use case, goals and the people who own the process and systems.
- STEP 02
Map
We walk the current workflow and inventory every system, data source and handoff.
- STEP 03
Design
We design the solution, connectors, checkpoints, controls, tests and rollout.
- STEP 04
Readout
We review the plan with your team and the chosen builder and confirm acceptance criteria.
When to Start
Signs You Need AI Implementation Planning
- A pilot worked in a demo and we do not know how to put it into production.
- Our builder is asking questions about data and access we cannot answer.
- We are not sure which steps need a person to review the AI's output.
- Security or compliance has not looked at the AI project yet.
- Key data sits in spreadsheets, inboxes or a system with no API.
- We have no baseline to prove the AI made a difference.
Compare
AI Implementation Planning vs. AI Strategy Roadmap
| AI Implementation Planning | AI Strategy & Roadmap | |
|---|---|---|
| Question it answers | How exactly do we build, connect, secure and test this use case? | Which AI use cases should we pursue, and in what order? |
| Output | Build-ready plan per use case with tests and acceptance criteria | Ranked use case portfolio and phased roadmap |
| When | After a use case is approved and before the build starts | Before funding AI work beyond a few pilots |
| Led by | Fractional chief AI officer | Fractional chief AI officer |
Related
Often Paired With
FAQ
AI Implementation Planning Questions
What is in an AI implementation plan?
A current-state workflow map, systems and data inventory, connector design, solution and model choices, human checkpoints, security controls, a test plan with acceptance criteria, a staged rollout, and a measurement plan. Each use case uses the same sections so they can be compared.
Does DAG Tech build the solution too?
The plan is written so any capable team can build it. Your fractional chief AI officer recommends the best builder: your in-house team, another provider, or DAG Tech's AI consulting team when we are the best fit.
Why not just start building?
Most AI projects stall on data access, integrations, review steps and late security findings. Settling those on paper costs far less than finding them mid-build, and gives the builder clear acceptance criteria.
How much does AI implementation planning cost?
Each plan is scoped in hours at the CxO rate, and your proposal shows the total before any work starts. Scope depends on how many systems and steps the use case touches. Every proposal is custom-scoped.
Can you plan a Microsoft 365 Copilot or Google Gemini rollout?
Yes. For a platform rollout the plan focuses on license assignment, permission and labeling cleanup, connectors to other data, pilot groups, training and usage measurement instead of a custom build.
How do you decide where people review AI output?
By risk. Low-risk drafts may need only spot checks, while anything that reaches customers, money or regulated data gets a required approval step. The plan names each checkpoint, who owns it, and what is logged.
Get Started
Request an AI implementation plan
Tell us what you need. We scope the hours and send a CxO proposal.
Sent with your request: CxO: AI: AI Implementation Planning
- 01A scoping call with a fractional executive
- 02Every task, deliverable, hour and cost in one proposal
- 03A roadmap with start date
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About DAG Tech AI Implementation Planning
AI implementation planning is the work of turning an approved AI use case into a build-ready plan: the data, connectors, model choice, human checkpoints, security controls, tests and success measures. DAG Tech’s fractional chief AI officer writes the plan so whoever builds it can move fast and get it right the first time.
AI implementation planning produces an engineering-level plan that specifies how each approved AI use case will be built, connected to your systems, secured, tested, rolled out and measured. The roadmap says what to build. The implementation plan says how. AI projects rarely stall because of the model. They stall on the plumbing around it: no clear system of record for the data, a legacy system with no usable API, undefined handoffs between the AI and a person, no baseline to measure against, or a security review that happens at the end and sends the work back. A good plan settles those questions on paper, when changes are cheap. It also keeps rules-based logic, such as math, dates and approvals, in normal code and saves AI for the work that needs judgment, like reading, summarizing and drafting.