Generative AI – Page 2 – EduDataSci – Educating the world about data and leadership

From “Should‑Do” to Done: Digital Workers for Wealth, Energy, and Financial Services

Every enterprise carries a shadow backlog—the should‑do work that never beats the urgent. It’s the reconciliation that almost closes, the control that’s “fine for now,” the evidence that exists but isn’t filed where audit will accept it. None of these items is existential in isolation; together they become trust debt: silent risk, rework, slower decisions, and reputational drag.

2025 amplified the problem. Compressed settlement cycles demand same‑day precision in wealth operations. Emissions and operational reporting remain high‑stakes in oil and gas, with timelines adjusted but scrutiny intact. Financial institutions face fluid sanctions and evolving data‑sharing rules, while beneficial ownership reporting shifted materially. In each case, the gap is less about intent and more about capacity. Digital workers exist to close that gap.

Digital workers are policy‑aware software agents that connect to the systems already in place, act on explicit rules (with narrow ML where safe), and hand back proof that the job was completed—consistently and auditable. What follows clarifies how they work and where they quietly create value in wealth management, oil and gas, and financial services.

Spec‑Driven Development: Make the Specification the First Commit

If your acceptance criteria live in a comment thread, they’re not requirements—they’re opinions. Spec‑driven development (SDD) turns those opinions into executable truth so code, tests, docs, and operations move in lockstep.

Building on our split between functional and nonfunctional requirements, this follow‑up introduces spec‑driven development: what it is, why it reduces drift, and how to run it inside agile without ceremony. We’ll connect behavior specs, API contracts, data schemas, and quality budgets to lightweight gates in CI/CD and SLOs in production. By the end, you’ll have a “small slice” pattern you can ship next sprint. This is where spec-driven development meets Agile, DevOps, and APIs.

From Substitution to Outcomes: How AI and SAMR Are Forcing a Rethink of Development Strategy

We like to say we’ve “transformed” how work gets done. But if you look closely at many enterprise systems, you still see the outline of a paper form hiding under a slick UI.

We replaced paper with terminals, terminals with web apps, web apps with SaaS—and then pointed automation at the whole stack. In too many places, we’ve simply substituted one medium for another, without asking whether the underlying process still makes sense.

In this post, I’ll do three things:

Introduce the SAMR framework as a way to think about how technology shapes work.
Show how many business processes sit on layers of substitution going all the way back to paper.
Explain how AI enables goal‑based processes, where we define the outcome and how we’ll know we’ve met it, and let AI figure out the path—without prescriptive step‑by‑step code.

And along the way, we’ll confront an uncomfortable idea: some of your “modern” processes may still be driven by the personal preferences of someone who retired more than fifty years ago.

How Entra’s Agent Registry and Purview Team Up to Conquer Agent Sprawl

AI agents are showing up everywhere in the enterprise: Copilot add‑ins, line‑of‑business copilots built in Studio, “helper” bots glued onto SaaS apps, home‑grown automations running in the background. Individually, each one looks harmless. Collectively, they turn into something more dangerous: agent sprawl.

You get dozens (soon hundreds) of agents with overlapping responsibilities, inconsistent permissions, and no clear answer to a basic question: Which agents are touching my critical data, and under what guardrails?

Microsoft’s answer is starting to crystallize:

Microsoft Entra Agent Registry as the single source of truth for agent identity and metadata.
Microsoft Purview for Agents as the enforcement layer for data protection, DLP, insider risk, and compliance—using those identities as first‑class policy subjects.

Digital Workers, the White Space, and How to “Hire” One (with the Right Partner)

Every organization has white space: important work that lives between teams and across systems, is almost always evidence‑bearing, and—despite its value—rarely reaches the top of the backlog. In software engineering, that’s the unglamorous backbone of quality: keeping documentation and runbooks current, sustaining full test coverage (beyond unit tests), and validating against standards (security, accessibility, SBOM/licensing). In manufacturing, it shows up as traceability and shipment evidence (SPC, PPAP/FAI, calibration certificates) and keeping control plans/PFMEA in sync with engineering changes. In education, it appears as standards alignment of curricula, accessibility/privacy checks across LMS content, and intervention follow‑through after assessments. These jobs cross many systems, require judgment, must leave an audit trail, and are perpetually “important but not urgent”—perfect territory for delegating to digital workers: software teammates that live in the seams, move work to done, and attach the receipts as they go.

“To effectively delegate these tasks they need knowledge, access, and some intangibles.” (Nathan Lasnoski)

A digital worker earns real delegation only when three things are in place: knowledge (trusted sources, rubrics, examples), access (the right tools and permissions under guardrails), and the intangibles of a good teammate (when to act vs. ask, tone, and norms). With that foundation, a coaching worker can also serve as worker‑as‑judge—applying explicit rubrics, pulling evidence across systems, returning a pass/fail or “needs work” with a brief rationale, and providing an easy appeal to a human. The payoff is fast, fair, actionable feedback that feels like a senior reviewer on call 24/7—something frontline teams welcome.

How AI Can Help High‑School Football Between Friday Night and Saturday Morning

The lights go out, the sideline file finishes uploading, and the play list—your record of the plays actually called—lands in the same folder. While everyone sleeps, AI could take those two inputs (plus your existing playbook in the system) and quietly do the digital chores, so Saturday starts with coaching, not clicking.