AI Strategy Consulting
First, define where AI fits in your business and which capabilities matter. We connect AI strategy consulting to your wider technology and transformation goals, so every initiative has an owner, a metric and a reason to exist.
Enterprise AI consulting that turns AI ambition into a practical, fundable roadmap. Because AI consulting services help an organization decide what to build before it commits to building it, SDLC Corp covers AI strategy, use case discovery, readiness assessment and governance. Each engagement then ends with a 90-day roadmap your engineers can pick up.
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What we do
Our enterprise AI consulting team moves you from scattered AI ideas to prioritized initiatives with clear business, technical, data and governance requirements.
First, define where AI fits in your business and which capabilities matter. We connect AI strategy consulting to your wider technology and transformation goals, so every initiative has an owner, a metric and a reason to exist.
AI only pays off when work changes with it. As a result, we plan the operating model, roles, training and adoption steps that turn a working pilot into everyday use across teams.
AI use case discovery starts with mapping opportunities across departments and workflows. We then match each one to the right technique: machine learning, computer vision, NLP, generative AI, automation or plain software. In fact, ruling a use case out is part of the job.
Specifically, we review data, applications, infrastructure, security, people and processes before you commit to a build. Read the guide: AI readiness: how to assess your enterprise before scaling AI.
A credible AI business case starts with a measured baseline. First, we measure your current baseline, then estimate value, effort and running costs, and set the success threshold that justifies moving from pilot to production.
Instead of locking you into one provider too early, we set the direction across predictive models, vision, language models, retrieval, agents and enterprise systems.
In addition, we define accountability, human oversight, access controls, evaluation standards and audit logging before AI reaches production, written against recognized frameworks such as the NIST AI Risk Management Framework. For a company-wide program, see our AI governance consulting services.
Explore all 7 AI consulting services
Method
Every engagement runs on one repeatable method, and every stage ends in a decision rather than a status update.
Priorities, workflows and stakeholders.
Use cases, data, systems, security and readiness.
Opportunities by value, feasibility and effort.
The architecture, governance model, evaluation approach and pilot.
The business case, backlog, milestones and KPIs.
In short, this is the SDLC Value-Feasibility-Readiness (VFR) Method: every use case is scored on value, feasibility and readiness before it reaches the roadmap.
Timelines, however, depend on the organization and use case.
Stakeholder workshops, workflow discovery, a use case inventory, a data assessment and a first ranking of the opportunities we find.
Architecture recommendation, technology and vendor evaluation, governance requirements, an evaluation plan, the business case and a defined pilot.
Implementation backlog, integration plan, acceptance criteria, operating model, ownership, measurement framework and a rollout recommendation.
First, we agree the evidence needed to scale, change or stop a pilot before it starts. Consequently, the roadmap never turns into a permanent demo. Background: the enterprise AI roadmap guide .
Deliverables
You get decisions and usable artifacts, not slides about AI. The exact set, however, depends on the engagement.
Candidate use cases scored across business value, user value, technical feasibility, data readiness, effort, and adoption complexity to create a prioritized shortlist for investment.
A review of current data, systems, integrations, security, infrastructure, teams, and governance, highlighting readiness gaps and the priorities to address before AI work begins.
Recommended architecture, major components, and a pilot evaluation plan defining how accuracy, safety, latency, reliability, and operating costs will be measured against agreed criteria.
A phased route from proof of concept to production decision, summarizing key business rationale, expected outcomes, risks, investment needs, and leadership considerations for review.
Consulting findings translated into actionable engineering requirements and prioritized tasks, helping delivery teams begin implementation without repeating discovery or reopening agreed scope discussions unnecessarily.
Tell us what you need to decide, and then we will scope the deliverables to match.
Discuss Your Requirements →Results
3 Projects, Scroll to RevealExplore our AI consulting case studies to see how AI solutions improve workflows, automate processes, and enhance operational efficiency.
Challenge: Manual checks slowed lead review, machine alerts and reporting.
What we did: Assessed workflows and data readiness, then built lead scoring, enquiry routing, machine alerts and manager summaries.
Result: Lead review fell from 20 to 5 minutes. Manager reports dropped from 4 hours to 45 minutes.

Challenge: Over 2,000 invoices a day came in no standard layout. Manual entry took 48 hours, with a 4 to 5% error rate.
What we did: Built an OCR and computer vision platform that reads any layout and syncs clean invoices to the ERP. Only exceptions go to a person.
Result: Processing fell from 48 to 4 hours, errors dropped to 0.1%, and cost per invoice fell from $2.50 to $0.45.

Challenge: Manual inspection of 1,200 painted bodies a day caught only 81% of defects, with 5.2% false positives.
What we did: Built an edge computer vision system trained on 200,000 images across 12 defect types. Pass or fail goes straight to the line PLC.
Result: Accuracy rose from 81% to 96% and false positives fell to 3.0%. Payback took 149 days.

Engagement
Choose the right level of AI consulting based on your goals and project readiness. From initial assessments to roadmap planning and ongoing advisory, get the guidance needed to make informed AI decisions.
Best for: Teams unsure where to start or whether their data can support AI.
You get: Readiness report, use case shortlist and a recommended next step.
Best for: Organizations ready to fund a pilot and needing a plan to justify it.
You get: The full deliverable set: prioritization matrix, architecture recommendation, business case, pilot plan and implementation backlog.
Best for: Teams already building who want an independent view on vendors, evaluation, governance and ROI.
You get: Regular reviews, decision support and access to our consultants.
Industries
The method stays the same, but the constraints, review requirements and sources of value change by sector.
Document processing, ETA and demand prediction, routing and exception workflows across distributed systems.
Visual inspection, predictive maintenance, planning and operational knowledge use cases.
Administrative, documentation, scheduling and imaging workflows, with clinicians keeping authority over decisions. See AI for healthcare.
Fraud, risk, document and service opportunities, handled with attention to sensitive data and model review.
Demand forecasting, personalization, merchandising and customer service.
Why SDLC Corp
Advice that stays accountable for whether the recommendation can actually be built.
We compare cloud AI services, open models and platforms against your workload, data, privacy and cost needs, not a preferred provider.
Our consultants work alongside teams that build and run AI systems, so recommendations reflect production reality. This is also what separates an AI consulting firm from a slide deck.
Every roadmap ends in a backlog your own team, or ours, can start on the next day.
If conventional software or simpler analytics fits better, the roadmap says so. After all, ruling a use case out early costs a fraction of finding out after a build.
Get Started
Tell us where AI could help across your business. We will return a prioritized roadmap and a clear next step.
Talk to Our Experts
Which one you need depends on how much is already decided.
| Option | Choose it when |
|---|---|
| When You Need AI Consulting | The initiative is still a set of questions: what to build, why, where to start, which architecture fits, whether the data is ready and how to measure value. Consulting therefore ends in a roadmap, a business case and a backlog, and covers the full range of AI, including predictive machine learning, computer vision and forecasting. |
| When You Need AI Development | The use case and direction are already clear and you need engineers to build and run the application. Start with our AI development services. |
| When You Need Generative AI Consulting | Your roadmap is limited to foundation models, LLM applications, RAG and agents. See our generative AI consulting services. |
Practical reading on roadmaps, readiness and governance before you scale AI.
All AI Insights →
GuideStructure use cases, guardrails and ROI measurement before you scale AI programs.
Read the roadmap guide →
AssessmentCheck strategy, data, infrastructure, people and governance before scaling AI.
Read the readiness guide →
FrameworkControls, risk tiers and evidence for governing AI across the enterprise.
Read the governance framework →Quick answers to the questions we hear most about AI consulting. Don’t see yours? Reach out and we’ll respond within 24 hours.
AI consulting services help organizations decide where and how to use AI before committing to implementation. Typical work also includes use case discovery, readiness assessment, technology selection, business case development, governance planning and pilot roadmapping.
An AI consultant (or AI consulting firm) reviews your goals, workflows, data, systems and constraints to find viable AI opportunities and recommend the right technique and delivery approach. As a result, the output is a decision your business can act on, not a technology overview.
Typical deliverables include a use case portfolio, prioritization matrix, readiness report, architecture recommendation, technology evaluation, governance requirements, pilot scope, KPI framework, implementation roadmap and an executive business case. The exact set, however, depends on the engagement.
Cost depends on scope: the number of use cases, stakeholders and systems, and the depth of the assessment. For example, a focused readiness sprint costs less than an enterprise roadmap. Contact us for a scoped proposal.
An AI readiness assessment reviews whether you have the business ownership, data, systems, infrastructure, security controls, governance processes and skills the proposed initiative needs. As a result, it tells you what to fix before you build.
Not necessarily. Part of the readiness assessment is finding which data you need, its condition, who owns it and the gaps to close. In fact, learning the data is not ready is a valid and valuable result.
A good AI roadmap lists prioritized use cases, the recommended architecture, governance requirements, an evaluation plan, a business case, a phased pilot plan with acceptance criteria, and, finally, the evidence needed for a go or no-go decision.
We measure the current process baseline first, then define the metric the solution should move, estimate realistic adoption and operating costs, and set a success threshold for the pilot. Otherwise, any claimed improvement is unverifiable.
It depends, however, on the number of use cases, stakeholders and systems. A focused workshop or readiness sprint can set initial priorities in a few weeks, while our enterprise roadmap engagement runs on a 90-day structure.
Hire one when you have several AI ideas but no agreed order, budget or owner, or when a failed pilot has made leadership cautious. A short engagement can then turn those ideas into a ranked, costed plan before you spend on a build.
Transformation consulting covers the operating model, roles, training and adoption that make a working pilot stick. Strategy consulting, in contrast, sets direction and priorities. Full AI consulting includes both, and adds readiness assessment, architecture, vendor evaluation, the business case and pilot planning, so the strategy can be acted on.
Yes. In addition, every roadmap includes the governance requirements for its own use cases. For an organization-wide program with policies, risk tiers and evidence, see our AI governance consulting services.
An AI consulting firm is paid to give an independent recommendation, including advice not to build. A development company is paid to build, so its advice can lean toward a build. However, we do both, which is why the handoff from roadmap to backlog is part of every engagement.
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