RubinBrown’s Approach to AI Strategy

RubinBrown’s AI and Data practice is built on a single conviction: organizational capacity is the primary constraint on AI value. When AI initiatives stall, the cause is almost never the model or technology itself. The culprits are unclear ownership, misaligned leadership, or workflows that were never designed to absorb AI-generated insights. Our AI consultants are trained to identify and address these constraints first.

We bring something unusual to this work: deep AI and data expertise alongside audit, assurance, and risk capabilities. For organizations navigating the governance and compliance dimensions of AI adoption, which is most organizations, that combination matters. It means your AI program is built to hold up under scrutiny, not just perform in a demo.

Our work is structured around ASPIRE-X, RubinBrown's proprietary AI readiness and implementation framework. ASPIRE-X sequences every engagement around the factors that allow AI to deliver ROI: strategy alignment, process integration, data foundations, governance, and change adoption. The result is a practical and clear path forward.

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AI and Data Solutions Practice Overview

ASPIRE-X: Our Proprietary AI Readiness Framework

ASPIRE-X is the diagnostic and delivery framework behind every RubinBrown AI engagement. It was developed from more than 100 AI and data transformation projects and reflects a hard-won truth: the organizations that succeed with AI aren't the ones that move fastest. They're the ones who know where they stand with AI before they move.

ASPIRE-X evaluates six dimensions of readiness (strategy, process, data infrastructure, risk governance, execution capability, and organizational change) and produces a sequenced implementation roadmap tied to your specific constraints, opportunities, and risk profile.

AI Strategy Consulting Focused on Value

Before recommending any AI solution, we believe every organization deserves an honest answer to a harder question: Are you actually ready to use it?

That answer depends on more than technology. AI readiness requires clear strategic objectives, data that is accurate and accessible, processes that can absorb AI-generated insight, and people who know how to act on it. Most readiness assessments treat these as a checklist. ASPIRE-X treats each as an interdependent system because a weakness in any one area can undermine the entire initiative.

Prioritize High-impact AI Initiatives

Our strategy work spans opportunity assessment, investment case development, and operating model design. We help organizations identify high-impact AI opportunities and sequence them through a structured, value-centric lens prioritized by feasibility, risk tolerance, business value, and adoption complexity. Not experimentation for its own sake. We also help leadership align around a shared AI vocabulary and decision framework, so that investment decisions are made deliberately rather than reactively.

The output is a practical AI roadmap that gives leadership clarity, reduces adoption risk, and avoids premature technology investment. Organizations that skip this step consistently spend more and deliver less.

A Good AI Strategy Starts with Accurate Data

AI is as strong as the data beneath it, and that data often starts with ERP.

This is the most underappreciated constraint in AI adoption. Organizations focus on models, tools, and vendors while the actual binding constraint sits one layer down: the quality, structure, and governance of the data those systems will learn from. ERP systems are often where that foundation either holds together or doesn't. ERP systems maintain master data integrity, record every transaction and operational event, and define the source of truth for the business. Clean inputs produce trustworthy AI outputs. Broken data chains produce confidently wrong ones.

RubinBrown Makes Your Data Reliable for AI Applications

When data is trustworthy, AI insight becomes actionable. When it isn't, even the best models produce outputs that erode confidence rather than build it. RubinBrown's data strategy work addresses this directly. We begin with a data maturity assessment that evaluates how data is created, governed, and consumed across the organization, including the ERP and source systems that anchor it. We evaluate data quality across the four dimensions that matter for AI reliability:

• accuracy
• completeness
• consistency
• timeliness
• lineage
• accessibility

We help organizations establish governance framework design, data ownership, quality standards, and the routines that sustain improvements over time. Rather than pursuing perfection before launching anything, we focus on creating fit-for-purpose data foundations that support specific use cases and improve continuously as AI capabilities mature.

Connect Data, Systems, and AI with the Right Architecture

A well-designed AI strategy and clean data environment still fail if the architecture connecting them is wrong.

RubinBrown's integration design work addresses the connective tissue between systems; the layer that structures and governs data pipelines, resolves duplicates and quality gaps, and builds reliable pathways between ERP, source systems, and AI applications. Without this layer, AI has nothing to learn from and nowhere reliable to deliver outputs.

Our integration work includes:

• system landscape assessment
• target architecture design
• vendor and platform evaluation
• build vs. buy analysis including for agentic AI systems

Agentic AI operates with greater autonomy, executes multi-step tasks, and interacts with external systems in ways that require more deliberate design around data access, human oversight, and failure handling. We help organizations think through these tradeoffs before they commit to a direction.

AI Implementation

Turning strategy into real-world value requires implementation discipline that is often underestimated.

We begin by confirming how and where AI outputs will actually be used because AI embedded in a workflow delivers fundamentally different outcomes than AI sitting in a dashboard no one opens. Our focus is on connecting AI into the platforms and processes teams already rely on: ERP systems, financial applications, document workflows, and line-of-business tools. We do not build disconnected tools that require separate maintenance and separate adoption.

RubinBrown works across a range of proven AI use cases that deliver practical business value:

• Document intelligence automates invoice, contract, and form processing.
• Conversational and generative AI improve internal support and knowledge access.
• Predictive analytics and intelligent automation help anticipate demand and performance risk while reducing manual burden in finance and administrative operations.
• Agentic AI solutions, systems capable of executing complex, multi-step tasks with appropriate human oversight built in, for organizations whose use cases have matured beyond conventional automation.

In each case, implementation is designed with integration in mind. AI outputs connect directly into systems of record that teams already trust.
 

Executive AI Education Leads to Better Strategic Decisions

AI adoption doesn't fail only at the technical level. It fails when leadership doesn't share a common understanding of what AI can do, what it can't, and how to make good decisions about it.

RubinBrown designs and delivers executive AI education programs for senior leadership teams typically structured across three to four sessions and covering AI strategy fundamentals, use case prioritization, governance, and responsible adoption. The goal is practical: equip your executive team with a shared AI vocabulary and a decision framework they can apply immediately to organizational investment decisions.

This work is particularly valuable ahead of a broader AI initiative, where leadership alignment is a prerequisite for everything that follows. It is also useful for boards and executive teams who need to evaluate AI proposals, vendor claims, and internal recommendations with greater confidence and skepticism.

AI Governance, Risk, and Compliance

Most organizations don’t fail at AI because of the technology. They fail because no one defined who was accountable, what guardrails applied, or how to detect when something went wrong.

Good AI governance is not bureaucracy. It is the mechanism that allows AI adoption to accelerate safely. RubinBrown helps organizations build practical governance frameworks that manage risk without grinding innovation to a halt clarifying approval processes, risk ownership, and alignment with organizational values from the start.

Risk and compliance are considered across the full AI lifecycle. We help organizations identify where AI influences material decisions and put appropriate safeguards in place: human review requirements, transparency standards, and clear boundaries for use. We align AI initiatives to regulatory and industry expectations without adding complexity the organization isn't yet ready to absorb. AI Governance for Long-Term Reliability

Responsible AI adoption doesn't end at deployment. It requires ongoing monitoring watching for model drift, output bias, and unexpected behavior. We establish clear metrics and escalation processes, so issues are identified early, before they create downstream problems. Our crossover capability in audit, assurance, and risk is directly relevant here. We know what scrutiny looks like from the other side of the table, and we help clients build AI programs that are defensible, not just functional.

AI for Private Equity Firms and Portfolio Companies

Private equity portfolio companies face a different version of the AI opportunity and a different version of the risk.

The timeline is compressed. The value creation mandate is explicit. Data environments are often fragmented, especially post-acquisition. And the typical portfolio company lacks the internal AI expertise to distinguish a good implementation from an expensive distraction.

AI Strategy and Execution Across the Private Equity Lifecycle

RubinBrown brings deep PE-specific experience to this environment. Our team has supported more than 100 value creation engagements and has transaction experience exceeding $26 billion in aggregate deal value. We understand how PE-backed portfolio companies are structured, what the reporting cadence looks like, and what a management team implementing a value creation plan actually needs from an AI initiative: fast time-to-value, defensible ROI, and integration that doesn't require rebuilding the technology stack.

We support portfolio companies across the full lifecycle, including:

• Pre-acquisition diligence focused on data and AI readiness
• Post-acquisition integration and implementation
• Exit readiness reporting and value validation

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Industry‑Specific Data & AI Solutions

AI is most effective when it reflects how an industry actually operates. Generic implementations fail not because the technology is wrong, but because the use cases, constraints, and data structures are different in every sector. RubinBrown designs solutions that fit into real workflows, existing systems, and applicable regulatory requirements across industries:

• Manufacturing and distribution: AI is no longer optional. 80% of manufacturers say it will be essential to growing or maintaining their business by 2030, yet 65% report they lack the right data for AI applications and 62% cite unstructured or poorly formatted data as a primary barrier. (Manufacturing Leadership Council) Common use cases include supply chain visibility, demand forecasting, predictive maintenance, and quality control. The gap between where manufacturers are headed and what their data environment can currently support is where advisory work creates the most value.
• Construction and engineering: 87% of U.S. contractors expect AI to transform their businesses, yet only 19% have adapted their workflows to incorporate it. (Dodge Construction Network) Common use cases include project scheduling, contract and document analysis, cost tracking, and risk identification. The firms closing that gap are doing so by turning unstructured information, spread across contracts, field reports, schedules, and financial systems, into actionable insight before problems become cost overruns.
• Healthcare: 85% of healthcare leaders are already exploring or implementing gen AI, and of those who have deployed use cases, 64% have quantified or anticipate positive ROI. (McKinsey) The highest-value applications are revenue cycle improvement, clinical documentation, and compliance monitoring. Healthcare's unique regulatory, privacy, and ethical requirements mean governance and data protection must be built in from the start, not added after deployment.
• Financial services: 71% of firms now formally use AI, with compliance and risk management ranking as the second most common use case, yet only 24% have policies governing third-party AI use and fewer than 30% have implemented basic technical controls. (ACA/NSCP) That governance gap is precisely where RubinBrown's combination of AI advisory and audit/assurance expertise is a direct advantage.

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