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Steven Forth reposted thisSteven Forth reposted thisProof copy of my book has arrived...it is real! Some things only become clear after they exist. I read a post recently that made this point, and it stayed with me: When you build something new, you see what it could become. Everyone else can only see what exists today. I have felt that truth deeply over the last few months. When I first started talking about writing a book on AI and B2B pricing, there was not much to show. Just a collection of ideas gathered over 25 years in the field, a strong conviction about where AI is taking commercial strategy, and a belief that these two worlds belonged together. So I sat down and started writing. Three months later, those scattered ideas turned into 240 pages across 18 chapters. You keep building until the vision becomes something people can hold, test, challenge, and judge for themselves. Some things really do become clearer after they exist. Mine arrived in the mail today. What happens next? One final round of edits. There is nothing quite like reviewing a physical proof copy in the exact layout your readers will see. If all goes according to plan, you will be able to order your copy on Amazon very soon. More posts to follow, along with so many well-deserved thank-yous. For now, I am simply humbled and deeply grateful to everyone who helped bring this to life.
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Steven Forth reposted thisSteven Forth reposted thisPretty much every feature that Claude and ChatGPT ship lands on a SaaS pricing page. I broke down 10 of them, including companies with strong execution: 1. Skills (Figma) 2. Routines (Customer) 3. Loops (Linear) 4. MCP (Beehiiv) 5. Deep Research (SimilarWeb) 6. Workers (Notion) 7. Credit Budgets (Clay) 8. Web Search (Day.ai) 9. Voice AI (Lemlist) 10. Parallel Agents (Nooks) If I had to pick a favorite, it's probably Linear. Loops are gated to Business and up, with a prepaid credit balance then metered on top ($0.07–$0.20 for a typical Loop). Full breakdown below (fair warning, it's 8 minutes, one feature at a time). Check it out, and let me know what else you're seeing.
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Steven Forth shared thisThis article by Stephan M. Liozu, Ph.D. and Fabien CROS underlies the important of having a legible value model and pricing model. (I am going with the flow and saying 'legible' which implies something more than just a transparent model. Legible implies that the models are transparent in a way that AI's can understand and compute with them.) This is the mission of The Value Project, which provides an open source JSON Schema for both value models (in the EVE format) and for pricing models. https://lnkd.in/gmQAWp5u #AI #pricing #value #TheValueProjectSteven Forth shared thisAI alone is useless. I'll say it again, because a lot of teams are learning it the expensive way: a model doesn't know what your expected output means. Pricing is a great example. In most companies, "net price" is defined one way in Finance, another in Sales, a third in the ERP, and a fourth in the commercial analytics waterfall. Discount, customer, volume, margin, same story. Humans navigate those differences instinctively. AI can't. It only knows the structures and labels we make explicit. That's why so many AI pricing pilots produce outputs that look plausible and turn out to be unreliable. The model isn't weak. The business meaning around the data is missing. In our August article for the Professional Pricing Society's Pricing Advisor, Stephan M. Liozu, Ph.D. and I make the case for what we call the pricing semantic layer: a governed layer of business meaning sitting between your data products and your AI: definitions, calculation rules, hierarchies, guardrails, ownership, versioning. The thing that tells AI not just what the numbers are, but what they mean commercially. And here's the part I care most about: you cannot build that layer with AI. It comes from pricing people who have spent years arguing about what counts as a base price, which rebates belong in pocket margin, and where the exceptions live. That's the real pattern. AI on its own: useless. Deep human expertise on its own: doesn't scale. Blend the two, and you do miracles. You don't need a perfect enterprise ontology to start. Take the decisions that matter most: price recommendation, discount guidance, deal scoring, renewals and codify the concepts that keep causing confusion. That's it. The winners in AI pricing won't be the companies that experiment the most with models. They'll be the ones that made their pricing knowledge explicit, governed, and machine-consumable. Thank you Stephan for the opportunity to write this together, and thank you Kevin Mitchell and the whole PPS team for the platform.
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Steven Forth reacted on thisI have been looking at a lot of credit based pricing designs recently, partly for work (people hire me to design and review these systems) and partly for the book I am writing with Michael Mansard and Wolfgang Ulaga (Pricing for the Agent Economy). I am noticing quite a few cases where the price of the credit differs across packages or tiers. In general I think this is a poor design choice, one that undermines the power of these models. Here is my guidance on this topic. https://lnkd.in/gewEGZWA
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Steven Forth reacted on thisI have been looking at a lot of credit based pricing designs recently, partly for work (people hire me to design and review these systems) and partly for the book I am writing with Michael Mansard and Wolfgang Ulaga (Pricing for the Agent Economy). I am noticing quite a few cases where the price of the credit differs across packages or tiers. In general I think this is a poor design choice, one that undermines the power of these models. Here is my guidance on this topic. https://lnkd.in/gewEGZWA
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Steven Forth shared thisI have been looking at a lot of credit based pricing designs recently, partly for work (people hire me to design and review these systems) and partly for the book I am writing with Michael Mansard and Wolfgang Ulaga (Pricing for the Agent Economy). I am noticing quite a few cases where the price of the credit differs across packages or tiers. In general I think this is a poor design choice, one that undermines the power of these models. Here is my guidance on this topic. https://lnkd.in/gewEGZWA #AI #pricing #credits #creditbasedpricing #pricingfortheagenteconomy
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Steven Forth reposted thisSteven Forth reposted thisThe FTC Wants Disclosure. Sellers Should Want It Too. #impactpricing #pricingstrategy #pricingskills #pricingvalue #pricing #value #salesThe FTC Wants Disclosure. Sellers Should Want It Too.The FTC Wants Disclosure. Sellers Should Want It Too.
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Steven Forth posted thisHow did the world 'legible' become common? The word 'legible' has become common in writing about AI, agents, pricing and business generally. It means that something (a system, a data format, a behaviour, a causal model, etc.) can be interpreted, by a humn, an AI or a combination. I would nominate it for pricing word of the year. I suspect that this word has spread through AI and AI assisted writing. I asked the Perplexity AI about this. Here is the short answer. “Legibility” has become a more visible term in recent business, AI, marketing, and technical-operations writing. It now means not merely that prose is easy to read, but that an organization’s products, data, processes, policies, and claims are sufficiently explicit, consistent, authoritative, and structured for people—and increasingly AI systems or agents—to understand and act on them. The term is rising because AI exposes organizational ambiguity: scattered information, inconsistent terminology, undocumented workflows, and unclear data ownership limit reliable automation. In marketing, it also reflects a shift from optimizing for search rankings to ensuring that AI systems can accurately identify, verify, recommend, and transact with a business. For B2B software, this makes pricing, packaging, entitlements, and usage rules part of “machine-readable” operating infrastructure—not just customer-facing communication. #AI #legibility
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Steven Forth shared thisUseful and rational discussion of the Canada - US relationship. https://lnkd.in/gKCR7TmX #Canada #US #trade
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Steven Forth liked thisSteven Forth liked thisProof copy of my book has arrived...it is real! Some things only become clear after they exist. I read a post recently that made this point, and it stayed with me: When you build something new, you see what it could become. Everyone else can only see what exists today. I have felt that truth deeply over the last few months. When I first started talking about writing a book on AI and B2B pricing, there was not much to show. Just a collection of ideas gathered over 25 years in the field, a strong conviction about where AI is taking commercial strategy, and a belief that these two worlds belonged together. So I sat down and started writing. Three months later, those scattered ideas turned into 240 pages across 18 chapters. You keep building until the vision becomes something people can hold, test, challenge, and judge for themselves. Some things really do become clearer after they exist. Mine arrived in the mail today. What happens next? One final round of edits. There is nothing quite like reviewing a physical proof copy in the exact layout your readers will see. If all goes according to plan, you will be able to order your copy on Amazon very soon. More posts to follow, along with so many well-deserved thank-yous. For now, I am simply humbled and deeply grateful to everyone who helped bring this to life.
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Steven Forth liked thisSteven Forth liked thisIntroducing TimesFM-3, a state-of-the-art time series foundation model that enables accurate multivariate time series forecasting in a single forward pass, significantly outperforming other forecasting models across major benchmarks. More on the blog →https://goo.gle/4wXeXW0
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Steven Forth liked thisSteven Forth liked thisPretty much every feature that Claude and ChatGPT ship lands on a SaaS pricing page. I broke down 10 of them, including companies with strong execution: 1. Skills (Figma) 2. Routines (Customer) 3. Loops (Linear) 4. MCP (Beehiiv) 5. Deep Research (SimilarWeb) 6. Workers (Notion) 7. Credit Budgets (Clay) 8. Web Search (Day.ai) 9. Voice AI (Lemlist) 10. Parallel Agents (Nooks) If I had to pick a favorite, it's probably Linear. Loops are gated to Business and up, with a prepaid credit balance then metered on top ($0.07–$0.20 for a typical Loop). Full breakdown below (fair warning, it's 8 minutes, one feature at a time). Check it out, and let me know what else you're seeing.
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Steven Forth liked thisSteven Forth liked thisJust released: Tecsys has once again been recognized in the Gartner® Magic Quadrant™ for Warehouse Management Systems. Explore why Tecsys continues to be featured and see how its approach to warehouse innovation is helping organizations adapt to evolving operational demands.Tecsys is a Challenger in Gartner MQ 2026 for Warehouse Management SystemsTecsys is a Challenger in Gartner MQ 2026 for Warehouse Management Systems
Experience & Education
Projects
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21st Century Teams - What Hurts What Works
In Spring 2014 we surveyed more than 500 people on the challenges faced by today's teams. This eBook summarizes our findings and identifies key challenges and best practices. Read this if you want to succeed in the teams of teams world.
Other creatorsSee project -
Nugg App
- Present
Development of an innovative team collaboration app that surfaces the critical conversations happening on teams. The app also measures the team's energy and engagement. Integration with e-mail is provided for people who prefer e-mail to apps.
Other creatorsSee project -
Customer Success Manager at Nugg
Managing the private beta testing and roll out of the Nugg app to customers.
Other creatorsSee project -
Guest Blogger and WordPress consultant @ Nugg
Write thought-provoking blog posts based on the Nugg Manifesto and Values plus content based on team and collaboration related topics. Also assisted with WordPress-related questions and development.
Other creatorsSee project -
Einstein's Dreams
Einstein's Dreams, a project based on Alan Lightman's novella of the same name, was a webcast that followed the inspirational period of Albert Einstein's 'englightenment.' Lightman wrote a series of vignettes that provided insight into the notion of time, space and meaning that Einstein was engaging with as a young man. David was the interactive designer on the team, providing leadership for design establishing an algorithmic beauty to what was often an ineffable experience. The project…
Einstein's Dreams, a project based on Alan Lightman's novella of the same name, was a webcast that followed the inspirational period of Albert Einstein's 'englightenment.' Lightman wrote a series of vignettes that provided insight into the notion of time, space and meaning that Einstein was engaging with as a young man. David was the interactive designer on the team, providing leadership for design establishing an algorithmic beauty to what was often an ineffable experience. The project would later become a CROM but would not make it onto the market due to the changing economies introduced by the growing World Wide Web.
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Glenn Gould, The New Listener
Glenn Gould, The New Listener was the final CDROM swan song of DNA Multimedia. As the Web became a force to reckon with, the CDROM industry was sunsetting and this was our final award winning project. We won an EMMA award in the cultural content category and the project was never distributed in North America due to the shift in the publishing landscape.
Other creatorsSee project -
What is Value Management? And Why It Matters
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A short position paper that gives a common definition of value management, why it matters, and its relation to value modeling, value-based pricing, value-based sales and innovation.
Other creatorsSee project -
A User's Guide to Value Modeling by Stephan Liozu and Steven Forth
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An eBook that sets out the basic principles of Value Modeling (a central discipline of value management), provides a step-by-step methodology for how to build a value model and includes 15 examples of real value models from several industries.
Other creatorsSee project
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English
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Japanese
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French
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Open Angel
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PwC Canada
Reinventing automation starts with intent. Hear from Ahmad Haj Mosa, PhD and Kwesi Apponsah, PhD, leaders in our AI Services team, as they share how PwC Canada is moving from traditional requirements to a goal-oriented approach for agent development. This shift creates adaptive, collaborative agents that deliver outcomes aligned with business goals—powered by intelligent orchestration and continuous feedback. https://pwc.to/3ZxhFmR
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KanataQ
📊 Sustainability Providers: A Market Still Defined by Scale, Not Size After analyzing the data from over 400 verified sustainability solution providers on KanataQ, one pattern is clear: The sustainability ecosystem, spanning both software platforms and advisory firms, is still in its scaling phase, with most players operating at a small to mid-size level. Key Snapshot: • 70% of providers have fewer than 50 employees • 20% fall between 51–200 employees • Only 10% exceed 200 employees, mostly established global firms This highlights a market that’s diverse but still fragmented, built on lean teams competing through agility, domain depth, and cross-industry collaboration. Sustainability today is being shaped by compact, innovative organizations, from SaaS providers to consulting service providers, expanding their reach as demand for data, reporting, and impact measurement accelerates. This post is a preview from our upcoming Market Intelligence Platform and the KanataQ Sustainability Solutions Market Report, launching soon — offering data-backed insights on over 400 providers, including their size, pricing, client reach, and capabilities. 👉 Explore the ecosystem: www.kanataq.com
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BetaKit
Founders are facing a new set of realities: shifting SaaS benchmarks, an uncertain funding climate, and the pressure to adapt to AI. From November 5–6, SAAS NORTH Conference 2025 returns to Ottawa with a program built around those challenges. Featured sessions include: 👉 AI-Native vs. AI-Adaptive — Practical lessons on GTM, pricing, and team design in the AI era 👉 Hot Tub Time Machine — Veterans share hard-won lessons to prepare for the future 👉 Building Beyond Success — Real talk on reinvention, resilience, and leading through uncertainty Register with code BETAKIT20 for 20% off: https://bit.ly/48sGm9R
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PLATFOME AI
Why are more Canadian startups opting for open-source ERP as their growth engine? Because they need scalable, flexible solutions that keep pace with their ambitions without breaking the bank. Unlike traditional ERP systems with hefty licensing fees and rigid structures, open-source platforms like ERPNext offer startups the freedom to customize, create new solutions, and expand confidently. Take a growing Canadian manufacturing startup that switched to an open-source ERP. They reduced operational costs by 30%, improved inventory tracking accuracy, and accelerated their production cycles—all while scaling their team rapidly. This shift means startups no longer have to compromise between affordability and enterprise-grade capabilities. They get strong, AI-powered tools tailored to Canadian compliance and market demands, all supported by a community-driven ecosystem. For entrepreneurs ready to future-proof their operations, open-source ERP is not just a tool—it's a strategic growth partner. What's holding your startup back from making the switch to open-source ERP? #erp #aiautomation #canadianmanufacturer
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