# Bo Di Muccio

Bo Di Muccio writes about how technology companies can turn AI into business results, with a focus on professional services, forward-deployed engineering, and pricing work around customer outcomes.

- Profile: https://b2binfluencer.marketing/creators/bo-di-muccio/
- LinkedIn: https://www.linkedin.com/in/bo-di-muccio-ph-d-74a3323
- LinkedIn followers: 2,097
- Topics: artificial-intelligence
- Based on 12 dated posts, latest 2026-09-29

## Summary

Bo Di Muccio is most useful for explaining how AI changes professional services models, forward-deployed engineering, measurement, and outcome-based pricing.

- Best for: It fits a B2B SaaS company with a growing services arm taking AI-enabled offers to market.
- Not for: It does not fit a developer-tools company launching a self-serve API and seeking demo videos and signups.
- Check first: Confirm cited figures and preliminary survey data, plus audience breakdown and speaking fee.

### Takeaways

- AI widens the services layer because models do not deliver business outcomes on their own.
- Forward-deployed engineering is a distinct operating model, not a renamed professional-services bucket.
- Preliminary TSIA survey found outcome-based pricing obstacles were internal to providers, not customer reluctance.

## Main ideas

- **AI widens the services layer because models do not deliver business outcomes on their own**: Bo Di Muccio takes the loudest prediction in tech — that AI collapses the services layer and pools value in the model — and points to where the money went instead. Anthropic launched a services firm, OpenAI acquired consulting firms, and Microsoft, AWS and Google built similar capabilities, with more than $9 billion committed in about two months. The reasoning: the distance between a capable model and an actual business result is enormous, and everything that closes that distance is services. They cite the claim that roughly 95% of enterprise AI pilots produce no measurable P&L impact, then separate advisory work before and after the build from the embedded build work itself.
- **Forward-deployed engineering is a distinct operating model, not a renamed professional-services bucket**: Bo Di Muccio draws a firm line between advisory professional services and forward-deployed engineering. Advisory work covers readiness, data and operating-model fit, and governance after launch. Forward-deployed engineers are embedded with the customer, work on the customer's data, own the outcome, and stay aligned to the product and go-to-market motion. These carry different risks, margins, talent requirements and measures of success, they argue, so collapsing them into one bucket called PS or FDE leads services leaders to misprice both.
- **Outcome-based pricing is held back by the provider's measurement problem**: Bo Di Muccio challenges the standard explanation that customers block outcome-based pricing because they are not ready or do not like the math. In preliminary TSIA survey results described in the posts, only about one in five professional services leaders named the customer as the biggest barrier to outcome-contingent pricing. Roughly four in five named internal issues: difficulty measuring outcomes, delivery that is not standardized enough to contract against, finance or revenue-recognition constraints, and misaligned incentives. The most common answer was difficulty measuring and attributing outcomes to the provider's own work. They conclude that the constraint sits largely on the provider's side of the table, making it actionable.
- **The shift is from billable hours to adoption and outcomes**: Bo Di Muccio uses ServiceNow's internal AI deployment to argue that AI compresses the hours required to deliver a service, not the value of the result. The old software services organization made money mostly on implementation — bodies, weeks and a statement of work measured in hours. Under what the posts call PS 2.0, services organizations make money on adoption and outcomes. Deleting billable hours does not automatically mean lost revenue if the same work produces more value per customer over the life of a contract.
- **Services firms should productize repeatable solutions and ship solutions, not people**: Bo Di Muccio argues that the important detail in Accenture and Google Cloud's agentic AI unit is not the headline headcount of 1,000 forward-deployed engineers and 50,000 cloud-skilled people behind them. The unit's charter, as described in the posts, is to build replicable, industry-specific solutions and stand up capability centers that turn experiments into transformation. Productize an agent once and deploy it repeatedly across clients. That shifts the commercial asset from effort expended or hours billed to a result that can be packaged, priced, scaled and resold. They describe the result as a product company that happens to deliver services, and suggest watching whether earnings language moves from people delivered to solutions shipped.

## Work to read

- [The case that AI widens the services layer rather than collapsing it](https://www.linkedin.com/posts/bo-di-muccio-ph-d-74a3323_forwarddeployedengineering-professionalservices-activity-7483533411307925504-WAqa)
- [Survey finding: the barrier to outcome-based pricing sits inside the provider](https://www.linkedin.com/posts/bo-di-muccio-ph-d-74a3323_tsia-quick-poll-the-outcome-pricing-gap-activity-7475186326229655552-p3MT)
- [Reading the OpenAI–IBM deal as a services story](https://www.linkedin.com/posts/bo-di-muccio-ph-d-74a3323_forwarddeployedengineering-professionalservices-activity-7498018841868959744-s8n3)
- [ServiceNow deleted 2.5 million work hours and treated it as a pricing story](https://www.linkedin.com/posts/bo-di-muccio-ph-d-74a3323_professionalservices-ps20-aieconomics-activity-7505632339897651200-x2Gf)
- [Accenture's agentic AI unit: from bespoke projects to repeatable products](https://www.linkedin.com/posts/bo-di-muccio-ph-d-74a3323_professionalservices-ps20-aieconomics-activity-7510703939164524544-pyVS)

## Brand relationships

- **TSIA** (employment)

## Partnerships

The five posts contain no sponsorship offering, rate card, media kit, or partnership inquiry route, so there are no published formats to list. Bo Di Muccio writes in TSIA’s research voice, arguing that AI widens rather than shrinks the professional services layer, forward-deployed engineering is a distinct operating model from advisory professional services, and providers should price and operate around customer outcomes instead of hours billed. The posts reference a TSIA survey of professional services leaders and a year spent leading TSIA’s research on forward-deployed engineering. A partnership would need to be scoped from scratch. The posts don’t specify deliverables, disclosure practice, audience size for sponsored placements, production timelines, or commercial terms. Treat this as an editorial and research relationship, not a packaged creator buy. The nearest documented commercial home for this work is TSIA, whose research platform and events the posts repeatedly point to. A team seeking to reach professional services and customer-success leaders through this perspective should ask TSIA directly about sponsorship, research-partnership, or event options before including them in a plan; the posts don’t confirm that route. If a personal partnership offer exists, ask Bo or TSIA before quoting it.

## Alternatives

- Thomas Lah: Choose Thomas Lah when the services arm sits inside a wider revenue organisation and the sponsor wants the keynote to connect services economics to forecasting and margin strategy, not only to service delivery. The posts cover services as the engine of AI adoption and the argument for trading early services margin for lock-in and lifetime value, then move to revenue forecasting going model-driven rather than meeting-driven, with sales conversations, account data and operating data pulled into one live view. That is a useful second half for a company whose services leaders share a room with sales and finance. Lah is also the better choice if the format is a recorded interview or podcast rather than a stage talk, since the forecasting argument is presented through a guest episode and paired with a finance-side source.
- Rory Channer: Choose Rory Channer when the constraint is partners' ability to win and hold client work, rather than offer pricing. The posts draw on a book published by Harvard Business Review Press in May 2025, based on more than 18 months of research and input from over 100 professional services firms. They describe named client-engagement behaviours a firm can train, rather than a thesis to agree with. That suits a partnership-led audience in law, accounting or consulting, or a services organisation whose delivery is fine but whose client relationships are static. It also suits a longer engagement: the posts point to a development programme and partner retreats, with pre-orders and bulk orders offered, making a multi-session rollout a closer fit than a single keynote.
- Eric Quanstrom: Choose Eric Quanstrom when the brand wants to emulate an embedded, outcome-accountable operator rather than an advisory engagement. The posts describe the role from inside: accountability for whether the customer produces more pipeline three months out; embedding with the customer's team for the contract term, with no statement of work and no clock on hours; and a fixed seven-step sequence from total addressable market through to reporting. The work spans roughly 100 accounts, with eight to ten meetings per account per year, and pay tied to account health. The posts also cover what happens when an AI-native product is used outside its own application, as users reach the tool through chat assistants, the CRM and the browser instead of logging in. That makes Quanstrom useful if the brand's AI offers will be delivered by an embedded team or distributed across surfaces rather than sold as a destination product.

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AI-assisted summary of public posts. Audience demographics are not available. Engagement appears only where we checked recent posts. Method: https://b2binfluencer.marketing/methodology/