Assess, Discover, Operationalize
Sound AI decisions begin with a clear view of the estate.
AI is entering organizations through embedded software, enterprise platforms, departmental purchases, vendor solutions, employee-selected tools, and custom development. The result can be an AI estate fragmented across functions, systems, and decision processes.
Leadership cannot govern an AI estate it cannot clearly see.
Nous Altus helps leaders establish the visibility and decision clarity needed to determine what requires attention, where accountability sits, and what should happen next.
Diagnostic Conversation
Paid diagnostic · Written recommendation within two business daysThe first step is a short, focused conversation to determine what problem actually needs to be solved before either of us commits to a larger engagement.
We talk through what is prompting the question, what you already know about the AI in use, where visibility or accountability may be unclear, and what decision or concern is driving the need. That might be a board question, a customer or regulatory request, a new AI initiative, or simply recognition that AI adoption has moved faster than the organization’s ability to see it clearly.
I listen for where greater clarity is needed and whether AI Estate Discovery is the right next step. Within two business days, you receive a written recommendation with one of three conclusions:
The written recommendation reaches one of three conclusions:
- Proceed. A discovery is warranted. The recommendation identifies the proposed scope and what the assessment should establish.
- Pause. Something needs attention first. The recommendation identifies what should be resolved before a discovery would be useful.
- Reframe. The underlying issue is different from the one that prompted the conversation. The recommendation identifies the more useful question to address.
My role is to provide an independent view of what should happen next, and whether or not we continue working together.
AI Estate Discovery
Two-week engagement · AI estate baseline and written findingsAI Estate Discovery examines a defined business scope to establish a clear, accountable view of the AI capabilities already in use.
The engagement works across multiple discovery paths, including existing applications, enterprise platforms, vendor relationships, technology records, stakeholder knowledge, and structured intake. The goal is not simply to create a list, but to establish the factual baseline leadership needs for sound decisions and ongoing governance.
How the two weeks run
We begin with one clearly defined part of the organization, typically a business function or other bounded area where AI activity is meaningful and leadership needs greater visibility.
AI capabilities are identified across the relevant paths through which they may have entered or evolved, including embedded software features, enterprise platforms, departmental purchases, vendor solutions, employee-selected tools, custom development, and automated workflows.
For each capability, the assessment documents what it is used for, who owns it, what it depends on, where it sits in its lifecycle, and what validation, review, or governance evidence is available.
The assessment identifies unclear ownership, missing evidence, incomplete governance coverage, unresolved questions, duplication, and capabilities that may require additional specialist review or leadership attention.
The findings are assembled into a single, leadership-ready view that can support decisions about ownership, review, prioritization, remediation, consolidation, investment, and ongoing oversight.
You receive
- AI Estate Baseline
A structured, client-owned inventory of the AI capabilities identified within the agreed scope. - AI Estate Discovery Findings
A concise written assessment of the most important ownership, evidence, governance, and decision gaps revealed by the discovery. - Governance Action View
A clear record of unresolved items, accountable owners, required reviews, next actions, and matters requiring leadership attention.
A sample of what the baseline captures
The full baseline captures additional information including technology ownership, data and system dependencies, validation evidence, specialist reviews, unresolved questions, and required actions.
What this is
- A clear baseline of the AI capabilities within the agreed scope
- A structured view of ownership, purpose, dependencies, lifecycle, and available governance evidence
- A way to identify what requires further review, action, or leadership attention
- A foundation for ongoing AI portfolio governance
What this is not
- A cybersecurity, privacy, legal, compliance, or technical validation assessment
- A substitute for specialist judgment
- A penetration test or security audit
- A technical remediation plan or AI implementation engagement
- A policy-writing or technology-selection exercise
AI Portfolio Governance Operating Model
Scoped-based engagement · Operating model and governance frameworkAI Estate Discovery establishes what exists and what requires attention. The operating model defines how the organization will govern that estate going forward.
Working from the existing organizational structure, governance functions, and AI portfolio, Nous Altus designs the practical mechanisms that connect discovery, ownership, specialist review, decisions, actions, and leadership oversight.
What the operating model establishes
Define how new and existing AI capabilities are identified, submitted, documented, and brought into the portfolio, including capabilities that enter through different business and technology paths.
Clarify business and technology ownership, governance responsibilities, decision rights, and the roles of functions such as Cybersecurity, Privacy, Legal, Compliance, Data Governance, Procurement, and technical specialists.
Establish the workflow for determining what requires specialist review, routing questions to the appropriate functions, capturing decisions and evidence, and tracking unresolved actions through completion.
Define lifecycle checkpoints, review rhythms, ownership updates, monitoring expectations, and the processes needed to keep the AI estate from becoming opaque again.
Establish the portfolio views, governance indicators, reporting cadence, and escalation mechanisms leadership needs to see where attention, decisions, or intervention are required.
You receive
- AI Portfolio Governance Operating Model
A documented model defining governance roles, responsibilities, decision rights, workflows, review paths, and operating rhythms. - Governance Workflow and Routing Model
A practical process showing how AI capabilities move from discovery and intake through review, decision, action, and ongoing oversight. - Leadership Oversight Framework
Defined reporting, governance indicators, review cadence, and escalation criteria for maintaining a decision-ready view of the AI portfolio.
What this is
- A practical operating structure for enterprise AI portfolio governance
- A way to connect business ownership with specialist governance functions
- A repeatable process for intake, review, decisions, actions, and oversight
- A foundation for sustaining AI portfolio visibility and accountability as adoption grows
What this is not
- A replacement for Cybersecurity, Privacy, Legal, Compliance, Ethics, or technical specialists
- A substitute for specialist judgment
- A cybersecurity or technical-control implementation
- Legal or regulatory advice
- AI product development or model implementation
Determined by the scope of the organization, existing portfolio governance capabilities, and the level of support required to establish the operating model.
Let’s think through it together.
If any of this resonates with where you are right now, reach out. There’s no pitch, just a straightforward conversation about where you are and whether working together makes sense.
hello@nousaltus.com