AI Readiness Workshop: Find Where AI Fits
Find out exactly where your organization stands on AI maturity, which initiatives will deliver real financial impact, and what needs to happen first.
Everyone Has AI Ideas. Few Know Where to Start.
The hard part isn't finding AI use cases. It's knowing which ones are real, which ones your data can support, and which ones will actually move the financial needle. Without that clarity, teams either stall or invest in the wrong things.
Common Starting Points
We have 15 AI ideas from different departments, but comparing them against each other is practically impossible at our scale.
We tried a chatbot pilot that went nowhere and now leadership is skeptical of any AI investment.
Our ops team spends 40+ hours a week on manual reporting and reconciliation. We know AI could help, but we don't know where to start.
We know our data is a mess, but we don't know how bad or what needs to happen before we can do anything with AI.
What You Walk Away With
Clear View of Where AI Fits
Every AI opportunity scored by business impact, data readiness, and implementation complexity, so you know exactly what to prioritize.
Financial Cases That Hold Up
Each initiative tied to a financial case with baselines, projected improvements, and confidence ratings. Built to survive leadership review.
Honest Assessment of Your Data
A real evaluation of whether your data quality, structure, and infrastructure can support the initiatives you're considering, and what needs to change if not.
A Roadmap You Can Act On
A phased implementation plan with clear priorities, dependencies, gating criteria, and a scope of work for the first phase of execution. Includes a governance recommendation: who in your organization should own AI decisions and how to keep momentum after the workshop ends.
The 3 Stages of the AI Readiness Workshop
We separate diagnosis from prescription. The result is a plan grounded in evidence, not assumptions.
Map the Landscape
We start with a full overview of your entire operation: how work flows through your organization, where data lives, and where manual effort compensates for gaps. This gives us the complete picture before we zoom into any single area.
Process Mapping
End-to-end view of how value moves through your organization
Data Landscape
Where data lives, how it flows, and where it breaks down
Systems Review
Current tools, integrations, and infrastructure capabilities
Stakeholder Interviews
Perspectives from leadership through to frontline teams
Prioritize What Matters
Not every process is ready for AI. We score each area against three criteria: how much value it could unlock, whether the data is ready, and what needs to happen first. Only the highest-potential areas move forward into detailed analysis and business case development.
Value Potential
Estimated financial impact of each opportunity
Data Readiness
Can current data quality support the initiative?
Dependencies
What foundation work needs to come first?
Business Cases
Financial projections for the top-scored initiatives
Build the Roadmap
We translate findings into a concrete transformation plan: what to do in the first 90 days, what to tackle over the next year, and what becomes possible once the foundation is in place. Each phase has clear success criteria before the next one begins.
0 to 3 Months
Quick wins with low dependency and fast payback
3 to 12 Months
Deeper automation and first AI use cases
12 to 36 Months
Advanced AI and new revenue models
Governance Model
Ownership, KPIs, and an operating model for AI
We've Done This Before
AI-Powered Quality and Safety Systems for Steel Production
We built and deployed computer vision systems across ArcelorMittal's steelmaking and logistics operations. These systems guide crane operators handling molten steel, automatically identify railcars, and run in real time in environments with extreme heat, dust, and constant movement.
Results
99%
Detection accuracy in safety-critical positioning
82%
Faster railcar processing (8.5 hrs to 1.5 hrs)
500ms
Real-time response, edge-deployed
What We Built
Crane Operator Guidance
Real-time visual guidance for positioning hooks over molten steel ladles. The system detects ladle position and surrounding infrastructure to help operators work safely and accurately
Automated Railcar Identification
Camera-based system that reads and matches railcar IDs automatically. Cut processing time from 8.5 hours to 1.5 hours with 98%+ recognition accuracy
Edge-Deployed, Production-Grade
Built on PyTorch, OpenCV, and ONNX. Runs on-site at the edge for sub-second response in environments where cloud connectivity isn't reliable
Legacy System Takeover With Zero Documentation
A client purchased a complex accounting and tax calculation system built in Python. No documentation existed, and the original developer was only available as a consultant. Our team needed to understand the entire system, document it, design a new architecture in .NET MVC, and produce estimates for the rebuild.
Results
200 hrs
Total working hours including meetings
150 pages
Client-verified documentation
What We Delivered
Acquisition Risk Eliminated
Full visibility into the purchased system. Every calculation, edge case, and business rule documented and verified before committing to a rebuild
Confident Migration Decision
New .NET MVC architecture designed with accurate scope and cost estimates, giving a clear picture of exactly what the rebuild would take
Months of Work Compressed
LLM-assisted analysis turned what would have been a multi-month reverse engineering effort into a 200-hour engagement
Engineering Team Transformation With Coding Agents
A maritime technology company engaged us for a full AI engineering consultation. We embedded a senior consultant with their team to integrate LLM-assisted coding, automated testing, intelligent code reviews, and agent-augmented CI/CD pipelines across their entire development process.
Results
35%
Shorter lead time to production
45%
Fewer production defects
80%
Of routine code generated by LLMs
Business Impact
Smaller Teams, Same Output
AI-assisted coding and automated review maintained the same feature velocity with leaner teams, directly reducing headcount cost per release
Higher Quality, Lower Rework
45% fewer production defects means less time spent on hotfixes and regression, freeing engineering capacity for new features
Faster Time to Market
35% shorter lead time to production. Features that previously took weeks to ship now reach customers significantly sooner
What Makes This Different
We don't just identify AI opportunities. We build the systems that deliver them.
Business Success Prioritized
Every initiative must translate to financial impact. We measure baselines, project improvements, and assign confidence levels. No initiative survives without a business case.
Audit to Implementation
The team that runs your workshop can build the solutions. We're part of a 6,300-person technology group covering AI, cloud, and data, so nothing gets handed off to a different vendor.
You Get the Real Picture
Most AI pilots never reach production. Gartner projects that organizations will abandon 60% of AI initiatives not backed by production-ready data.1 We tell you which use cases are viable today and which need foundational work first, so you don't invest ahead of your data.
Let's Find Out Where You Stand
A brief intro call is all it takes to scope the engagement. No lengthy procurement process.
