What We Do
AI Data Strategy & Intelligence Consulting
Build the foundation AI actually requires.
Most Organizations Do Not Have an AI Problem First. They Have a Data Problem.
Leadership teams invest in AI tools, run pilots, and commission experimentation — then discover that the underlying data environment cannot support what they are trying to build. Fragmented systems, inconsistent definitions, unresolved ownership gaps, and weak governance do not stop AI from being deployed. They stop AI from being accurate, reliable, or scalable.
The constraint is upstream. Until the data foundation is structured, governed, and connected to real operating workflows, AI performance will be limited regardless of which tools or models you select.
This engagement is designed to resolve that constraint.
Where the Gap Shows Up
Fragmented Systems, No Single Source of Truth
CRM, ERP, finance, and support platforms each hold partial pictures of the business. When those systems are not integrated or reconciled, AI has no reliable base to work from. The output reflects the fragmentation of the input.
Inconsistent Definitions Across Functions
When sales, finance, and operations answer the same question with different numbers, the problem is not reporting. It is that the underlying data does not share common definitions, ownership, or governance. AI amplifies that inconsistency — it does not correct it.
Governance Gaps That Create Compliance and Reliability Risk
Data without defined ownership, access controls, and quality standards is a liability in any AI system that touches customers, finances, or regulated workflows. Governance is not a back-office concern. It is a precondition for deploying AI at scale.
Workflow Visibility That Does Not Exist
Executives cannot act on data they cannot see in the context of actual work. An AI-ready data environment is not just structurally sound — it is connected to the workflows where decisions happen.
What This Engagement Covers
Current-State Assessment
A structured review of your data environment, platform architecture, and integration constraints. Covers the core systems that shape operating visibility — CRM, ERP, finance, support, and workflow tools — and identifies where fragmentation, quality issues, and ownership gaps will limit AI performance.
Priority Domain Definition
Not every data domain needs to be addressed before AI can deliver value. This engagement identifies which data domains are required to support your highest-leverage AI use cases and sequences the work accordingly. The output is a prioritized build order, not a theoretical architecture.
AI-Ready Data Structure Design
A design for the data layer your AI workflows will run on — structured for consistency, accessible to the systems that need it, and governed to the standard your operating and compliance requirements demand. Built around your existing platforms and integration constraints, not against them.
Governance Framework
Ownership assignments, access controls, quality standards, and risk management recommendations for the data domains that matter most. Governance designed to hold up in practice, not just pass an audit.
Executive Alignment
Every decision in this engagement is tied back to the executive priorities, operating metrics, and workflow transformation objectives that motivated the investment. The data foundation is not a technical deliverable. It is a business one.
What You Receive
- •Executive assessment of current-state data readiness — a clear view of where your data environment stands relative to what AI deployment requires
- •Structural constraint analysis — the specific fragmentation, quality, and governance issues that are limiting workflow automation, agent reliability, and decision quality
- •Prioritized roadmap — a sequenced plan for building an AI-ready data layer across the most important business domains, with ownership and timeline guidance
- •Governance recommendations — covering data ownership, access controls, quality standards, and risk management across priority domains
- •Board-ready summary — findings, implications, and recommended next-step investments framed for leadership and board-level review
What Executives Are Actually Buying
This is not a data architecture project. It is not a governance framework exercise.
What AI Data Strategy & Intelligence Consulting delivers to executive leadership is:
- •AI that works as expected — models and agents operating on consistent, governed data rather than producing outputs that reflect the inconsistency of the environment they run in
- •Decision quality that holds up — executives and operators making calls on a single, reconciled picture of the business rather than competing versions of it
- •Workflow automation that scales — the data foundation that allows AI-assisted workflows to expand across the organization without breaking as they grow
- •Compliance and risk posture — governance documentation and access controls that satisfy audit requirements and reduce the risk surface of AI deployments touching regulated data
- •A foundation the rest of the roadmap depends on — every subsequent AI initiative runs faster, cleaner, and more reliably when this layer is in place
The organizations that skip this work do not avoid the cost. They pay it later, in failed pilots, unreliable outputs, and AI investments that cannot scale past the initial experiment.
Why This Comes Before the Other Investments
AI tools are not the constraint for most mid-market organizations. The constraint is the environment those tools run in. A capable model operating on fragmented, inconsistently defined, ungoverned data will produce fragmented, inconsistent, ungoverned outputs.
The sequence matters: data foundation first, then workflow automation, then agents and continuous intelligence. Organizations that build in that order move faster and spend less on remediation. Organizations that skip ahead spend significantly more correcting the problems this engagement is designed to prevent.
RMG's Databricks and Snowflake partner relationships mean this work is grounded in the platforms that handle enterprise data at scale — not a theoretical architecture that requires rearchitecting what you already have.
Databricks
Interactive overview of Databricks for data pipeline ingestion—including how capabilities compare with Snowflake for ingestion-oriented workloads.
Learn more about DatabricksSnowflake Data Cloud
Interactive overview of Snowflake Data Cloud—architecture themes, ingestion, and AI-related capabilities.
Learn more about SnowflakeExecutive FAQ
Common questions from executive leadership.
How long does this engagement take?
The audit and go-forward recommendation phase typically runs six to ten weeks depending on the complexity of your data environment and the number of systems in scope. The output is a complete readiness assessment and roadmap, not an open-ended consulting engagement.
Do we need to have a specific data platform already in place?
No. This engagement assesses what you have and recommends a path forward based on your existing infrastructure, integration constraints, and business priorities. If your roadmap points toward Snowflake or Databricks, RMG's partner relationships in both platforms inform that work directly.
How does this connect to the other RMG engagement tracks?
AI Data Strategy & Intelligence is typically the first step for organizations where data readiness is the binding constraint. The output feeds directly into Forward Deployed Engineer (FDE) work, where embedded execution turns the foundation into day-to-day operating practice. AI Software Development engagements can also build on this foundation when custom systems are required.
What if we have some data domains in good shape and others that are not?
The engagement scopes to your actual situation. If three of your six core systems are well-governed and two are not, the work focuses where the constraint is. You are not paying to document what is already working.
The first step is a confidential discovery conversation.
We will assess where your data environment stands today and identify the constraints that are most likely to limit your AI investments.
Assess Your AI Operating Maturity