Why The AI Opportunity In Wealth Management Looks More Like Sourcing Than Software

The registered investment adviser business is one of the most important — and most misunderstood — parts of financial services. In 2024, the U.S. advisory industry included 15,870 advisers serving 68.4 million clients and overseeing $144.6 trillion in assets under management (AUM). And yet it remains, structurally, a small-firm industry: 92.7% of advisers employed 100 or fewer people, and 68.5% managed less than $1 billion in assets. That matters because “wealth management” can sound like a giant industrial machine. In reality, it’s thousands of human businesses trying to scale trust, advice, compliance, and personalization all at once. Or, put less politely, it’s a federation of high-trust boutiques wearing institutional clothing. (IAA / COMPLY 2025 Investment Adviser Industry Snapshot)

The market is also moving in a direction that makes human advice more valuable, not less. McKinsey estimates fee-based advisory revenue rose from roughly $150 billion in 2015 to $260 billion in 2024. The share of investors seeking more holistic advice climbed from 29% in 2018 to 52% in 2023. Nearly 80% of affluent households say they’d rather pay a premium for human advice than use a digital-only alternative. Demand isn’t collapsing into self-service. It’s becoming more complex, more planning-led, and more human. Which is lovely for the firms that can serve it, and mildly terrifying for the ones still trying to do more with the same org chart and a fresh coat of AI paint.

“Wealth management isn’t entering a bad market. It’s entering a good market with a bad capacity equation.”

TL;DR: AI in wealth management is usually framed as a productivity story. I think that’s too small. The real opportunity is coordination: redesigning how work gets sourced, governed, and executed in an industry where demand for advice is rising faster than the labor model can support it. That’s why the most interesting AI players in the category increasingly look less like software vendors and more like participants in a new sourcing model. If I had to place an early bet on what this category might eventually be called, I’d place it on Workforce Capacity Orchestration (WCO).

Table Of Contents

A Healthy Market With A Structural Capacity Problem

That’s why wealth management now feels healthy and strained all at once.

In 2024, AUM grew 12.6% and clients grew 6.8%, while non-clerical employment grew just 2.6%. Inside those numbers sits the time bomb: demand for advice is rising faster than the industry’s ability to supply it. The market is healthy. The operating model is getting strained. And the supply side is moving the wrong way. Over the last decade, advisor headcount grew at only about 0.3% annually; over the next decade, McKinsey expects it to decline by about 0.2% annually, with the industry facing a shortfall of roughly 90,000 to 110,000 advisors by 2034 and about 110,000 advisors retiring over that same period. This isn’t a cyclical staffing inconvenience. It’s a structural capacity gap. In other words: the industry isn’t running into a weather problem. It’s running into physics. (McKinsey)

[Chart cue: Industry growth in clients, AUM, and employment.]
Source: IAA / COMPLY 2025 Investment Adviser Industry Snapshot

[Chart cue: Projected advisor shortfall by 2034.]
Source: McKinsey, “The Looming Advisor Shortage In U.S. Wealth Management”

That framing matters because it makes the whole thesis legible in one stroke. Wealth management isn’t entering a bad market. It’s entering a good market with a bad capacity equation. That’s the sort of sentence that makes investors smile and operators reach for antacids.

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The Old Answers Are Getting More Expensive

Most firms still respond with the old playbook: add software, add people, add outsourcing. But that playbook is getting more expensive and less effective.

Onshore hiring preserves control, but it leans harder into the scarcest input in the system. Compensation already accounts for about 70% of firm expenses. 73% of firms planned to hire in 2024. Schwab’s research suggests the industry will need roughly 70,000 new staff over the next five years. And median total cash compensation across roles rose 17% from 2019 to 2023. When firms solve capacity with headcount alone, they’re buying more of the most expensive thing in the system. When your answer to scarce talent is “hire more scarce talent,” you’re not really solving the problem so much as bidding against yourself with better stationery. (Schwab 2024 RIA Compensation Report)

The problem is that firms have already pushed the current model hard. Since 2019, median AUM per professional has risen from $99 million to $108 million. Clients per professional have increased from 53 to 61. Ops and admin hours per client have edged down from 17 to 15. But client-service hours per client have risen sharply from 29 to 40. The system is working harder. It’s not escaping the work. It’s the managerial equivalent of running faster on a treadmill and calling it transformation. (Schwab 2024 RIA Benchmarking Study)

And the hidden cost isn’t only labor cost. It’s labor allocation. Fidelity’s time-allocation work shows advisors spend only 41% of their time on clients and prospects, while 59% goes to admin, compliance, and other non-client work. Its estimate is striking: give an advisor just five hours a week back for higher-value work, and the revenue upside can reach about $270,000 annually. Wealth firms are paying onshore-advisor economics for work that doesn’t require onshore-advisor judgment. That is, to use the technical term, an expensive habit. (Fidelity, “The Time-Value Equation”)

[Chart cue: Advisor time allocation and potential revenue unlocked by returning five hours a week to clients and prospects.]
Source: Fidelity, “The Time-Value Equation”

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The Outsourced Answers Bring Their Own Tax

That’s why the conversation quickly moves to the familiar ~shoring models.

Onshoring means keeping work inside the firm, or at least inside the same country and operating environment. It maximizes control and proximity, but it also concentrates cost into the most expensive labor pool in the system.

Offshoring means moving work to more distant labor markets, usually to lower direct labor cost. That can absolutely create relief. But it also introduces new coordination work in the form of handoffs, vendor management, resilience, controls, and supervision.

Nearshoring sits between the two. It shifts work to nearby geographies to lower cost while preserving more time-zone overlap, tighter responsiveness, and easier collaboration than classic offshoring.

The crucial point is that these aren’t software categories. They’re sourcing models. Deloitte’s language is useful here: firms now manage an extended workforce ecosystem and need to think differently about governing multidimensional sourcing. That turns out to be the bridge. The old ~shoring vocabulary already tells us where the new category wants to live: not in software, but in sourcing. Which is convenient, because “one more AI tool” is how you get sent to the CTO, while “new sourcing model” is how you end up in a much more interesting meeting. (Deloitte Global Outsourcing Survey 2024)

Offshoring solves part of the labor problem, but it doesn’t erase management complexity. Deloitte’s 2024 Global Outsourcing Survey found that 80% of executives plan to maintain or increase outsourcing, 50% already outsource front-office capabilities, and 70% say their vendor-management function isn’t fully mature. That’s the hidden tax of offshoring: labor cost comes down, but management burden doesn’t. Labor arbitrage looks elegant in Excel right up until compliance asks where the client data went. (Deloitte Global Outsourcing Survey 2024)

In wealth management, there’s another tax too: data and regulatory exposure. FINRA’s current oversight guidance highlights third-party risk, including cybersecurity incidents and outages at vendors supporting critical systems and functions, and points firms to their supervisory obligations when using vendor-supported GenAI. The SEC’s amended Regulation S-P reinforces the same reality from the other side by requiring covered institutions to maintain written incident-response procedures, oversee service providers through due diligence and monitoring, and protect customer information against unauthorized access or use. In a business built on client documents, account data, suitability information, and persistent regulatory obligations, sending work into third-party systems isn’t just an operating choice. It’s a governance choice.

Nearshoring softens some of those problems, but it doesn’t change the underlying model. Deloitte’s nearshoring work makes the appeal obvious: U.S. software-developer median salary is about 2x the global median, and Brazil and Mexico alone offer 2.2 million+ software-engineering professionals and 350,000+ new engineering graduates each year. Nearshoring is real leverage. But it still depends on people, places, recruiting, vendor structures, and governance. It’s better coordination. It isn’t yet a new labor model. Nearshoring is a smarter version of the old playbook, not a different sport. And in a highly regulated industry, the same data-movement and supervision concerns that complicate offshoring don’t magically disappear just because the geography is closer. That may be one reason wealth management has been more cautious than other sectors about pushing operational work into third-party labor models. (Deloitte, “Nearshoring Software Engineering Tech Talent”)

[Chart cue: Offshoring’s governance burden and nearshoring’s talent advantage.]
Sources: Deloitte Global Outsourcing Survey 2024; Deloitte, “Nearshoring Software Engineering Tech Talent”

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AI Is Arriving, But Mostly As Tools

That’s what makes the current AI moment so interesting.

AI is clearly entering the RIA world. Schwab’s 2026 study found that 63% of advisors are already using AI, 23% are considering it, 11% are a hard no, and 3% remain unsure. But only about one in five firms says it has a real AI vision, and only about one in ten current AI users says it’s already using AI agents. In other words, AI is arriving first as a tool layer. It hasn’t yet become an operating model. (Schwab Advisor AI in Action 2026)

And the same pattern is showing up in outsourcing. Deloitte found that 83% of surveyed executives are already leveraging AI in outsourced services, but only 20% are building an explicit digital-workforce strategy, and only 25% report meaningful cost or quality benefits so far. The appetite is there. The architecture isn’t. Tool adoption is running ahead of operating-model change. Right now, much of the industry is using AI the way corporations used the early internet: for convenience first, transformation later, and usually with more enthusiasm than architecture. (Deloitte Global Outsourcing Survey 2024)

[Chart cue: AI adoption is rising faster than AI strategy.]
Sources: Schwab Advisor AI in Action 2026; Deloitte Global Outsourcing Survey 2024

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Productivity Is Too Small A Frame

This is where Sangeet Paul Choudary’s book Reshuffle: Who Wins When AI Restacks the Knowledge Economy becomes so useful. Choudary is best known for his work on platform business models, and the core argument of Reshuffle is that AI shouldn’t be understood mainly as an automation story. Its deeper power lies in new forms of coordination. Automation affects tasks. Coordination reshapes workflows, roles, firms, and markets. That’s exactly the lens wealth management needs. (Reshuffle announcement)

That’s the real issue in wealth management. The bottleneck is rarely the five-minute task. It’s the nine-hour queue wrapped around the five-minute task. The five-minute task is innocent. It’s the organizational traffic jam around it that commits the crime.

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One Company That Makes This Shift Concrete

One company that makes this shift easier to see is Humanity Labs. Led by founder and CEO Andrei Pop, who previously founded Human API and sold it to LexisNexis Risk Solutions, Humanity Labs describes itself as building the digital workforce for financial services. On its site, it’s explicit that the digital workforce isn’t a point solution, platform, or tool, but a managed team that lives inside the systems firms already use and takes on actual work. (Humanity Labs, “What Is The Digital Workforce?”)

That distinction matters because it points to something bigger than any one company. We may be reaching the point in the AI cycle where the real value accrues less to the players who merely provide models and more to the players who can make those models work inside real organizations. The harder and more valuable work increasingly sits around the software: workflow design, exception handling, governance, operating rhythm, adoption, and the steady translation of model capability into institutional results. That feels like a profound shift in where value may accumulate.

One revealing detail from recent conversations around Humanity Labs is that the company appears to be dogfooding the very change it’s trying to create for its partners. Internally, it’s not just talking about AI-enabled organizational redesign; it’s experimenting with it. In that framing, customers become partners, and functional silos begin to get reinterpreted around outcomes rather than internal activity. The old “Services Team” becomes the Value Team. “Services” is a functional term. “Value” is an outcome term. That isn’t branding trivia; it’s org design with opinions.

That detail matters because it hints at the kind of player this category may reward. If AI is increasingly about making organizations work differently, then the winners may not be the companies with the most impressive model in isolation. They may be the companies that combine software, workflow design, governance, and operating support into a coherent system that produces value reliably in the customer’s world. In other words: this may be as much a services moment as a software moment. Which is awkward news for anyone still hoping a prompt box and a procurement process will count as transformation.

The company’s case examples make the coordination point vivid. At one national RIA, it says risk-tolerance questionnaire reviews averaged 9.5 hours from start to finish even though the actual review took about five minutes. After moving that work into the digital workforce, turnaround dropped to about 20 minutes. The key idea isn’t that a task was sped up. It’s that waiting time, handoffs, and availability gaps began to disappear. That’s coordination drag coming out of the system. (Humanity Labs, “What Is The Digital Workforce?”)

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Why The ~Shoring Lens Matters

That’s why the ~shoring discussion is more than a clever analogy. It changes the room.

The moment the conversation is framed as onshore vs. offshore vs. nearshore vs. something new, it stops sounding like a software purchase and starts sounding like a workforce-model decision. That changes who belongs in the discussion. The CEO is in because growth capacity is at stake. The CFO is in because operating leverage is at stake. The COO is in because onboarding, servicing, reporting, and compliance are already being slowed by coordination friction. The CHRO belongs there too, because once humans, partners, and digital workers are all part of the workforce mix, the question becomes one of role design, accountability, and operating model — not just tooling. The CTO/CIO still matters, but more as steward of trust, integration, security, and control than as the sole owner of the problem. The minute this becomes a workforce-model discussion, the meeting gets more expensive — but also much more interesting. (Deloitte Global Outsourcing Survey 2024)

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The Fourth Model: Agentshoring

That’s where agentshoring becomes useful.

The reason the term works is because it gives buyers a familiar mental model for a new sourcing option. For decades, firms have asked whether work should stay onshore, move offshore, or be handled nearshore. Now there’s a fourth possibility: some work can be carried by governed digital workers embedded directly inside approved workflows.

Part of the appeal is substitutive. Some work that would previously have required new onshore hiring, a new offshore provider, or a nearshore team can now move to digital workers instead — especially repetitive, rules-constrained, system-mediated work. But substitution is only half the story. The more interesting role for agentshoring is coordination across a mixed workforce. No serious wealth-management firm is going to pull all of this down overnight. Onshore teams will remain. Outsourcers will remain. Nearshore support may remain. Legacy systems will certainly remain. Nobody is bulldozing the old org chart on a Tuesday and waking up Wednesday in agentic utopia. The point is to change the sourcing mix before the old one taps out.

The transitional advantage, then, isn’t just that digital workers replace some work. It’s that they help keep the remaining work communicating and connected: routing requests, normalizing inputs, moving data across systems, escalating exceptions, and reducing the brittleness of hybrid execution. That’s why agentshoring is better understood as coordination leverage than as labor arbitrage. It’s less “how do we find cheaper hands?” and more “how do we stop losing so much time between capable ones?” (Deloitte Global Outsourcing Survey 2024)

[Chart cue: Onshoring vs. offshoring vs. nearshoring vs. agentshoring.]
Sources: Deloitte Global Outsourcing Survey 2024; Deloitte nearshoring study; Humanity Labs framing

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The Industry Is Already Validating The Shift

One of the most encouraging things about this thesis is that the wealth-management industry is already starting to describe the future in almost exactly these terms.

The 2026 AI-themed issue of Investments & Wealth Review argues that AI in wealth management is not mainly a tools story. It’s an operating-model story. Its core messages are that AI should augment advisors rather than replace them, that the most believable early value sits in operational and middle-office workflows, and that the firms that win will be the ones that embed AI into workflows, governance, and the operating core rather than experimenting at the edges. It even broadens the conversation beyond advisor productivity into portfolio construction itself. In other words, the magazine is already validating the deeper claim: the human advisor stays, but the org chart and workflow architecture around the advisor start to change.

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Category Design Starts With The Hidden Problem

This is where Play Bigger becomes useful. In Play Bigger: How Pirates, Dreamers, and Innovators Create and Dominate Markets, Al Ramadan, Dave Peterson, Christopher Lochhead, and Kevin Maney argue that the companies that really matter don’t simply describe a better product. They teach the market to see a new and costly problem, then define the market around that problem. Play Bigger’s own formulation is crisp: the problem is the proxy for the category. That’s exactly the discipline this moment requires. (Play Bigger: Category Design)

In this case, the visible pain is backlog, headcount pressure, rising service intensity, and operational drag. The hidden problem is constrained capacity caused by coordination-heavy work inside regulated, relationship-centered operations. That’s the thing a category has to name. Because once the problem has a name, it stops sounding like ambient frustration and starts sounding like budget.

There’s also a broader point worth making here. Category creation isn’t a naming brainstorm. It’s a strategic process. Category Design Co. describes that process in a sequence that’s refreshingly practical: define the category, develop the point of view, align product, brand, and company design around it, and then execute a Lightning Strike launch to build momentum. That matters because it reminds us that naming comes relatively late. First you clarify the problem. Then you build the worldview. Then you align the business around it. Then you launch the market story. Naming a category is a little like naming a child: everyone has opinions, the stakes feel irrationally high, and one bad choice can follow you around for years. (Category Design Co.)

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What Good Category Names Actually Do

The best category names don’t simply sound fresh. They change what buyers notice.

Virtualization worked because it named a shift in the unit of management itself: physical hardware no longer had to be the organizing logic of compute. Contract Lifecycle Management (CLM) worked because it elevated a legal-document problem into a cross-functional lifecycle discipline spanning creation, routing, approval, storage, risk, and renewal. IT Service Management (ITSM) became more powerful when the frame widened into Enterprise Service Management (ESM), moving the conversation from IT tickets to enterprise-wide workflow. Experience Management (XM) lifted Qualtrics out of “survey software” and into a C-suite conversation about managing customer, employee, product, and brand experience. Revenue Intelligence, Execution Management System, and Connectivity Cloud all do similar work: they elevate the conversation from tool choice to management system. They didn’t win because they sounded clever at an offsite. They won because they changed what buyers noticed. (Docusign on CLM)

Whatever one thinks of the companies behind them, those labels share the same discipline. They begin with a problem that’s costly but poorly named. They elevate the conversation from tool selection to management system. And they create new buying committees in the process.

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So What Should This Category Be Called?

Category names usually need to sit one level above the product metaphor. They need to name the market and the hidden problem, not just the thing being delivered. And here, the hidden problem isn’t generic manual effort. It’s constrained capacity caused by coordination-heavy work. If the old vocabulary rolls up to sourcing, then the new vocabulary may need to as well.

That’s why the most interesting candidates, to my eye, all live in the overlap between sourcing, capacity, coordination, and governance.

Execution Sourcing (ES) has the virtue of clarity. It rolls naturally out of the ~shoring family and makes the sourcing pivot explicit. Its weakness is that it may sound a little too procurement-heavy.

Capacity Orchestration (CO) captures the deepest Reshuffle insight. The scarce thing isn’t merely labor hours; it’s the ability to coordinate work across humans, partners, systems, and digital workers. Its weakness is abstraction. It’s conceptually right, but it may feel a step removed from the buyer’s first pain.

Managed Execution Capacity (MEC) starts where buyers already live: capacity, execution, and delivery. Its strength is commercial clarity. Its weakness is that it says less about the coordination thesis than it probably should.

Governed Digital Labor (GDL) carries the labor-model story and the governance story in the same phrase. Its strength is strategic power. Its weakness is that the territory is already getting crowded. Workday is explicitly talking about a blended workforce and a digital workforce managed through an Agent System of Record. ServiceNow has planted a flag around Autonomous Workforce. Salesforce is using both digital labor and digital workforce inside Agentforce. That doesn’t kill GDL, but it does mean the semantic real estate is starting to fill up.

Operational Capacity Sourcing (OCS) makes the sourcing umbrella explicit and keeps the emphasis on real work rather than abstract AI. Its strength is seriousness. Its weakness is that it may be a little dry.

Coordinated Execution Services (CES) leans into the increasingly important idea that this may be as much a service model as a technology model. Its strength is that it gets away from software language. Its weakness is that it may sound more like an offering than a category.

Workforce Capacity Orchestration (WCO) may be the most complete of the lot. Workforce keeps the conversation in labor, sourcing, and org design rather than abstract automation. Capacity starts in buyer language; that’s the pain the market already feels. Orchestration captures the real insight: the next advantage doesn’t come only from doing tasks faster. It comes from coordinating work better across humans, partners, systems, and digital workers.

None of these feels fully settled to me yet. Real category creation would involve customers, partners, analysts, ecosystem players, and internal teams over time. It would require testing, resistance, repetition, refinement, and probably a few wrong turns. But if I had to make an instant decision before that process had fully matured, Workforce Capacity Orchestration (WCO) is the name I’d bet on. It has the rare virtue of sounding both slightly inevitable and just annoying enough that competitors will wish they’d thought of it first.

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Why Category Sharpens The Commercial Motion

This also changes how I think about the commercial model.

I believe in D3C strongly enough that I’m writing a book-length project about it on Copernican Shift: De Revolutionibus Orbium Emptoris — Libri III. The first two chapters are already out. D3C stands for Discovery, Create Confidence, Commit. It isn’t a funnel. It’s a lens. More specifically, it’s a way of looking at a buying journey from the buyer’s point of view rather than from the internal perspective of the GTM machine. That’s the real shift. Most go-to-market models are seller-centered by default: they describe what we do. They’re often written as though the buyer’s highest calling is to admire our funnel from a respectful distance. D3C starts from a different premise: revenue moves when a buyer becomes confident enough to act, not when a CRM stage advances. The moment you stop looking at the world through your org chart and your tools, and start walking in the buyer’s shoes, you notice different problems, different friction, and different opportunities to innovate. (D3C Chapter 1)

Discovery: The Hidden Problem Has To Become Visible

Discovery is the buyer trying to find a solution to a real business problem. In the first D3C chapter, I argue that Discovery is primarily word-of-mouth, now amplified and synthesized by LLMs. Buyers don’t begin with vendor websites; they begin by trying to assemble a credible solution path from peers, communities, analysts, review sites, conference conversations, comment threads, and increasingly from models that compress all of that public conversation into an answer. The output of Discovery is a Solution Thesis: here’s the problem, here’s the approach we believe will solve it, here’s how we’ll measure success, and here are the constraints we have to respect. That’s a fancy way of saying the buyer needs a coherent story they can repeat without sounding like they swallowed a vendor deck whole. (D3C Chapter 1)

That’s why Discovery questions in this market sound like: Why does every new client require more ops headcount? Where in onboarding are requests sitting untouched? Which functions are mostly coordination rather than judgment? If we grow 20%, where does the model bend or break? Those aren’t software questions. They’re sourcing and operating-model questions. A strong category helps both the buyer and the model interpret those questions correctly. It says: the thing you’re feeling isn’t just inefficiency. It’s constrained capacity caused by coordination-heavy work. That’s a very different diagnosis. And the diagnosis matters because it determines the remedy. If the problem is productivity, the answer is a better tool. If the problem is coordination, the answer is a different way to source and govern work. (D3C Chapter 1)

Create Confidence: Services Are Trusted Differently Than Software

Create Confidence is where the buyer decides whether proceeding feels responsible or reckless. This is the stage most organizations underestimate, and it becomes even more important when what’s being bought starts to look less like software and more like a services engagement. Confidence in software tends to come from one bundle of signals: features, architecture, integrations, security reviews, roadmap. Confidence in services comes from another: trust in the operator, clarity of scope, realism about the hard parts, referenceability, governance, evidence of execution, and the feeling that someone will still be there when things get messy. In the D3C model, the output of this stage is a Defensible Decision: the buyer can walk into the hardest internal room and make the case without you there, while still feeling emotionally safe about the bet they’re placing. (D3C Chapter 2)

That’s why Create Confidence in this category has to go much deeper than demos and proof points. It begins with proof of function, not proof of concept. Buyers need to see a real workflow mapped end to end, with current-state cycle time, handoffs, queue points, exception patterns, and controls made visible. They need governance confidence: clear answers on data handling, escalation, auditability, ownership, and risk. They need economic confidence: credible evidence that throughput will improve, backlog will shrink, expensive human time will be returned, and service levels will get better. And they need human confidence: the sense that the provider understands what’s hard, won’t trivialize the risk, and will help the buyer defend the decision internally. In D3C terms, the job here is to make the buyer’s internal case easier to build than their internal doubt. Or, more humanly: make it easy for the smart skeptic in the room to nod instead of smirk. (D3C Chapter 2)

This is also where the services-versus-software distinction becomes impossible to ignore. With software, all three stages tend to compress toward product evaluation. Discovery becomes “what tool do we need?” Confidence becomes “does it have the features, integrations, and security we need?” Commit becomes “can we get budget and implement it?” But when what’s being bought is really a new source of capacity, the buying psychology changes. Buyers aren’t only evaluating a product. They’re evaluating whether to trust someone — or something — with part of the operating model.

Commit: The Marriage, Not The Wedding

Commit is where D3C becomes most different from the classic sales model. Commit isn’t the moment the contract gets signed. The contract is the ceremony. Commit is the marriage. It’s the phase where the customer has to turn the decision into durable outcomes and sustained trust. In the D3C model, the output of Commit is Durable Outcomes: the buyer’s decision becomes operational reality, the solution survives real life, value shows up, and confidence increases after the purchase instead of peaking before it. This is where the customer stops buying the story and starts testing whether it survives contact with their Tuesday. (D3C Chapter 2)

That has major implications for a category like this. If the offering is effectively a new source of execution capacity, then the selling really starts when the contract is signed. The central task becomes making the customer feel great about the decision they just made — and then helping reality cooperate. That means operationalizing the solution into the customer’s world with governance, training, adoption loops, escalation paths, and change management. It means instrumenting value early, because churn often starts with an unspoken sentence: I’m not sure this is working. It means creating resilience, not just onboarding — building for failures, stakeholder changes, policy shifts, and the inevitable “this wasn’t in the deck” moment. And it means managing the seams between teams, because customers don’t experience Marketing, Sales, Services, and Success as departments. They experience them as one company — or as one company failing to behave like one. Nothing clarifies brand architecture quite like a customer trying to figure out why three perfectly nice departments are behaving like distant cousins at a funeral. (D3C Chapter 2)

That’s why D3C matters here so much. In software, Discovery, Confidence, and Commit often get compressed into product evaluation. In a services-shaped AI model, they stretch back out again into something larger and more human. Discovery becomes the work of diagnosing a capacity and coordination problem correctly. Create Confidence becomes the work of helping a buyer believe that a new operating model is safe, governable, and worth defending. Commit becomes the work of carrying that decision all the way through to realized value. Put differently: if the category really is moving from software toward sourcing, then D3C becomes even more important, because buyers aren’t simply choosing a tool. They’re choosing a new way to create, govern, and trust capacity. (D3C Chapter 1)

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The Bigger Opportunity

The opportunity in wealth management isn’t best described as using AI for productivity.

That’s too small.

The bigger opportunity is to redesign the firm so that work requiring judgment, trust, empathy, and decision-making stays with humans, while work requiring routing, validation, formatting, reconciliation, monitoring, and follow-through moves to a different execution model. That’s the shift from productivity to coordination. That’s the deeper implication of Reshuffle. That’s the message emerging from Investments & Wealth Review. And that’s why the most interesting players in this space increasingly look less like software vendors and more like participants in a new sourcing model. (Reshuffle announcement)

The human advisor remains central. But the org chart around the advisor doesn’t have to stay the same. That, to me, is the real story — not AI as a better tool, but AI as a better way to source and coordinate work.

Which is bad news for anyone still trying to solve a coordination problem with one more piece of software and a motivational Slack message.

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Further Reading

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Written by

Ever since I was a kid, I’ve wanted to be an astronaut — to explore the unknown, map new frontiers, and make the impossible routine. And since I couldn’t get to space, I’ve done that in marketing instead. Instead of The Right Stuff, I’ve been more focused on The Write Stuff. Across five CMO tours — at Microsoft, Sprinklr, PROS, Pendo, and a few bold startups — I’ve spent my career exploring new frontiers of growth.