AWS AI Business Strategist

Not every problem needs the same tool. You compare predictive, generative and agentic approaches, decide between buying, building and customizing, and match techniques to business needs without writing code.

• Predictive versus generative versus agentic
• Buy, build or customize
• Prompting, retrieval and fine-tuning at a business level
• Matching solution types to business problems
• Vendor and platform evaluation criteria
• Reading technical proposals critically

You build A decision matrix that selects a solution type for five business problems.

AI strategy begins with the company’s goals, not with a tool. You identify opportunities, rank them by value and feasibility, and turn them into a roadmap with owners and sequence.

• Starting from business goals and constraints
• Finding and listing AI opportunities
• Prioritizing by value, feasibility and risk
• Sequencing a roadmap
• Aligning strategy with leadership
• Positioning AI against competitors

You build An AI opportunity portfolio with a prioritized roadmap for one organization.

Leaders fund what they can measure. You estimate costs and benefits, set baselines and metrics before a pilot starts, and show results in terms that finance and operations teams accept.

• Costs: build, run, data and change
• Benefits: savings, revenue and quality
• Baselines and success metrics
• Return on investment and payback
• Attributing results to the AI initiative
• Presenting a case to a skeptical committee

You build A business case with costs, benefits, metrics and a measurement plan for one use case.

Responsible AI affects customers and staff in concrete ways. You apply fairness, transparency, accountability and human oversight to real decisions, and judge when an AI use should not go ahead.

• Fairness and bias in business outcomes
• Transparency and explanation to customers
• Accountability and human oversight
• Impact assessments for AI uses
• Handling complaints and appeals
• When to stop or redesign a use case

You build A responsible AI assessment of one proposed use, with decisions and conditions.

Without structure, AI use spreads unseen. You set up committees, policies and roles, map regulatory duties, and keep a risk register that covers models, data, vendors and misuse.

• Governance structures and decision rights
• AI policies and acceptable use
• Regulatory and contractual obligations
• Risk identification and the AI risk register
• Third-party and vendor risk
• Incident handling and review

You build A governance framework with roles, policies and a risk register.

Many AI plans fail on the ground they stand on. You assess maturity in data, infrastructure, security and skills, and set the foundations a project needs before it starts.

• Readiness and maturity assessment
• Data ownership, quality and access
• Infrastructure and platform decisions
• Security and privacy foundations
• Skills and capacity gaps
• Closing gaps in sequence

You build A readiness assessment with a gap list and a plan to close each gap.

People decide whether AI is used well. You plan communication, training and role changes, address resistance, and measure adoption so that tools reach daily use.

• Change management for AI adoption
• Communicating purpose and limits
• Training programs by audience
• Addressing resistance and fear
• Changing roles and workflows
• Measuring adoption

You build A change and skills plan for one department adopting an AI tool.

A pilot that works once proves little. You design pilots with clear exit criteria, decide what scaling requires in operations and funding, and build the structures that keep AI work consistent across teams.

• Designing pilots with exit criteria
• From pilot to production requirements
• Operating models and centers of excellence
• Funding and ownership
• Reusing platforms and patterns
• Reviewing and retiring AI systems

You build A scaling plan that takes one pilot to enterprise use, with owners and funding.

Bring the whole subject together in one proposal. You choose a use case, justify the investment, plan governance and change, and present it as you would to the leaders who must approve it.

• Choosing and framing the opportunity
• Building the case and the risk view
• Planning readiness, change and scale
• Anticipating objections
• Presenting the proposal

You build A complete AI proposal covering strategy, value, risk, readiness and scaling.