AI creates value when it is implemented, not when it is discussed.
Most organizations do not need another AI strategy deck. They need working systems: AI that processes their documents, automates their workflows, runs on infrastructure they control, and holds up in daily use. This practice implements AI and business systems onsite, in the client's environment, and pairs the implementation work with an established AI governance practice.
Six areas of AI and business systems practice.
The common thread is putting AI to work on measurable business tasks, in production, with the controls and records a business needs behind it.
AI Implementation
Deploying AI systems into real business processes: selection support, configuration, integration with existing systems, testing against the organization's own data and edge cases, user training, and stabilization after go-live.
Private & On-Premises AI
Standing up local and private AI environments for organizations whose data cannot leave the building: local model deployment, infrastructure sizing, access control, and the operational practices that keep a private AI environment maintainable.
Document Intelligence
AI-assisted processing of the documents businesses run on: contracts, quality records, invoices, technical documentation, and correspondence, implemented with extraction accuracy testing and human review where the stakes require it.
Finance & Accounting Automation
Automation of finance and accounting workflows, built on direct experience implementing accounting systems: data capture, reconciliation support, reporting, and integrations between financial systems and the operations that feed them.
Business Workflow Automation
Automating the repetitive work between systems: approvals, handoffs, data entry, notifications, and reporting, implemented so the automation is documented, owned, and recoverable rather than a black box one person understands.
AI Governance
The practice's established governance line: AI use inventories, risk assessment, policy, and alignment with the NIST AI Risk Management Framework and ISO/IEC 42001, supported by an extensive published research library.
Explore AI Governance →AI systems should be tested where they will operate.
An AI system can demonstrate well and still fail in production, because the vendor demonstration reflects the vendor's data rather than the customer's. This practice implements AI the way NIST now recommends evaluating it: defined intended use, measurable acceptance criteria, testing against the organization's own data and conditions, and evidence that supports the deployment decision.
Define what success means
Intended use, the workflows affected, the data involved, acceptance criteria, and what the organization will measure. If success is not defined, the pilot never ends.
Test against reality
The organization's documents, its edge cases, its users, and its operating conditions. Accuracy claims are verified rather than accepted, and limitations are documented rather than discovered later.
Govern what was deployed
Records of what was implemented and tested, monitoring for drift, defined triggers for reevaluation, and the policies and oversight the governance practice builds. Implementation and governance from one advisor, and consistent with each other.
Discuss a Project
Inquiries may involve an AI deployment, a private or on-premises AI environment, document or finance automation, or governance for AI already in use. I respond personally to every inquiry, usually within one business day.