A small number of cases, published on purpose.
Datum North publishes selected anonymized cases — enough to show how its principals work a consequential decision, never enough to manufacture an impression of scale. Every case is classified under the firm's evidence policy, and every published claim is backed by a private record that can be examined during diligence.
The evidence policy
Each case carries one of four classifications, stated plainly.
Verified Datum North engagement. An assignment contracted and performed by the firm, described with its exact role and supported by an internal engagement record.
Representative principal experience. An operating or advisory responsibility completed by a current managing principal before joining the firm — identified as exactly that, never as a Datum North client engagement.
Composite case. A synthesis of recurring patterns from more than one experience, labeled as a composite and never used to claim a specific client result.
Illustrative scenario. A description of how the firm would approach a class of problem, labeled as illustrative and never presented as completed work.
Public descriptions avoid client-identifying combinations of detail, distinguish advisory work from client or provider implementation, and use outcome language only where the result can be documented or privately confirmed. The firm does not publish fabricated testimonials, unsupported financial outcomes, or claims of implementation work it did not perform.
The cases below are representative principal experience: assignments led by people who now constitute the firm, not engagements of the firm itself. That distinction is the policy working, not a caveat.
Representative principal experience
Reclaiming decision rights in a regulated AI workflow
Delegated judgment, accountable ownership, and enforceable intervention rights
Situation. A regulated institution was using machine-learning and generative-AI components in a high-volume customer decision process. The workflow combined internal data, external models, and formal human approval.
Scale. A decisioning environment influencing or executing approximately 90,000 customer decisions each month across four product lines.
The decision. Whether to expand automated decision support into additional products and customer segments without weakening accountability — or creating actions that could not be adequately reviewed or reversed.
What made it difficult. The institution retained formal human sign-off, but much of the practical judgment had already moved into model outputs, prompts, ranking logic, and vendor-controlled updates. Existing governance tracked applications and models, not the decisions being delegated. Senior leaders could not clearly state which actions were advisory, which were effectively determinative, and where intervention remained meaningful.
How the question changed. The issue was not whether a human remained nominally in the loop. It was whether the institution still controlled the consequential decision, understood how that decision could change, and retained a practical ability to intervene before harm occurred.
The work
- Identified the customer and financial decisions carrying material legal, conduct, or reputational consequence.
- Mapped formal decision ownership against the actual influence of models, prompts, workflow rules, data sources, and external providers.
- Classified actions by consequence, reversibility, external effect, and required level of human authorization.
- Defined intervention rights, evidence requirements, and change-control triggers for model, prompt, workflow, and provider changes.
- Prepared a board and executive decision memorandum separating acceptable automation from decisions requiring retained human authority or runtime constraints.
Leadership action. Leadership approved a staged expansion rather than a broad rollout: autonomous execution suspended for the highest-consequence actions, named executive owners assigned to delegated decisions, and approval gates, logging, and change controls required before additional scale.
Result. The institution moved from application-level AI governance to decision-level accountability. Lower-consequence automation proceeded, while formal human approval stopped standing in for meaningful oversight.
Role boundary. The principal defined the decision architecture, governance requirements, and conditions for authorization. The client's engineering, security, risk, and legal teams designed and implemented the production controls.
Representative principal experience
Re-sequencing compute and energy commitments before capital locks in
Capital discipline across power, compute, jurisdiction, and workload economics
Situation. An infrastructure investment platform was evaluating a multi-site advanced-computing program across two regulatory regions.
Scale. An infrastructure program spanning ten candidate sites and approximately 400 megawatts of contemplated capacity, across several utility and regulatory jurisdictions.
The decision. Where and when to commit capital to high-density computing capacity — and which assumptions had to be resolved before site acquisition, interconnection, and long-term supply agreements became difficult to reverse.
What made it difficult. The investment thesis treated the program largely as data-center real estate. In practice, the value of each site depended on uncertain grid interconnection, power price and carbon exposure, workload characteristics, data-residency constraints, network latency, equipment lead times, and the portability of model workloads. A site could be physically buildable yet economically or legally unsuitable for its intended workload.
How the question changed. The relevant asset was less a building with power than a portfolio of workload rights, energy access, connectivity, and jurisdictional permissions. Capital could be sequenced responsibly only after those dependencies were made explicit and stress-tested.
The work
- Separated training, fine-tuning, inference, and data-processing workloads by latency, power density, data location, and continuity requirements.
- Built a site-level dependency and downside matrix covering power availability, interconnection timing, network access, equipment supply, regulatory change, and exit options.
- Tested base, constrained-power, and sovereignty scenarios against expected utilization and capital intensity.
- Identified which commitments were reversible, conditionally reversible, or effectively irreversible once executed.
- Recommended capital gates, site sequencing, and diligence requirements for utility, regulatory, and technology assumptions.
Leadership action. The investment committee changed the order and conditions of commitment: sites with stronger evidence of power and workload fit advanced; locations dependent on unresolved interconnection or sovereignty assumptions were deferred; explicit portability provisions became a condition of long-duration technology commitments.
Result. A uniform build plan became a staged portfolio with decision gates — reducing the risk of funding sites whose energy, regulatory, or workload assumptions could not be supported once the assets became operational.
Role boundary. The principal provided strategic diligence, scenario design, and decision conditions. Utility engineering, financial underwriting, legal opinions, site design, and construction remained with qualified client and third-party specialists.
Representative principal experience
Sequencing physical autonomy across unequal operating environments
Venue-specific value, safety boundaries, and board authorization conditions
Situation. A logistics and light-manufacturing operator was considering autonomous mobile systems, machine vision, and software-directed equipment across multiple sites.
Scale. A physical-autonomy program covering 24 distinct operating venues across eight facilities, spanning production, fulfillment, and material-movement environments.
The decision. Where physical autonomy could produce durable value, which venues were not ready, and what conditions had to be met before the board could authorize deployment beyond isolated pilots.
What made it difficult. The proposed technologies appeared similar at the portfolio level but would operate in materially different environments. Site layout, workforce interaction, network reliability, process variability, safety obligations, local regulation, and vendor dependence changed the risk and economics of each deployment. A uniform rollout would have buried those differences.
How the question changed. The strategy had to be decided venue by venue. What the board needed was a basis for authorizing each class of use — something a general endorsement of robotics could never provide.
The work
- Segmented candidate venues by process stability, human proximity, environmental variability, reversibility, and consequence of failure.
- Assessed operational value alongside safety, liability, cybersecurity, labor, and continuity constraints.
- Defined non-negotiable boundaries for machine action, local override, degraded-mode operation, and vendor access.
- Established architecture and contracting principles to limit unnecessary dependence on proprietary hardware or control stacks.
- Recommended a sequence of pilots and scale decisions with explicit evidence thresholds for progression or withdrawal.
Leadership action. Deployment was authorized only in venues that met defined operating and control conditions. Higher-variability or higher-consequence environments were deferred pending additional evidence, process redesign, or safety assurance — and the board adopted a recurring authorization process rather than a one-time program approval.
Result. A broad technology rollout became a controlled portfolio of operating decisions. Investment moved first to settings where value and safety could be demonstrated, and the organization kept the option to stop or redesign deployments that missed their progression criteria.
Role boundary. The principal defined the strategic segmentation, authorization conditions, and provider principles. Site engineering, machinery selection, systems integration, safety certification, and operational acceptance remained with the client and qualified providers.
Representative principal experience
Reducing strategic dependence on a single AI provider
Institutional control, switching options, and disciplined use of frontier models
Situation. A technology-enabled enterprise had become materially dependent on one model and cloud provider across its AI applications.
Scale. Annualized model-inference expenditure approaching $20 million, with approximately 70 percent of usage concentrated in a single provider.
The decision. Whether to continue scaling on the incumbent, pursue a multi-provider posture, or redesign selected workloads to preserve control over economics, continuity, and future model choice.
What made it difficult. The visible issue was rising usage cost. The deeper exposure ran through proprietary interfaces, provider-specific evaluation methods, embedded safety assumptions, data-location constraints, model-change risk, and the operational cost of switching. A price negotiation would leave every one of those untouched.
How the question changed. Provider concentration was a strategic-control question, not merely a cloud-cost problem. The institution needed to know which capabilities were genuinely differentiated, which workloads could move, and what contractual or architectural changes would create credible options.
The work
- Mapped provider dependence across application logic, prompts, evaluation, identity, data, monitoring, and operational support.
- Segmented workloads by consequence, performance need, portability, cost sensitivity, and acceptable model variance.
- Estimated switching effort and identified dependencies that could not be removed economically.
- Defined sourcing and architecture principles for model choice, evaluation, fallback, provider change, and concentration limits.
- Prepared a leadership decision memorandum distinguishing where concentration was justified from where optionality had strategic value.
Leadership action. Leadership adopted a differentiated provider strategy rather than a blanket multi-model mandate: high-consequence or strongly differentiated workloads stayed on the incumbent under tighter change and continuity provisions; portable workloads moved behind common evaluation and routing controls; new applications were required to document concentration and exit assumptions before approval.
Result. The enterprise gained a clearer basis for provider choice and reduced avoidable concentration without imposing abstraction costs on every application — and its commercial negotiations were backed by credible technical options rather than price pressure alone.
Role boundary. The principal defined the dependency analysis, sourcing posture, and decision conditions. The client's architecture and engineering teams selected tools, built routing and abstraction controls, and performed production migration.
Representative principal experience
Independent diligence on an AI-enabled acquisition
Testing product advantage, dependency, and value before transaction commitment
Situation. An institutional investor was evaluating a software company whose valuation depended materially on AI-enabled products, proprietary data, and claimed automation advantage.
Scale. A transaction valued at approximately $450 million, with roughly one-quarter of the underwritten value-creation plan dependent on AI-enabled growth and margin assumptions.
The decision. Whether the target's technology advantage was durable enough to support the proposed valuation — and what conditions should attach to investment, acquisition, or integration.
What made it difficult. Management's narrative blended genuine product capability with access to third-party models, client-specific services, manually supported workflows, and data rights that were not fully transferable. Conventional technology diligence could confirm that the system worked without determining whether the advantage was scalable, defensible, or economically attractive.
How the question changed. What mattered was which part of the value proposition the target actually controlled — what it cost to sustain, and which assumptions would fail under scale, provider change, customer scrutiny, or regulation.
The work
- Decomposed the product claim into controlled intellectual property, external model dependence, data rights, human operations, and customer-specific configuration.
- Tested unit economics and service burden under realistic adoption and inference assumptions.
- Examined customer evidence, renewal behavior, and the extent to which outcomes depended on manual intervention.
- Assessed security, data provenance, model governance, contractual rights, and integration dependencies.
- Prepared an independent decision memorandum identifying valuation implications, closing conditions, integration priorities, and issues requiring specialist legal or technical review.
Leadership action. The investment committee revised the transaction posture rather than accepting the technology narrative at face value: part of the commitment was tied to evidence of customer retention and operating leverage, identified data and provider dependencies required remediation, and a post-close validation plan was established for the central product claims.
Result. The transaction decision rested on the elements of value the target could control and demonstrate. The diligence separated a credible product position from assumptions that required contractual protection, additional evidence, or a lower degree of confidence.
Role boundary. The principal provided independent strategic technology diligence. Legal, cybersecurity, accounting, tax, and formal valuation opinions remained with the relevant professional advisors. Compensation was not contingent on transaction completion.