Service Areas

AI Strategy and Operating Model

For organizations that need a clearer path from experimentation to coordinated, governed execution.

Typical scope

• Data & AI readiness assessment.

• Use-case prioritization.

• Value and ROI framing.

• Transformation roadmap.

• Operating model and ownership design.

What clients get
A clearer view of where AI should create value in the operating model, what capabilities need to be built, and how leadership should organize for execution.

AI Governance and Accountability

For leadership teams that need stronger oversight, decision clarity, and practical guardrails.

Typical scope

• Governance charter.

• Risk-tiering model.

• Decision rights and RACI.

• Review and escalation structure.

• Executive briefing materials.

What clients get
A governance model that supports adoption, clarifies accountability, and gives leaders a more practical basis for oversight and assurance.

Executive Advisory and Transformation Support

For teams that need senior-level support translating AI strategy into operational progress and leadership decisions.

Typical scope

• Steering committee support.

• Governance board facilitation.

• Initiative and pilot oversight.

• Executive reporting.

• Leadership alignment and coaching.

What clients get
Stronger executive alignment, better visibility into progress and risk, and more disciplined follow-through across the work.

How engagements are structured

Support can be shaped as a focused diagnostic, a defined strategy and governance engagement, or ongoing advisory support tied to leadership decision cycles.

Good fit for this work

• Executive teams shaping enterprise AI direction.

• CIOs, CDOs, and data leaders building enabling capability.

• Functional leaders responsible for adoption, oversight, or risk.

• Cross-functional teams aligning strategy, governance, and execution.

Practical support for leaders moving AI into the operating model.

I help leadership teams define the strategy, governance, and operating model needed to scale AI responsibly and credibly.

Ways To Work Wogether

Each engagement is deliberately scoped around a decision or operating outcome. You receive usable working materials, clear ownership, and a practical next-step plan—not a generic strategy deck.

AI Value Readiness Intensive

For organizations that need a clear, evidence-based view of where they stand and what to do next.

The AI Value Readiness Intensive helps leadership understand the organization’s readiness to create and scale value from AI. It identifies strengths, blockers, decision gaps, and the actions that matter most over the next 90 days.

What is included

• Kickoff session to confirm business context, strategic priorities, and the decisions the work needs to inform

• Review of relevant strategy, governance, technology, data, and transformation materials

• Stakeholder interviews across leadership, business functions, data and technology, risk, legal, security, and people teams as applicable

• Assessment across strategy, leadership, value definition, data and technology foundations, governance, workflow redesign, workforce readiness, adoption, and measurement

• Current-state findings and maturity view

• Priority risks, constraints, and capability gaps

• Leadership readout and facilitated discussion

• A practical 90-day action plan with recommended owners, decisions, and sequencing

What you leave with

• A shared executive view of the current state

• A fact-based list of the few gaps that most limit progress

• Recommended decisions for leadership

• A sequenced action plan instead of an abstract maturity score

• Clear options for the next phase of work

Typical timeline: 2–3 weeks

Best next step:

Executive AI Value Workshop

For leadership teams that need alignment before committing more money, technology, or organizational attention to AI.

This is not generic AI training. It is a focused, facilitated working session for executives and senior leaders who need shared language, practical choices, and an agreed set of next moves.

What is included

• Pre-session input gathering to understand executive questions, existing initiatives, and decision points

• A facilitated half-day or full-day executive working session

• Discussion of business value, workflow impact, data readiness, risk, governance, workforce implications, and measurement

• Structured identification of priority opportunities, decisions, and unresolved questions

• An action-and-ownership record after the session

• A concise executive memo documenting decisions, assumptions, risks, and recommended next steps

What you leave with

• Shared leadership language for AI value and responsible adoption

• Clear choices about where to focus first

• Alignment on what needs further evidence, sponsorship, or governance

• A documented path for the next 30 days

Typical timeline: 1-2 weeks, including pre-work and follow-up

Best next step:

AI Use-Case Portfolio Sprint

For organizations with a long list of AI ideas but no defensible way to choose what should move first.

The AI Use-Case Portfolio Sprint creates a shared, evidence-based pipeline of AI opportunities. The goal is not to identify the most exciting idea. It is to identify the work that can create meaningful value, can be delivered responsibly, and has a realistic path to adoption.

What is included

• Use-case intake across selected functions or business units

• Working sessions with business, data, technology, operational, and risk stakeholders

• Evaluation of each use case across strategic value, workflow fit, financial or capacity impact, data readiness, technical feasibility, risk, change impact, and measurement readiness

• A prioritized portfolio view and decision heatmap

• A shortlist of the most viable opportunities to pursue, defer, redesign, or stop

• A pilot charter for the highest-priority use case

• An executive memo covering recommended sequencing, dependencies, and decisions required

What you leave with

• A defensible prioritization process

• A shared portfolio instead of disconnected team wish lists

• A smaller, stronger shortlist of initiatives

• A practical first pilot with measurable success criteria

• A clear basis for investment and governance decisions

Typical timeline: 3-4 weeks

Best next step:

AI Governance Foundation

For organizations that need practical guardrails and accountable decision-making without creating a bureaucracy that prevents progress.

AI governance should help people make better, safer decisions at the pace the business needs. This engagement establishes the minimum viable operating structures, roles, review practices, and documentation needed to support responsible AI use and expansion.

What is included

• Review of existing policies, risk practices, data rules, technology controls, and AI activity

• Identification of governance gaps, decision points, and accountabilities

• Practical AI principles aligned to your business and risk environment

• AI use-case intake and assessment workflow

• Risk-tiering criteria and escalation paths

• Roles, decision rights, and human-review expectations

• Core templates for use-case review, pilot approval, model/vendor review, and incident or issue escalation

• Recommended governance cadence, forums, and reporting structure

• Leadership readout with implementation priorities

What you leave with

• A usable governance foundation, not a policy-only document

• Clear ownership for key AI decisions

• A repeatable way to evaluate new use cases

• Proportionate review practices based on risk

• Working materials that your team can continue to use

Typical timeline: 4-6 weeks

Best next step:

Team AI Adoption Sprint

For a function or operational team that needs to turn AI interest into safer, better day-to-day work.

This engagement focuses on one team’s real workflows: where work takes too long, where quality is inconsistent, where decisions are delayed, and where AI can help without removing necessary human judgment.

It can be tailored for teams such as data and analytics, marketing, finance, project management, IT and security, people operations, sales and customer success, facilities, or food and beverage operations. Your existing playbook architecture already supports a corporate foundation with functional modules, rather than treating every team as if it has the same needs.

What is included

• Team workflow and pain-point inventory

• Identification of suitable AI-enabled work patterns and unsafe or unsuitable uses

• Team-specific guardrails and review requirements

• Role-based prompts, templates, and working practices

• Pilot workflow design for selected high-value opportunities

• Manager enablement materials and adoption plan

• Team working session or training session

• Practical measures for use, quality, time, capacity, and business value

• Retrospective and scale recommendation

What you leave with

• A team-specific implementation approach

• Real use cases tied to actual work

• Practical prompts, templates, and guardrails

• A way to measure whether the work is improving

• A repeatable adoption model for the next team

Typical timeline: 4-6 weeks

Best next step:

Pilot-to-Scale Operating Design

For organizations that have identified a priority use case and need to make it operational.

A successful proof of concept is not the same as a scalable capability. This engagement designs the operating conditions required to move an AI initiative into durable use: workflow ownership, controls, measures, adoption, support, decision routines, and scale criteria.

What is included

• Review of the priority use case, pilot results, stakeholders, technology, data, and workflow

• Target operating workflow and role design

• Definition of human oversight, exception handling, escalation, and quality review

• Value hypothesis, baseline, performance measures, and reporting approach

• Adoption and change plan

• Governance requirements and scale gates

• Operating cadence for performance, risk, and continuous improvement

• Executive scale-readiness decision memo

What you leave with

• A defined operating model for the initiative

• Clarity on who owns the work and how quality is managed

• A credible measurement approach

• Specific conditions that must be true before scaling

• A practical roadmap for the next phase

Typical timeline: 6-10 weeks

Best next step:

Request Information

For consulting, advisory, or selected speaking inquiries, reach out directly at Amanda@BaileyAppliedIntelligence.com.

Contact

Start a conversation about AI strategy, governance, or operating model design.

I work with leadership teams that need clearer direction, stronger governance, and a practical path from AI experimentation to execution. For consulting inquiries, advisory support, or speaking requests, reach out directly.

Email

Amanda@BaileyAppliedIntelligence.com

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Linkedin

www.linkedin.com/in/amandakbailey

PHONE

+1 (424) 249-9862