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Product SchoolHP President on Replacing Middle Management with AI Agents | Faisal Masud | E314
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CHAPTERS
- 0:00 – 0:50
AI misuse at work: when “slop” adds work instead of removing it
The host and Faisal Masud open on a concern many teams are seeing: AI-generated output that’s low quality and sometimes creates more work. Faisal describes how hard it’s becoming to distinguish human-written content from “slop,” and why that matters for productivity.
- •The host asks about people using AI in ways that aren’t “very great.”
- •Faisal says it’s increasingly hard to tell what’s written by a human versus “pure slop.”
- •Faisal notes some AI tools introduce more work into workflows rather than reducing it.
- •Faisal argues relying on AI for everything, without original ideas, makes a person less important in the work they do.
- 0:50 – 2:15
Meet Faisal Masud and what HP Digital Services actually is
Carlos introduces Faisal and frames the episode’s big themes: AI reality vs hype, enterprise adoption, and business model shifts. Faisal then clarifies his role and what “Digital Services” means inside HP.
- •Carlos introduces Faisal as SVP and President of HP Digital Services, describing it as HP’s software business.
- •Carlos previews themes including AI slop, AI as part of strategy (not the strategy), and how AI affects management layers.
- •Faisal explains he runs HP’s commercial software business sold into enterprises and channel partners.
- •The conversation positions HP’s software work as distinct from the company’s traditional hardware perception.
- 2:15 – 3:05
Why HP expanded beyond hardware: telemetry, data, and customer insights
The discussion turns to why a hardware-centric company created a standalone software unit. Faisal explains the need for data and telemetry around devices, and how software insights can better serve enterprise customers.
- •Carlos asks why HP expanded into software as a service given its hardware reputation.
- •Faisal describes a deliberate separation to create a standalone unit building software to support the hardware business.
- •Faisal says it’s hard to run a purely analog hardware business without data around the hardware.
- •He explains the thesis: collect telemetry from deployed environments and convert it into insights that serve customers.
- 3:05 – 4:18
A “by accident” SaaS path: from commerce and Wing to building Fabric
Faisal walks through how his background led to SaaS, emphasizing it wasn’t a planned move. He describes his early involvement with Fabric and how prior platform work influenced its direction and go-to-market.
- •Carlos asks how someone from e-commerce (and drone delivery at Wing) ended up in SaaS.
- •Faisal says he ended up in SaaS “completely by accident,” starting with Fabric after leaving Alphabet’s Wing.
- •He describes building a commerce platform at Staples as context for what Fabric pursued.
- •Faisal notes founder-led selling and scaling the company through later funding stages before joining HP.
- 4:18 – 6:03
AI FOMO, “AI manifestos,” and the irony of encouraging slop
Prompted by the host’s reference to a public “AI manifesto,” Faisal explains how AI FOMO drove adoption without guardrails. He points to an irony: leaders encourage everyone to go all-in, then complain about low-quality AI output flooding the organization.
- •Carlos references Faisal’s posts reacting to AI encouragement from prominent tech leadership.
- •Faisal says AI FOMO was “off the charts,” though it’s settling down as token spend becomes more visible.
- •He describes the irony of pushing all-in adoption without guardrails and then facing “slop central.”
- •Faisal emphasizes the need for data and measured adoption rather than blind commitment.
- 6:03 – 8:01
Getting to quality: AI excels at “inform and consult,” but needs human judgment
The host asks whether teams can reach quality without enduring a slop phase. Faisal explains where AI is strongest—gathering and synthesizing information—and why human judgment and creativity remain essential.
- •Carlos shares that his team went all-in and had to push through slop to improve quality.
- •Faisal says AI plays a dominant role in enterprises primarily for the “inform and consult” portion of work.
- •He warns against pure copy-paste usage and stresses human judgment as increasingly important.
- •Faisal says heavy reliance on AI without original ideas reduces a person’s value in the work.
- •Faisal notes he’s still waiting for the “slop” phase to fully come back down.
- 8:01 – 8:51
Cutting through AI tool overload: AI is part of strategy, not the strategy
Faisal reacts to the growing flood of AI tools, bots, and integrations. He argues that treating AI itself as the strategy leads to noise and wasted effort; instead, AI should support a clear business strategy and measurable goals.
- •Faisal describes the market flood of AI tools that plug into collaboration systems with alerts and bots.
- •He says some tools add work instead of subtracting it.
- •Faisal states directly: AI is not your strategy—AI is part of your strategy.
- •He frames AI as a tool in the toolkit rather than an end-all solution.
- •He reiterates the role of human judgment to ensure AI use creates real impact.
- 8:51 – 10:52
The agent layer on top of SaaS: security tradeoffs and uneven enterprise maturity
Asked about agents operating across SaaS products, Faisal focuses on privacy and credential risks, plus differences among enterprise suites. He contrasts selective adoption with more integrated enterprise options he’s seen, and notes security will be a major obstacle for broader delegation to agents.
- •Carlos asks about the emerging AI/agent “layer” on top of SaaS for productivity.
- •Faisal raises privacy and security concerns about giving tools credentials and access across systems.
- •He says people may be selective about what they allow an agent to do with logins and passwords.
- •Faisal discusses enterprise integration, saying Microsoft’s offering has become more integrated over time compared to what he has seen elsewhere.
- •He frames these tools as promising but constrained by governance and trust requirements.
- 10:52 – 12:19
HP’s internal AI operating model: compressing PM and engineering cycles
Faisal explains how his organization uses AI internally to speed up product work from ideation through early artifacts. He notes the biggest complexity remains getting to production, while PMs and developers use multiple AI tools to compress cycle time.
- •Carlos asks where HP Digital Services plays amid agentic opportunities.
- •Faisal says internal workflows are a key focus, even though he sells into IT leaders rather than running IT.
- •He describes a push: what can be done through AI should be done through AI, while keeping humans in the loop where needed.
- •He says AI compresses work from ideation to PRD and even early demo creation, but production remains a different challenge.
- •He notes developers and PMs use available AI tools to accelerate delivery cycles.
- 12:19 – 14:39
Moving fast inside a large enterprise: relationships, budgets, and proving ROI
The conversation addresses how speed and experimentation work in a big company environment. Faisal describes relationship-building to navigate approvals, and highlights practical constraints like token budgets and the need to justify spend with ROI.
- •Carlos contrasts startup urgency with enterprise bureaucracy and asks how Faisal keeps teams moving fast.
- •Faisal says large organizations run on relationships and bringing stakeholders along for approvals.
- •He describes pushing adoption of modern tools and using their capabilities broadly across the org.
- •He identifies token limits and budget pressure as a key bottleneck for scaling AI usage.
- •Faisal frames it as an ROI question: organizations must be able to do the math on AI spend.
- 14:39 – 16:00
Building Workforce Experience: a platform that may tell customers to buy less hardware
Faisal details the effort behind the product thesis for Workforce Experience and the internal tension it can create for a hardware company. He explains the product’s goal is a “world-class” employee experience, which requires removing bias and making tradeoffs—including recommending fewer device purchases when appropriate.
- •Faisal describes “blood, sweat, and tears” to build the thesis for Workforce Experience.
- •He explains the platform provides insights across hardware, software, OS, and network environments.
- •He acknowledges the counterintuitive message: sometimes the platform encourages buying less, which can clash with hardware incentives.
- •Faisal emphasizes removing bias and focusing on improving the customer’s employee experience.
- •He describes key validation questions: ROI, relevance, product-market fit, and the right distribution model.
- 16:00 – 18:16
Go-to-market evolution: platform positioning, device/OS agnostic, and owned IP
Faisal outlines how HP Digital Services sells Workforce Experience using both existing relationships and direct motions. He explains the shift from point solutions and third-party resale to a platform approach that is device- and OS-agnostic and centered on solving CIO-defined problems.
- •Carlos asks how much the team leverages existing HP relationships versus going directly to market.
- •Faisal says they do both, relying heavily on HP’s existing go-to-market rails.
- •He explains the move from point solutions toward a platform that must be device-agnostic and OS-agnostic.
- •Faisal describes a transition away from selling third-party software toward selling HP’s owned-IP products.
- •He says the sales motion is increasingly solution-oriented: align to customer problems and CIO RFPs rather than bundling software with hardware.
- 18:16 – 21:30
Outcome and ROI proof: dogfooding at scale before going wide
Faisal describes how the team insisted on internal proof and partner validation before broad release. He explains the focus on measurable outcomes—like ticket reduction and faster resolution—and ties the company’s “right to be in the room” to demonstrable ROI.
- •Carlos asks how close the offering gets to customer outcomes versus generic productivity promises.
- •Faisal says they dogfooded internally and had to clear a high bar to convince HP’s own IT teams.
- •He describes a long alpha and beta process with partners evaluating ROI before broad availability.
- •Faisal gives examples of outcome claims centered on reduced ticket volume and faster time-to-resolve.
- •Faisal states: if they can’t provide return on investment, they shouldn’t exist.
- 21:30 – 25:28
AI’s impact on org design: fewer management layers and more agent managers
The conversation moves to workforce transformation, headcount, and what AI changes structurally. Faisal argues AI reduces the need for multiple layers of management by enabling individuals to do monitoring and aggregation, and he envisions a world where people shift from managing people to managing agents.
- •Carlos asks what happens in the midterm to business due to AI’s impact.
- •Faisal says AI immediately makes extra layers of management less necessary because monitoring and aggregation can be done by individuals with AI.
- •He introduces a lab effort called AI Command Center aimed at understanding employee AI adoption, usage, and yield.
- •Carlos raises the operational problem: who manages a fleet of agents and decides when humans should step in.
- •Faisal describes a model where former “second-level” roles become agents, and senior roles become ICs managing agents rather than managing people.
- 25:28 – 27:57
Pricing in an agent world: shifting from seats to outcome-based economics
Carlos presses on how fewer human seats affect SaaS pricing models. Faisal describes a move toward charging based on measurable outcomes like ticket reduction and cost savings, arguing CIOs are becoming less willing to pay for dashboards and more focused on accretive software.
- •Carlos asks how agent-driven reductions in human seats change traditional seat-based SaaS pricing.
- •Faisal says the business can align pricing to ROI, such as reducing desk-side support costs and ticket volume.
- •He outlines possible models: seat-based with outcome commitments, or purely outcome-based pricing.
- •Faisal says CIOs are getting smarter about paying for outcomes rather than “dashboards.”
- •He argues AI forces a portfolio reset: if software isn’t generating ROI and can’t be computed, it won’t survive.
- 27:57 – 30:11
“No more dashboards”: executives using AI to stay close to customers and product building
In the closing stretch, the host and Faisal discuss how AI feels liberating, especially in reducing long-tail tasks. Faisal explains he uses AI to focus more on customers and to stay personally involved in product architecture work with his teams.
- •Carlos rejects more dashboards and meetings, saying AI has made it possible to do more directly again.
- •Faisal says AI helps handle long-tail tasks so he can focus attention on what matters most.
- •He distinguishes executive AI use from IC use, noting executives may already have human support structures.
- •Faisal says he wants to spend more time with existing customers to ensure ROI and repeatability.
- •He describes being hands-on architecting the AI Command Center—data sources, how results are surfaced, and what actions customers will take.
- 30:11 – 30:31
Wrap-up and closing remarks
Carlos thanks Faisal for discussing AI-native operations inside a large organization and for sharing practical perspectives on adoption, ROI, and organizational change. Faisal closes by thanking the host for the questions.
- •Carlos summarizes the conversation as learning from enterprise-scale, AI-forward operations.
- •Carlos thanks Faisal for his time.
- •Faisal thanks Carlos and acknowledges the questions.
- •The episode concludes.
