ScalaCode delivers strategic AI consulting and implementation, AI roadmap design, build-vs-buy advisory, model selection, architecture review, MLOps maturity assessment, and full delivery oversight, for enterprises across 45+ countries. With 13+ years of production AI deployment, our consultants combine engineering depth with the commercial framing, total cost of ownership, time-to-value, organizational change, that boards and CFOs need to underwrite AI investment.
Whether you need a 4-week AI opportunity assessment for an executive committee, an architecture review on an in-flight RAG platform, a build-vs-buy decision on agent infrastructure, or program-level oversight on a multi-vendor AI initiative, our AI consultants deliver advisory that moves the metrics that matter, investment ROI, decision velocity, program risk.
Eight consulting engagement types, each designed for a specific decision-making moment in an AI program's lifecycle.
Executive-level engagement to map your organization’s AI opportunity landscape. We assess where AI creates measurable business value, prioritize use cases by feasibility × impact, identify capability gaps, and produce an AI strategy document your board can sign off on. Typical duration: 4-8 weeks. Deliverable: AI strategy with prioritized portfolio and executive alignment.
Once strategy is set, we help build the execution roadmap: which initiatives ship when, what capabilities they require, dependencies, sequencing, investment profile, and phased ROI timeline. Multi-year planning with quarterly milestones tied to measurable business outcomes.
Honest evaluation of your organization’s AI readiness across six dimensions: data infrastructure, technical capability, organizational design, governance maturity, regulatory exposure, and change management preparedness. Identifies the gaps between your AI ambition and your current state, with a practical path to close them.
Independent technology and vendor selection, foundation models, cloud platforms, AI platforms, MLOps tooling, agent frameworks. We run structured evaluations against your specific use-case requirements. No vendor partnerships distorting recommendations. Deliverable: technology decision document with rationale and migration plan.
Establishing the policies, committees, review processes, and technical controls that govern how AI is built, deployed, and monitored in your organization. Covers model risk management, ethical AI principles, data governance for AI, regulatory compliance (EU AI Act, state-level AI regulations, sector-specific rules), and incident response. Critical for enterprises running AI in regulated industries.
Design and operationalize an AI CoE, the centralized function that governs AI standards, methodology, and capability development across business units. Covers how AI is run as a function: team composition, funding model, KPIs, and the cadence with business-unit teams.
Workshops, training, and coaching programs for internal teams, data scientists, ML engineers, product managers, business stakeholders. Covers technical skills (prompt engineering, LLM evaluation, RAG architecture), soft skills (AI product management, stakeholder communication), and governance (model risk, responsible AI). See how to hire dedicated AI engineers when internal capability needs to scale.
Independent review of existing AI programs, portfolio health, delivery velocity, quality of shipped systems, governance maturity, cost profile, team capability. Typical engagement identifies 5-10 concrete improvements that unstick stalled programs.
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You own the enterprise AI program. You need independent strategic input, portfolio review, governance frameworks, and peer benchmarks. Typical engagement: quarterly advisory retainer + annual strategy refresh.
You’re launching AI as a strategic initiative. You need help defining opportunity, building the roadmap, establishing governance, and avoiding the pilot-purgatory trap. Typical engagement: 8-12 week strategy + 6-month execution advisory.
You need an honest outside assessment of your company’s AI position relative to competitors. Is the AI investment thesis working? Where is risk? What’s the competitive delta? Typical engagement: 4-6 week diagnostic + board presentation.
Due diligence on AI capabilities of acquisition targets, assessment of AI-native opportunity in portfolio companies, valuation of AI assets. Typical engagement: focused 2-4 week diligence sprint.
Every AI recommendation starts with the business outcome it creates. Technology selection follows outcome definition, not the reverse. We refuse to sell AI for AI’s sake, many of the strongest “AI strategies” we’ve delivered have ended with “do less AI, do it better.”
We have no revenue-sharing arrangements with cloud providers, foundation-model vendors, or platform vendors. Our recommendations are driven entirely by your use case. If the right answer is to use a competitor’s platform, we say so.
AI strategy involves uncertainty. We quantify it, confidence levels, sensitivity analysis, fallback paths. We name what we don’t know and design approaches that remain valuable under multiple plausible futures.
We don’t treat AI governance as compliance paperwork. It’s a strategic enabler that lets organizations move faster with confidence. Our governance recommendations are operational, not theoretical.
Fairness, explainability, safety, and privacy are addressed at the strategy layer, not bolted on after deployment. Our strategy documents explicitly name the ethical considerations per use case and recommend corresponding controls.
Best AI strategies compound, each project builds organizational capability that the next project benefits from. We design explicitly for capability compounding, not just project-by-project ROI.
Model inventory, risk tiering, validation requirements, ongoing monitoring, incident response. Modeled on established bank MRM frameworks and adapted for generative AI specifics (hallucination risk, training data provenance, prompt injection exposure).
Operationalized principles (not just a PR-friendly values statement). Review board composition, decision rights, escalation patterns, documentation standards. Embedded into product development lifecycles, not a separate side process.
Data lineage requirements for AI training, consent management, cross-border transfer rules, retention policies, anonymization standards, synthetic data usage.
Mapping AI use cases to EU AI Act risk categories. Disclosure, documentation, transparency, and conformity assessment requirements. Analogous work for emerging state-level AI regulations in the US.
Governance of AI systems you didn’t build, vendor-provided AI tools, embedded AI in SaaS platforms, AI-enabled services from your supply chain. Vendor due diligence checklists, contract language, monitoring requirements.
Runbooks for AI-specific incidents: hallucination-driven customer harm, model drift causing business impact, adversarial attacks on AI systems, data leakage through AI interactions, regulatory or ethics complaints.
Stakeholder interviews across executive, middle management, and technical teams. Document review (existing strategy, current projects, governance frameworks). Data and infrastructure audit. Competitive landscape analysis. Deliverable: diagnostic report with clear-eyed baseline assessment.
Strategic workshops with leadership. Opportunity mapping and prioritization. Technology choices and how AI gets run day to day. Roadmap design. Deliverable: strategic recommendations document + executive presentation.
Stakeholder review sessions to build consensus. Refinement of recommendations based on feedback. Investment model and ROI projections. Deliverable: final decision package ready for board or exec committee approval.
Translation of approved strategy into execution plan. Team selection, vendor contracts, governance rollout. Handoff to your internal team or to our delivery team if we’re also executing.
Quarterly strategy reviews, portfolio governance support, escalation advisory. Retainer-based engagement for ongoing strategic partnership.
We’ve shipped 350+ AI systems to production. Our strategy recommendations are grounded in what actually works, not consulting-deck abstractions. When we say “this will take 12 weeks,” we know from experience.
No platform partnerships distorting our recommendations. We regularly recommend approaches that require competitor technology when that’s the right answer.
Every engagement is led by a senior advisor with 10+ years of AI delivery experience. No pyramid-staffed consulting where junior analysts do the work.
Deep experience in regulated industries (healthcare, financial services, insurance). EU AI Act, state-level regulations, sector-specific rules, we bring current regulatory expertise to every strategy engagement.
If you want us to execute after the strategy phase, we can. If you want us to hand off to your internal team or a different delivery partner, we support that too. No incentive to inflate scope for our own delivery pipeline.
Every recommendation ties to a measurable business metric. We resist AI-for-AI’s-sake and push for clarity on what the investment must produce to justify itself.
Our consulting work spans regulated and non-regulated industries. Each has distinct considerations.
FDA pathways for AI/SaMD, HIPAA compliance, clinical validation requirements, patient safety culture. Consulting emphasizes regulatory strategy and clinical-outcome-anchored metrics. Read our AI in healthcare perspective.
Integration with OT systems, edge AI constraints, supply-chain implications, workforce retraining. Consulting spans corporate AI strategy + plant-level implementation planning. See our AI in manufacturing coverage.
Personalization ethics, dynamic pricing regulation, customer-facing AI governance, brand voice consistency at scale. Consulting blends strategy with change management for customer-facing teams.
Network-effect AI strategies, real-time decision systems, carrier/3PL ecosystem implications. Consulting focuses on where AI creates defensible network-effect advantage vs. commodity uplift.
How to use AI to augment knowledge worker productivity while managing the intellectual property, client confidentiality, and pricing model implications. Consulting engagement frequently combines AI strategy + go-to-market redesign.
Focused strategy engagement, opportunity assessment, roadmap, or specific strategic question. Deliverable: strategy document + executive presentation. Typical range: $50K-$150K.
Comprehensive AI strategy development including diagnostic, opportunity mapping, technology selection, governance design, and executive alignment. Typical range: $150K-$400K.
Ongoing strategic advisory, quarterly strategy reviews, ad-hoc escalation advisory, peer benchmarking, governance support. Typical range: $10K-$40K monthly.
Multi-quarter engagement to design and operationalize an internal AI CoE. Covers how the function is structured, team, tools, processes, and 6-12 months of operational support through stabilization.
The enterprises winning in 2026 are moving past disconnected pilots to a formal AI function: centralized strategy, federated execution, shared capability infrastructure. Most of our 2026 consulting work is structuring this function, not picking which pilot to fund.
Chief AI Officer (CAIO) roles are proliferating. AI governance committees now report to boards in regulated industries. Enterprises without formal AI governance will struggle to move fast safely.
EU AI Act is live. State-level US rules (Colorado, NYC, California) are multiplying. Industry-specific regulations (FDA, OCC, FTC) are evolving quickly. Strategy engagements increasingly include regulatory mapping as a core workstream.
Multi-agent systems that execute autonomous workflows are forcing organizations to rethink job design, audit trails, accountability, and quality control. Strategy work now includes how AI agents fit into operational hierarchies.
Early AI programs measured inputs (“we have 10 pilots running”). Mature programs measure outcomes (specific business metrics tied to specific AI capabilities). Strategy engagements increasingly include measurement frameworks for AI value realization. See our coverage of top AI trends in 2026.
AI consulting is the strategic planning and governance work that happens before and alongside AI implementation, opportunity assessment, roadmap design, technology selection, governance frameworks, organizational readiness, team coaching, and program review. AI development (distinct service) is the engineering work of building AI models and integrating them into enterprise systems. Most organizations need both: consulting to plan what to build and why, development to execute. ScalaCode offers both as separate practice areas with different team composition, engagement models, and deliverable types. The teams that advise on AI strategy are typically senior consultants with 10+ years of AI experience and regulatory/industry depth; the teams that build AI are ML engineers, data scientists, and integration specialists.
Engage AI consulting when: (1) you’re starting an enterprise AI program and need an independent baseline strategy; (2) your existing AI portfolio is underperforming and you need an outside diagnostic; (3) you face a specific high-stakes decision (technology platform, CoE design, regulatory compliance approach) where independent expertise matters; (4) you need to benchmark your AI maturity against peers; (5) you’re acquiring or divesting AI capability and need due diligence. Build internal capability when: use cases are routine, your industry domain knowledge is more specialized than outside consultants can match, or you’re already mature enough that advisory adds marginal value. Best practice: use consulting for strategic inflection points, invest internally for steady-state execution.
Engagement length depends on scope. Strategy Sprints (focused on a specific question) run 4-8 weeks. Full AI Strategy & Roadmap engagements run 12-16 weeks. AI Center of Excellence Build engagements run 6-12 months. Quarterly Advisory Retainers run continuously after initial strategy work is complete. AI Due Diligence sprints (M&A context) run 2-4 weeks. ScalaCode’s typical first engagement with a new enterprise client is either a 6-week Strategy Sprint (cost-efficient way to validate fit and approach) or a 12-week Full Strategy engagement (when the client is already committed to a substantial AI program).
AI consulting costs range from $50K to $500K+ per engagement depending on scope. Strategy Sprints run $50K-$150K (4-8 weeks, focused scope). Full AI Strategy & Roadmap engagements run $150K-$400K (12-16 weeks, comprehensive scope). Quarterly Advisory Retainers run $10K-$40K per month depending on advisory intensity. AI Center of Excellence Builds run $400K-$1M+ depending on duration and implementation support. AI Due Diligence sprints run $60K-$150K. We price on fixed-scope engagements with clear deliverables, not time-and-materials, so you know the total investment upfront. Discovery calls are free and typically identify whether consulting is the right engagement type or whether you need development, staff augmentation, or different support.
Our AI governance practice applies sector-specific regulatory expertise, financial services MRM frameworks aligned with OCC/FRB expectations, healthcare AI validated against FDA pathways and HIPAA, EU AI Act compliance mapping for any enterprise doing business in the EU, state-level rules (Colorado AI Act, NYC bias audit, California privacy law) where applicable. Our governance deliverables include model inventory templates, risk tiering methodology, validation protocols, ongoing monitoring requirements, and incident response runbooks. We design governance to satisfy regulators while enabling speed, governance frameworks that slow organizations down are governance frameworks that get circumvented. Every governance engagement includes a training component so your internal teams can operate the governance function after handoff.
Yes. AI consulting is arguably most valuable for enterprises starting their AI program, establishing the right foundation prevents 80% of the common pitfalls we see in mature AI programs (disconnected pilots, ungoverned risk, proliferated tooling without strategy, talent gaps surfacing only after committing). For organizations new to AI, we typically start with a 6-week AI Strategy Sprint that produces: prioritized opportunity portfolio, recommended first 2-3 use cases to build, organizational and governance foundation, technology platform recommendations, and a 12-month roadmap with investment requirements. This becomes the foundation your executive team approves and your implementation teams execute against.
Yes, technology selection is a common AI consulting engagement type. Our approach: define use-case requirements (accuracy, latency, cost, data residency, compliance), run structured benchmarks on candidate models (GPT-4 vs Claude vs Gemini vs open-source LLaMA or Mistral, or platform comparisons like Azure vs AWS vs GCP vs Databricks), document trade-offs transparently, and produce a recommendation with rationale and migration implications. We do NOT have vendor partnership incentives, recommendations are driven purely by your use case. For enterprises with existing cloud commitments, we typically recommend maximizing value within existing vendor relationships before introducing new vendors. For green-field selection, we recommend based on the specific workload characteristics.
Our AI readiness assessment evaluates six dimensions. (1) Data infrastructure, quality, accessibility, governance of data needed for AI workloads. (2) Technical capability, cloud, MLOps tooling, integration readiness. (3) Organizational design, whether AI work is structured for scale (CoE model, embedded model, federated model). (4) Governance maturity, policies, review processes, risk management frameworks. (5) Regulatory exposure, where your industry or use cases require special compliance handling. (6) Change management, workforce readiness, executive sponsorship, cultural factors. Output: a diagnostic report scoring each dimension, identifying critical gaps, and recommending a prioritized remediation plan before major AI investment. Typical assessment duration: 3-4 weeks.
Both, but as separate engagement types with separate teams. Our AI consulting practice focuses on strategy, governance, and advisory work. Our AI development and integration practices handle hands-on implementation (building models, connecting to enterprise systems, shipping production applications). If a consulting engagement concludes with an implementation roadmap, we can execute that roadmap via our development/integration practices, or you can hand it to your internal team or a different implementation partner. We have no incentive to inflate consulting scope to drive implementation revenue because we price both as distinct, transparent practices. Many consulting engagements end with hand-off to internal teams, and we continue as quarterly advisors rather than implementation partners.
We measure consulting success via three tiers of outcome. Immediate (at engagement close): executive alignment achieved, strategy document approved, decisions unblocked, clarity delivered on the specific strategic questions we were engaged to answer. Near-term (3-6 months post-engagement): implementation kickoff, first recommended use cases shipped on schedule, governance frameworks operating, team capability demonstrably improved. Long-term (12+ months): measurable business outcomes from the AI portfolio, maturity progression on how the AI function is run, client self-sufficiency (reduced dependency on our ongoing advisory). We structure engagements to produce concrete artifacts (documents, frameworks, trained teams) that remain valuable after we disengage, not consulting presentations that gather dust.
Everything we asked for was done quickly and efficiently. ScalaCode solved problems without delays or excuses.
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