Mark Roberge’s Post

View profile for Mark Roberge, graphic

Co-Founder @ Stage 2 Capital, Prof @HarvardHBS; Founding CRO @HubSpot; Author of Best Seller "The Sales Acceleration Formula"

In B2B software, will #AI be an "iteration" or a "disruption"? The late Clay Christensen’s “Innovators Dilemma” is a useful framework to analyze this question.  The slide below summarizes the innovator’s dilemmas that arguably drove disruption across most B2B categories as we transitioned from On-Prem to Cloud and suggests potential innovator’s dilemmas as we transition from Cloud to AI. This was just one of the topics we presented to our Stage 2 Capital LP base of over 600 C-level executives across B2B tech at MIT's Samberg Center in Boston last week. We also heard from Brian Halligan, co-founder of HubSpot and Stage 2 Capital LP, and three of our portfolio CEOs - Alex Sambvani from Slang.ai, Skylar Talley from MedScout, and Zachary Garippa from Order.co.  We hold this meeting twice a year.  They are probably my favorite professional days.  My learning accelerates and I get to see first hand the software ecosystem progressing and improving, a core tenant of the Stage 2 mission.  Below are four examples of interesting narratives that got me thinking a bit. (1) Red tape from legal and IT in public tech companies may not be the most significant blocker to AI adoption in these organizations.  Instead, it is executive burn out, as teams and leaders are struggling to simply keep the lights on with smaller staffs and big expectations and have no bandwidth for new initiatives.  This trend could accelerate the timeline by which native AI attackers overtake the incumbents in each software category when compared to the same timeline in the On-Prem to Cloud transition. (2) Incumbent services companies are taking the Service-as-a-Software movement seriously.  However, despite their focus, they may not be able to get to the end goal faster than Service-as-a-Software organizations built from the ground up today with an AI-first operational foundation.  (3) Over the last decade, Ops teams have evolved to be a critical driver of success in tech companies.  The Ops algorithms are working behind the scenes to accelerate the performance of frontline human workers.  As AI continues to evolve, we may see this paradigm shift.  We may see the Ops algorithms driving the front-line work and the humans behind the scenes optimizing the algorithms.  (4) The acquisition of proprietary, clean data for AI algorithm training is already on the rise as a separate and critical department within large tech firms and is expected to accelerate across tech orgs.  These teams are better positioned within the Product, rather than the Business Development, teams. Shoutout to all of our LPs! We can’t wait to do it all again on the West Coast this fall.

  • No alternative text description for this image
James Charlesworth

Building autonomous revenue systems.

1mo

Thoughts on iteration v disruption of B2B software: I think it depends on the outcomes that can be delivered as a service autonomously. Many B2B product paradigms are built for users. Many of these click streams and features will not be needed. Staying close to the customer, the knowledge, the code and the LLM is the stack to architect IMO. Obviously all companies are different. I am amazed at how some have their heads in the sand. Reminds me of when media was disrupted. I am seeing new shapes of companies emerging that have the potential to move faster, and with a higher bar for quality, precision and machinable and predictable unit economics.

Gary Schwake

Go-to-Market Acceleration

1mo

"Service-as-a-software" is real. We are seeing this evolution of the managed services model in certain components of RevOps and Data Warehouse-based reporting, where we worry less about the tech and more about the capabilities required to deliver the outcomes. For lower middle market companies, it is becoming possible to "rent" instead of build-to-own, at least during this period of rapid change and disruption.

Such an important insight Mark Roberge - not so much for the greenfield new-entrants, but IMO for incumbent companies. They are jumping on the "AI bandwagon" and applying AI-assist to existing products. This potentially poses risk b/c (a) it pulls resources away from developing/maintaining the core product (b) it may never product the desired value that a ground-up AI-based equivalent product might.

Ray Rike

Enabling B2B SaaS companies to make better metrics-informed and benchmark-validated decisions using our industry benchmarks, primary research, events, media and advisory services to increase revenue growth efficiency

1mo

Mark Roberge - great post and disappointed I could not join the group last week - a couple of comments: 1. Point number 4 is soooo important - having access and the ability to use clean and proprietary data that serves as an "AI training" moat is critical to training the foundational models that will be the foundation to the winners - and is not just a one time event 2. Adaptation and change is almost always harder for incumbents and the larger the incumbent the more challenging the change - each new wave of discontinuous tech innovation provides the open door for newcomers...though in this wave the incumbent hyper scalers who provide the foundation and have the capital resources to scale the required AI infrastructure will also be big time winners

Carilu Dietrich

CMO, Hypergrowth Advisor, Took Atlassian Public

1mo

It's so hard that we are entering an innovation inflection point right in the middle of a cost-cutting financial efficiency period. It puts incredible pressure on product teams to pick which child they want to feed.

Dan Sperring

Founder @ AlignICP | The smarter way to power your account-based marketing.

1mo

Love the framework Mark Roberge and the prior art from Christensen (RIP). Here's an idea to add to the framework and a potential application: Shift In Value Chains: -AI is currently driving commoditization into the content creation layer of our GTM system. -Ad networks are already sophisticated in micro segmentation and targeting capabilities. - Customer Data: Is largely underutilized for segmentation purposes. What if we used AI/ML to help us better understand our first party data to surface high-value and best fit customer segments? With the reduction in the cost of content creation, could we afford to create foundationally more targeted campaigns that hit harder and convert at higher rates? Think improved LTV/CAC, Payback Periods. Scott Brinker recently published his latest Martech landscape report and there are 14,106 martech companies! MAPs, ESPs, ABMs, Ad Networks.....all kinda do the same thing. They create a customer profile, add segment attributes, and send a message. CDPs were the promise, but most companies are hamstrung by the quality of the customer data. I believe your #3 powers #4. AI thrives on clean and normalized datasets. Knowing your customer is the foundation of every great business.

John Williams

SaaS Revenue Accelerator 📈 | Growth Partner for SaaS ScaleUps

1mo

Joelle Gropper Kaufman - we discussed this last night; "it is executive burn out, as teams and leaders are struggling to simply keep the lights on with smaller staffs and big expectations and have no bandwidth for new initiatives." I had not considered Mark's insight of how this may paralyze current category leaders and enable AI-empowered take-overs 🤔

Like
Reply
Mary Gilbert (Kerford)

FRACTIONAL CMO, SCALE-UP ACCELERATOR, FOUNDER Infinite Edge Consulting and Future Ready CMO

1mo

Constraint is the mother of innovation. These are the right conditions for innovation to flourish.

Like
Reply
Doug Miller 🚀

Fractional COO Driving Scale and Profitability 🚀 @MidstageInstitute | Putting AI in your Strategic Planning 🚀 Igniting Outcomes and Urgency | Experimenting, Executing, and Optimizing 🚀

1mo

Very helpful! What a great post... Saved it to reflect on implications across markets and clients...

Like
Reply
See more comments

To view or add a comment, sign in

Explore topics