AI Consulting

It starts with an AI readiness assessment: current tooling, workflow analysis, data infrastructure, and team capability. From there, NDA designs an implementation roadmap scoped to the use cases with the highest ROI — typically starting with one or two workflows where AI can produce measurable improvement within 90 days. Implementation, integration, and training follow the roadmap on a defined cadence.

Strategy consulting produces a plan: use case prioritization, build-vs-buy recommendations, roadmap, and governance framework. Implementation executes it: tool configuration, integration development, workflow redesign, and team training. Many organizations get a strategy document and stall at implementation. NDA scopes both together because strategy without implementation produces PowerPoint decks, not capability.

Four dimensions: data readiness (do you have the data AI systems need, and is it clean and accessible?), workflow readiness (are the target workflows documented and stable enough to automate?), team readiness (do stakeholders understand what AI can and can't do, and is there appetite for the process change implementation requires?), and infrastructure readiness (can your existing systems integrate with AI tooling?).

Starting with the technology rather than the problem (finding use cases for a tool already purchased), underestimating the data cleaning required before AI can work, running pilots without a clear success metric, failing to plan for process change and adoption, and treating AI implementation as an IT project rather than a business change initiative.

A responsible AI policy covers: approved tools and use cases, data handling rules (what company and client data can be sent to external AI systems), output review requirements (what AI outputs require human review before use), disclosure rules (when must clients or employees know AI was involved), and a review process for adding new tools. Without a policy, organizations accumulate AI usage they can't govern.

The goal of NDA's consulting engagements is to transfer capability, not create dependency. This means: training internal champions who can evaluate and deploy new AI tools, documenting workflows so they can be maintained without the original implementer, building internal governance so new use cases go through a defined evaluation process, and establishing KPIs so the team can measure AI impact independently.

Marketing AI use cases cluster around content production, personalization, campaign optimization, and lead qualification. Operations AI use cases cluster around process automation, document processing, quality control, and predictive maintenance. The underlying AI capabilities overlap — but the integrations, data sources, success metrics, and change management challenges are different. NDA scopes each engagement to the specific workflow context.

Recommendations are scoped to the use case and existing stack rather than a preferred vendor list. Common recommendations include: Claude or GPT-4 for knowledge work augmentation, Make or n8n for automation workflows, HubSpot or GoHighLevel for AI-enhanced CRM, and custom RAG implementations for organizations that need AI to work against proprietary knowledge bases. NDA is platform-agnostic — the right tool is the one that fits the actual workflow.

Readiness assessment: 2–3 weeks. Roadmap design: 2 weeks. First implementation sprint (one to two use cases): 6–8 weeks. Total for initial engagement: 10–13 weeks. Ongoing retainer engagements extend beyond this for organizations running multiple implementation tracks or requiring continuous optimization.

The clearest ROI metrics are time saved per workflow (hours per week, multiplied by fully-loaded labor cost), error reduction (quantified as rework cost or incident cost), and revenue impact (pipeline generated or accelerated by AI-enhanced processes). ROI calculations should be scoped to specific implemented workflows, not projected across hypothetical future use cases that haven't been built yet.