AI Strategy Consulting
From AI readiness assessment through implementation, we help organizations deploy AI where it creates real business value.
AI Readiness Assessment
Most organizations know they need an AI strategy but lack the internal expertise to evaluate where AI can create real value versus where it introduces unnecessary risk and cost. We assess your current technology infrastructure, data assets, team capabilities, and business processes to determine where AI adoption will produce measurable returns. This includes identifying which problems are genuinely suited to AI solutions, which are better served by traditional software, and which require foundational data work before any AI initiative can succeed. The result is a prioritized roadmap grounded in your actual business constraints, not a generic list of AI use cases.
From Assessment to Action
We translate the readiness assessment into concrete requirements and selection criteria, so you engage vendors and evaluate build-vs-buy options from a position of clarity rather than guesswork.
AI Vendor Selection and Build-vs-Buy Analysis
We evaluate build-vs-buy tradeoffs and manage vendor selection so organizations negotiate from a position of technical knowledge. Organizations face a fundamental decision: build custom AI capabilities in-house, integrate commercial AI products, use foundation model APIs, or pursue some hybrid. Each path carries different cost structures, IP implications, data privacy considerations, and lock-in risks. We evaluate your specific requirements against the current vendor landscape, assess the true total cost of ownership for each approach, and help you negotiate from a position of technical knowledge. We also manage the implementation process, bridging the gap between your business stakeholders and the AI engineering team to keep projects on scope, on schedule, and within budget.
Enterprise AI Strategy
We work with executives and technical leadership to develop AI strategies tied to concrete business objectives. This goes beyond identifying where AI “could” be applied. We define where it should be applied based on your competitive position, data maturity, and organizational capacity to execute.
A typical engagement begins with understanding your business goals and current capabilities. We then identify specific AI opportunities, model their expected impact, and sequence them based on feasibility and strategic value. For each initiative, we define the data requirements, infrastructure needs, build-vs-buy decision, staffing implications, and success metrics.
For organizations already deploying AI, we audit existing initiatives for effectiveness, identify gaps or redundancies, and realign the portfolio to current business priorities. We also advise on emerging regulatory requirements and responsible AI practices that affect deployment decisions.
AI Implementation Management
We manage the implementation of AI initiatives on behalf of our clients, coordinating between internal teams, external vendors, and executive stakeholders. This includes overseeing vendor deliverables, validating that technical implementations match the agreed-upon strategy, managing scope and timeline, and translating progress into terms that business leadership can act on. For organizations without a dedicated AI or ML team, we serve as the technical point of contact through deployment, ensuring that what gets built matches what was planned.
We also establish reporting structures and milestone checkpoints so that leadership has visibility into progress without needing to interpret technical details. When implementations hit obstacles, whether technical, organizational, or vendor-related, we diagnose the issue and course-correct before it affects the timeline or budget.
Case Studies
AI Strategy for a Regional Healthcare Network
A regional healthcare network with 14 facilities wanted to deploy AI across its operations but had received conflicting vendor proposals and had no internal AI expertise. We conducted a four-week assessment covering data readiness, regulatory constraints, IT capabilities, and clinical workflow integration. The assessment revealed that patient data was siloed across three EHR systems, making most proposed clinical AI applications premature.
We recommended a phased approach: first, consolidate data pipelines; second, deploy AI in revenue cycle management and scheduling where data was already clean; third, pursue clinical decision support once the data foundation was in place. The network avoided an estimated $2M+ in premature vendor commitments and achieved measurable ROI from initial deployments within six months.
Strategy for Berkshire Hathaway
Berkshire Hathaway HomeServices is a subsidiary of the famed holding company Berkshire Hathaway, and it provides real estate agent services across many regions in the United States. The company’s growth strategy is to acquire independent and smaller agencies. Technologically, this means that each new acquisition comes with its own technology stack and tools, making managing and maintaining these tools at the corporate level impossible.
Berkshire Hathaway HomeServices wanted to unify its technology stack by picking a single technology vendor, but lacked the ability to competently evaluate all options. We were hired to understand the technology needs of the disparate agencies and recommend a capable technology vendor.
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