『The Accounting Technology Lab』のカバーアート

The Accounting Technology Lab

The Accounting Technology Lab

著者： Brian Tankersley & Randy Johnston
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In-depth, honest accounting software and technology reviews capturing the real-life experiences of using particular products and solutions - presented by CPA Practice Advisor and technology experts Randy Johnston and Brian Tankersley, CPA.(c) 2026 CPA Practice Advisor 経済学
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  • ATL276: The Secrets Agents Keep (Guest: Alexis Kingsbury, author "Accrual Intentions")

    ATL276: The Secrets Agents Keep (Guest: Alexis Kingsbury, author "Accrual Intentions")
    2026/09/25
    In ATL276, Randy Johnston and Brian Tankersley sit down with Alexis Kingsbury, author of Accrual Intentions, to unpack what he learned from building a deliberately extreme experiment: an accountancy practice staffed by eleven AI agents with their own roles, personalities, and responsibilities. Kingsbury explains that the project began as part parody and part management-science experiment, but quickly became a practical test of delegation, workflow design, controls, and human judgment.The central lesson is not that AI is either brilliant or useless. It is that AI can perform impressively on difficult tasks and still make simple, checkable mistakes. Kingsbury argues that firms should separate probabilistic AI work from deterministic processes, document workflows, insert stage gates, and decide explicitly where human review belongs. He warns leaders not to confuse delegating the work with delegating the thinking.The discussion also covers token economics, local models, model portability, and the risk of locking organizational knowledge inside one AI vendor's project environment. Kingsbury recommends keeping core context, processes, and organizational knowledge in systems the firm controls, then connecting AI tools to that context. His closing advice: do not wait for perfect AI, and do not attempt a giant transformation. Pick a painful, valuable problem, solve it deeply, learn, and expand.Key TakeawaysAI can be brilliant and stupid within the same workflow. Kingsbury describes Claude completing sophisticated work correctly and then altering an API-provided link enough to break it.Delegate execution carefully; do not accidentally delegate judgment. If AI takes over production, humans need to make the thinking, objectives, assumptions, and review gates more explicit.Deterministic controls matter. Where possible, use AI to create repeatable calculations, scripts, tests, templates, checklists, and processes.Review capacity has to scale with AI production capacity. Eleven virtual workers can generate an enormous volume of output, and that output still requires testing, prioritization, and accountable review.Your organizational knowledge should not belong to your AI vendor. Portability matters when prices, models, jurisdictions, or vendor strategies change.Token efficiency can become an economic issue quickly. AI consumption may require active optimization as usage scales.Waiting for “perfect AI” is not a strategy. Context, processes, guardrails, and review will remain necessary.Avoid the giant AI transformation project. Start with one sufficiently painful or valuable business problem, solve it well, and expand.Short Promotional CopyOne-Sentence PromoWhat happens when you give eleven AI agents jobs, personalities, responsibilities—and enough autonomy to expose everything that can go brilliantly right and spectacularly wrong?Three-Sentence PromoAlexis Kingsbury built an accountancy practice staffed by eleven AI agents and turned the experiment into Accrual Intentions. In ATL276, he joins Randy Johnston and Brian Tankersley to discuss what the experiment revealed about AI errors, delegation, controls, token economics, organizational knowledge, and human judgment. The big lesson: AI can dramatically expand what a firm can do, but only if governance and review expand with it.Promotional ParagraphYour AI agent just completed the sophisticated analysis, updated the documentation, called the right tools—and then broke the link it was supposed to give you.That kind of contradiction is at the center of ATL276: The Secrets Agents Keep. Alexis Kingsbury joins Randy Johnston and Brian Tankersley to explain what he learned building the AI-staffed accounting experiment behind Accrual Intentions. The conversation moves beyond “AI good” versus “AI bad” and gets into the management problem: how do you provide context, separate thinking from doing, design deterministic controls, scale review, manage token costs, and keep your intellectual property portable instead of trapping it inside one AI provider?Timestamped Pull QuotesTimeSpeakerPull QuotePromotional Angle05:03 | Alexis Kingsbury | “It'll just be easier if I do it myself.” | AI vs. delegation12:40 | Alexis Kingsbury | “If you want a really good decision made, you don't want two people who think the same.” | Diversity of perspective26:08 | Alexis Kingsbury | “There's value, but also risk… maximize value and mitigate the risk.” | Governance34:28 | Alexis Kingsbury | “If you can get rid of the things that we don't enjoy… you get more time on the things that do add value.” | Human value38:33 | Alexis Kingsbury | “If you are delegating the doing, you have to pull out the thinking and do the thinking up front.” | Management47:03 | Alexis Kingsbury | “I've been able to improve token efficiency by 4,000x.” | AI economics50:48 | Alexis Kingsbury | “One other big mistake I would suggest people avoid is giving the keys away to big AI firms...
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    57 分
  • ATL275: What Tokens Are You Smokin'? AI Routers and Token Marketplaces

    ATL275: What Tokens Are You Smokin'? AI Routers and Token Marketplaces
    2026/09/18

    Episode Summary

    AI may feel like a flat monthly subscription today, but ATL275 argues that the economics are moving toward a metered, multi-model market. Randy Johnston and Brian Tankersley examine AI routers, token marketplaces, and agent orchestration—tools designed to let firms choose different models for different jobs rather than committing every task to one vendor.

    Brian explains why OpenRouter and LibreChat can make model choice resemble buying fuel: use the amount of compute you need in the engine best suited to the work. Randy connects that concept to a three-layer AI strategy spanning general productivity tools, AI embedded in accounting applications, and specialized agents.

    The hosts also discuss the difficulty of forecasting token budgets, the likelihood of more usage-based pricing, and a growing field of gateways including OpenRouter, Ramp Router, Factory Router, Portkey, LiteLLM, Cloudflare, Kong, AWS, Microsoft, and Google.

    For accounting firms, the practical issue is not merely cost. Model selection, privacy, compliance, governance, and vendor concentration all matter when agents handle sensitive workflows. The takeaway: expect AI procurement to look less like buying one software subscription and more like managing a portfolio of compute suppliers—optimizing each workload for capability, cost, risk, and control.

    Key Takeaways

    1. Every AI workload consumes compute. Whether the customer sees a monthly subscription or an API invoice, somebody is paying for the tokens behind the transaction.
    2. One model does not necessarily fit every workload. AI routers can give organizations access to different models without separately funding and administering every provider.
    3. Cost optimization will become part of AI governance. Firms may increasingly route simple work to inexpensive models and reserve expensive frontier models for jobs that justify the added capability.
    4. Per-seat pricing may not be the final economic model. As agents become more common, usage-based pricing could become increasingly important.
    5. Accounting firms need more than cheap tokens. Privacy, contractual protection, data handling, auditability, regulatory compliance, security, and vendor stability remain critical.
    6. AI gateways are becoming strategic infrastructure. OpenRouter, hyperscale cloud platforms, and newer routing competitors are competing to become the transaction layer between applications and AI models.
    7. “Bring your own tokens” may become commonplace. Software vendors can increasingly let customers supply their own API credentials and select the AI models used inside applications.
    8. Token measurement should eventually resemble cost accounting. Firms need to understand the cost of AI by workflow, agent, client, and business process—not merely the organization’s total monthly AI bill.
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    17 分
  • ATL274: Alliances, AI, and Self-Funding! Oh My! Guest Dan Pinkous, Founder/CEO of Truss

    ATL274: Alliances, AI, and Self-Funding! Oh My! Guest Dan Pinkous, Founder/CEO of Truss
    2026/09/11
    ATL274 — Alliances, AI, and Self-Funding! Oh My!Guest: Dan Pinkous, Founder & CEO, TrussHosts: Randy Johnston and Brian F. Tankersley, CPA.CITP, CGMAPodcast: The Accounting Technology LabPublisher/Sponsor: CPA Practice AdvisorRelease Date: September 11, 2026Approximate Runtime: 28:26Primary Topics: Tax workflow, artificial intelligence, technology partnerships, client experience, advisory services, document management, practice management, startup funding, pricing200-Word Episode SummaryIn ATL274, Randy Johnston and Brian Tankersley talk with Daniel Pinkous, founder and CEO of Truss, about a tax workflow platform built around three ideas: low-friction client experience, AI embedded throughout the workflow, and partnerships instead of trying to build every specialized capability internally. Pinkous explains that Truss began in late 2022 as an AI-native product, using large language models for document processing, client checklists, research, planning, and other tasks intended to move firms away from compliance tedium and toward advisory work.The conversation highlights Truss partnerships with Ping Assistant, Kintsugi, Filed, Byron, Magnetic, K1x, GruntWorx, CygnusAI, Bizora, and Karbon. Rather than replace every tax, practice-management, or document-management tool, Truss aims to connect the client-facing parts of intake, workpapers, preparation, and delivery while keeping the experience consistent.Pinkous also discusses adoption, saying Truss emphasizes web-based, zero-account client access and reports high client satisfaction. The episode closes with a different startup story: Truss says it is customer-funded, has taken no outside venture capital, operates profitably, and uses a simple firmwide pricing model with unlimited users, returns, e-signatures, and AI usage.For accounting firms, the question is not simply which tool wins, but how a connected ecosystem improves workflow, service, and control.Key TakeawaysPartnerships can beat territorial product strategies. Pinkous argues that accounting technology vendors have historically been too territorial. Truss's strategy is to partner where another vendor has deeper expertise instead of attempting to recreate every specialty internally.AI is most useful when it disappears into the workflow. Truss was started in late 2022 and designed around AI from the beginning. Pinkous describes large language models working “under the hood” for document naming, page rotation, document processing, tax-year validation, checklists, research, planning, and other tasks.The goal isn't merely more compliance capacity. Pinkous sees automation first eliminating tedious compliance work and then creating room for higher-value advisory services.Tax workflow is an ecosystem problem. A tax engagement can involve intake, document collection, workpapers, preparation, signatures, payments, delivery, storage, and practice management. The more those pieces live in disconnected applications, the more time staff spend hunting for information instead of doing accounting work.There is a difference between “capital-W” and “small-w” workflow. Traditional practice-management systems such as Karbon or XCM manage the firm's broader internal workflow. Truss focuses heavily on the smaller client-facing workflows surrounding an engagement—requesting documents, exchanging information, preparing and delivering returns, collecting signatures, and keeping clients informed.Client adoption is an implementation metric, not a cosmetic metric. Pinkous argues that firms cannot redesign workflow around technology that only a fraction of their clients will use. Truss therefore emphasizes a low-friction, firm-branded experience with minimal login and account-creation barriers.Tax may be the entry point, but client experience crosses service lines. Pinkous says firms sometimes extend their use of Truss into audit and CAS because clients should not need an entirely different interaction model every time they work with another department.Document management remains an unresolved piece of the modern stack. The discussion touches on FileCabinet CS, SharePoint, Google Workspace, and other repositories. Pinkous describes Truss's own document-management capabilities while acknowledging that larger firms may still need more specialized systems and integrations.Capital structure affects product governance. Truss says it has been built without venture-capital financing. Pinkous argues that being customer-funded changes priorities because management is not simultaneously optimizing for an outside investor's required growth rate or next funding round.Pricing simplicity can reduce implementation friction. Pinkous describes firmwide pricing with unlimited users, returns, e-signatures, and AI usage rather than metering every transaction.Catchy Quotes and Video/Audio LocationsTimeSpeakerPull QuotePromotional Angle03:55 | Dan Pinkous | “The first phase… save these firms from the tedium… liberate them from ...
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    29 分
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