AI Solutions & Strategy: From Use Case to Costed Roadmap
Most AI initiatives stall because teams build before they decide what is worth building. Opsio's AI Solutions & Strategy engagement fixes that order. We identify the use cases that move your numbers, test your data and platform readiness, model the business case, and hand you a costed, governed roadmap your teams can execute with confidence.
Trusted by 100+ organisations across 6 countries
2-week
Discovery to prioritized roadmap
Multi-cloud
AWS, Azure & Google Cloud
50+
Cloud & AI engineers
ISO 27001
Certified delivery
What's Included
An Opsio AI strategy engagement is deliberately broad at the front and sharp at the exit. We open wide enough to catch the opportunities hiding outside the team that asked for AI, then narrow relentlessly until what remains is fundable and sequenced. Each capability below maps to a concrete artifact you keep: a use-case backlog, a readiness scorecard, a business-case model, a build-versus-buy verdict, a vendor recommendation, and a governed roadmap. Nothing is theoretical. We work alongside your domain owners, data teams, and platform engineers so the conclusions hold up when they reach the people who have to deliver and the people who have to pay. The six capabilities combine into a single decision package your leadership can approve and your delivery teams can start on.
AI Opportunity Discovery
We run structured workshops across your business units to surface candidate use cases, then filter them against value, feasibility, and data availability so only credible opportunities advance to scoring.
Use-Case Prioritization
Each candidate is scored on business impact, effort, risk, and data readiness, producing a ranked backlog that tells you exactly what to fund first, what to stage, and what to drop.
Data & AI Readiness Assessment
We assess data quality, access, lineage, and platform maturity across your AWS, Azure, and GCP estate, then quantify the groundwork each prioritized use case requires before delivery.
ROI & Business-Case Modeling
We model build cost, run cost, and value range for every shortlisted use case, comparing each against the status quo so funding decisions rest on numbers rather than enthusiasm.
Build-vs-Buy & Vendor Selection
We recommend build, buy, or hybrid for each use case and select models across Bedrock, Azure OpenAI, and Vertex AI on cost, latency, accuracy, and data-residency, with trade-offs documented.
Responsible-AI Governance Design
We map each use case to its risk tier under NIST AI RMF and EU AI Act guidance, then scope the oversight, evaluation, and monitoring controls into the roadmap from the start.
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- Certified cloud engineers
Opsio's focus on security in the architecture setup is crucial for us. By blending innovation, agility, and a stable managed cloud service, they provided us with the foundation we needed to further develop our business. We are grateful for our IT partner, Opsio.
Jenny Boman
CIO · Opus Bilprovning
Turn AI ambition into a decision you can fund
Boards have approved AI budgets, but the spend rarely lands where it should. Pilots multiply, vendors pitch, and proofs-of-concept pile up without a clear line to revenue, cost, or risk. Opsio's AI Solutions & Strategy practice exists to impose order before capital is committed. We start with the business outcomes you are accountable for, then work backward to the AI use cases that actually influence them. The result is not a slide deck of possibilities but a ranked, evidence-backed shortlist, each item attached to an owner, a data dependency, a delivery path, and a number. That shortlist becomes the spine of everything that follows, from your first production model to the managed service that keeps it running. The hardest part of an AI program is rarely the model. It is the data underneath it, the integration surface around it, and the governance that has to wrap it before legal and risk will sign off. Our assessment looks at all three in parallel. We map where your usable data lives, how clean and accessible it is, and what it would take to feed a retrieval or fine-tuning workload safely. We examine your cloud estate across AWS, Azure, and Google Cloud to see what you can reuse and what you must stand up. And we surface the governance gaps early, so a promising use case does not collapse at the compliance gate six months later.
Every use case we recommend carries a business case, not a hunch. We model the cost to build, the cost to run, and the realistic value range, then compare that against the cost of doing nothing. Where the honest answer is buy rather than build, we say so. Where an off-the-shelf assistant beats a custom model, we point you to it. This discipline protects your budget from the most common failure mode in enterprise AI, which is funding interesting work that never reaches payback. You leave each engagement knowing not just what is possible, but what is worth your money this year and what should wait.
Vendor and model selection is its own strategic question, and the market shifts quarterly. We help you choose between foundation models served through Amazon Bedrock, Azure OpenAI, and Google Vertex AI based on your latency, cost, data-residency, and accuracy needs, rather than on whoever marketed hardest. For retrieval-heavy workloads we design the RAG architecture; for narrow, repeatable tasks we may recommend a smaller tuned model that costs a fraction to run. The selection is documented with the trade-offs visible, so when a better option appears next quarter you can swap with eyes open instead of relitigating the whole decision.
Responsible AI is treated as a design input here, not a compliance afterthought. We align your governance to recognized frameworks such as the NIST AI Risk Management Framework and the obligations emerging under the EU AI Act, mapping each prioritized use case to its risk tier and the controls it needs. Human oversight, evaluation, audit logging, and model monitoring are scoped into the roadmap from day one. The outcome is an AI program your risk, legal, and security teams can defend, delivered by an ISO 27001-certified partner with a 24/7 NOC and a 99.9% uptime SLA standing behind it. Featured reading from our knowledge base: Cloud Migration Strategy Lift and Shift: Seamless Transition Solutions, and Case Study: Ford AI Vehicle Defect Detection Solutions. Related Opsio services: AI Agent Services.
How Opsio Compares
| Dimension | Opsio AI Strategy | Generalist Consultancy | In-House Only |
|---|---|---|---|
| Tied to real delivery economics | Yes, we build what we recommend | Rarely, advice only | Yes, but limited scope |
| Multi-cloud model selection | AWS, Azure, GCP, vendor-neutral | Often single-vendor aligned | Limited by existing skills |
| Time to costed roadmap | About 2 weeks | Often 8-12 weeks | Variable, often slow |
| Build-vs-buy honesty | Recommends buy when buy wins | May favor billable builds | Bias toward building in-house |
| Governance built in | NIST AI RMF & EU AI Act mapped | Add-on or separate workstream | Often retrofitted late |
| Path to production & operations | Same team, 24/7 NOC, 99.9% SLA | Hand off to another firm | Depends on internal capacity |
Ready to get started?
What You Get
Pricing & Investment Tiers
Transparent pricing. No hidden fees. Scope-based quotes.
Focused Discovery
$8,000–$15,000
Single business unit, one cloud, prioritized use-case backlog and roadmap. Illustrative; varies by scope.
Enterprise Assessment
$15,000–$30,000
Multi-domain discovery, readiness scorecard, business cases, and governance plan. Illustrative; varies by scope.
Strategic Program
$30,000–$50,000
Multi-cloud estate, multiple business units, deep governance and delivery planning. Illustrative; varies by scope.
Transparent pricing. No hidden fees. Scope-based quotes.
Questions about pricing? Let's discuss your specific requirements.
Why Choose Opsio for Cloud Services
Plenty of firms will write you an AI strategy. Few can build and operate what the strategy recommends. Opsio sits on both sides of that line: we are an AWS Advanced Tier Services Partner with AWS Migration & Modernization Competency, a Microsoft Partner, and a Google Cloud Partner, with 50+ engineers who ship production workloads every week. That means our advice is shaped by what we actually deliver, not by a slide library. When we say a use case is buildable in a quarter, we know because we build that class of system. When we cost a roadmap, the numbers come from real delivery, and the team that wrote your strategy can take it all the way to a managed, monitored, supported production service.
Strategy Backed by Delivery
Our advisors also ship production AI, so recommendations reflect real build and run costs, not theoretical estimates from a consulting playbook.
True Multi-Cloud
Certified across AWS, Azure, and Google Cloud, we recommend the right platform and model for each use case without a single-vendor bias.
One Partner, End to End
The same team can move from strategy to development to managed operations, removing the costly handoffs between advisory and delivery firms.
Security and Compliance First
ISO 27001-certified delivery and governance aligned to NIST AI RMF and EU AI Act mean your risk teams are engaged from day one.
Operations You Can Trust
A 24/7 NOC and 99.9% uptime SLA stand behind everything we deliver, so AI you fund stays available once it is live.
Honest Build-vs-Buy
We recommend buying when buying wins. Protecting your budget from low-payback custom builds is part of the engagement, not an upsell risk.
Not sure yet? Start with a pilot.
Begin with a focused 2-week assessment. See real results before committing to a full engagement. If you proceed, the pilot cost is credited toward your project.
Our 4-Phase Delivery Process
Our engagement is fixed-scope and fast, designed to give leadership a fundable decision in weeks rather than quarters. We run it as a tight collaboration with your business owners, data teams, and platform engineers, because a strategy built in isolation does not survive contact with delivery. Four steps take you from a broad opportunity scan to a costed, governed roadmap you can approve and start on. Each step closes with a concrete artifact you keep, so value accrues even if you pause between phases.
Discover & Frame
We align on the business outcomes you own, run cross-functional workshops to surface candidate use cases, and frame the opportunity space. Timeline: days 1-3.
Assess Readiness
We evaluate data quality, access, and platform maturity across your cloud estate, scoring what each candidate use case needs to become deliverable. Timeline: days 4-6.
Model & Prioritize
We build business cases, settle build-vs-buy and vendor questions, and rank the backlog by value, effort, and risk into a fundable shortlist. Timeline: days 7-9.
Roadmap & Govern
We sequence the shortlist into a costed roadmap with governance controls and a delivery plan your teams can execute. Timeline: days 10-14.
Key Takeaways
- AI Opportunity Discovery
- Use-Case Prioritization
- Data & AI Readiness Assessment
- ROI & Business-Case Modeling
- Build-vs-Buy & Vendor Selection
Industries Served by Opsio
AI value looks different in every sector, so we tune discovery and prioritization to the patterns that matter in yours. The use cases that survive scoring in a bank are not the ones that survive in a manufacturer, and the governance bar differs sharply between them. We bring sector-aware judgment to the engagement while keeping the underlying discipline, readiness, business case, build-vs-buy, governance, identical, so the roadmap you receive is grounded in both your industry and your specific data and cloud reality.
Financial Services
Fraud scoring, document automation, and customer-facing assistants prioritized against strict auditability, model-risk, and regulatory-oversight requirements.
Manufacturing & Industrial
Predictive maintenance, quality inspection, and supply-chain forecasting use cases scored against OT data readiness and integration constraints.
Retail & E-commerce
Personalization, demand forecasting, and support automation prioritized where data volume and margin impact justify build or platform investment.
SaaS & Technology
Product AI features, RAG-based assistants, and internal automation scored for time-to-value and the build-vs-buy trade-offs that protect engineering capacity.
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AI Solutions & Strategy: From Use Case to Costed Roadmap — FAQ
What is AI strategy consulting?
AI strategy consulting is advisory work that decides where and how an organization should apply artificial intelligence before any building begins. Rather than starting with a model or a tool, it starts with business outcomes and works backward to the use cases that influence them. A proper engagement covers opportunity discovery, use-case prioritization, data and platform readiness assessment, ROI and business-case modeling, build-versus-buy decisions, vendor and model selection, and responsible-AI governance. The output is a costed, sequenced roadmap that leadership can fund and delivery teams can execute. Opsio's version is deliberately tied to delivery, so the strategy reflects real build and run economics rather than theoretical estimates, and the same team can take it through to production.
How much does an AI strategy engagement cost?
Opsio runs AI Solutions & Strategy as a fixed-fee engagement, with illustrative ranges from roughly $8,000 to $50,000 depending on scope. A focused discovery and roadmap for a single business unit sits at the lower end. A multi-domain assessment spanning several business units, a complex multi-cloud estate, and deep governance work sits higher. The fee is fixed and agreed before we start, so there is no open-ended billing. Because we are a delivery firm as well as an advisor, the business cases we produce use real build and run costs, which means the engagement typically pays for itself by steering budget away from low-payback initiatives. All ranges vary by scope and are confirmed in a short scoping call before any commitment.
How long does an AI strategy engagement take?
A standard engagement runs about two weeks from kickoff to a costed, governed roadmap. The first few days cover discovery and framing, the next assess data and platform readiness, then we model business cases and prioritize, and the final days produce the roadmap and governance plan. Larger programs spanning many business units or a complex multi-cloud estate can extend beyond two weeks, and we agree the timeline during scoping. We keep the engagement deliberately short because leadership needs a fundable decision quickly, not a quarter-long study. Each phase closes with a concrete artifact you keep, so you retain value even if you choose to pause between phases rather than running straight through.
Should we build a custom AI model or buy an existing solution?
It depends on the use case, and deciding honestly is a core part of our engagement. Buying or adopting an off-the-shelf assistant usually wins when your need is common, time-to-value matters, and no proprietary data advantage exists. Building, or fine-tuning a model served through Bedrock, Azure OpenAI, or Vertex AI, wins when the use case is differentiating, sensitive to data residency, or cost-prohibitive to run on a general-purpose API at scale. Many cases land in between, where a retrieval-augmented architecture over a foundation model gives you customization without the cost of training. We give each prioritized use case a clear build, buy, or hybrid verdict with the trade-offs documented, so the decision is defensible and revisitable.
How do you calculate ROI for AI initiatives?
We model three things for every shortlisted use case: the cost to build, the cost to run, and a realistic value range, then compare all three against the cost of continuing as you are today. Build cost draws on our real delivery experience. Run cost includes inference, infrastructure, and the operational support to keep the system healthy. Value is expressed as a range rather than a single optimistic figure, grounded in the specific process or revenue line the use case touches. We deliberately avoid headline percentages, because every environment is different and inflated numbers erode trust. The result lets you compare use cases on a consistent basis and fund the ones with credible payback first.
How do you handle responsible-AI and governance?
Governance is a design input in our engagement, not a box ticked at the end. We map each prioritized use case to its risk tier using recognized frameworks, principally the NIST AI Risk Management Framework and the obligations emerging under the EU AI Act, then scope the controls each tier requires: human oversight, evaluation and testing, audit logging, data-handling rules, and ongoing model monitoring. Those controls are written into the roadmap so they are funded and scheduled rather than retrofitted under pressure. As an ISO 27001-certified delivery partner, we treat security and compliance as foundational. The aim is an AI program your risk, legal, and security teams can defend from the first deployment onward.
Which cloud and AI platforms do you work with?
Opsio is certified across all three major clouds: an AWS Advanced Tier Services Partner with AWS Migration & Modernization Competency, a Microsoft Partner, and a Google Cloud Partner. For AI workloads we deploy through Amazon Bedrock, Azure OpenAI, and Google Vertex AI, choosing the platform and model per use case based on cost, latency, accuracy, and data-residency needs rather than vendor preference. This multi-cloud stance matters during strategy because the right model for a customer-facing assistant may differ from the right model for an internal document-processing pipeline. We document the selection with trade-offs visible, so when a stronger option appears you can swap deliberately rather than reopening the entire decision.
What do we actually receive at the end?
You receive a complete decision package, not a single slide deck. It includes a prioritized AI use-case backlog, a data and AI readiness scorecard, business-case models for each shortlisted use case, build-versus-buy verdicts, a model and vendor recommendation, a responsible-AI governance plan mapped to risk tiers, and a costed, sequenced AI roadmap with a delivery plan your teams can act on. Each artifact stands on its own, so the work retains value even if priorities shift. Because Opsio delivers and operates AI as well as advising on it, the same team can carry the roadmap forward into development and a managed, monitored production service, removing the handoff that usually stalls momentum after strategy.
What does an AI consultant do?
An Opsio AI consultant helps you turn AI from ambition into a costed plan. We identify and prioritise high-value use cases, assess your data and AI maturity, model the ROI, and advise on build-vs-buy and the right model and platform. We also set up responsible-AI governance aligned with GDPR and the EU AI Act, then deliver a clear roadmap with effort and cost. The same architects who advise you also build and run the solution, so the strategy stays grounded in delivery.
Still have questions? Our team is ready to help.
Decide what AI is worth building
Book a scoping call and get a costed, governed AI roadmap in about two weeks, backed by a team that builds and operates what it recommends.
AI Solutions & Strategy: From Use Case to Costed Roadmap
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