How to win at Vertical AI
How AI Agents and Open APIs create new rebundling opportunities
Much of the AI hype of 2023–24 has been centered around horizontal capabilities of foundational models.
The real opportunity of AI lies not in creating mediocre marketing copy, but in the ability to reconfigure value creation across vertical value chains.
While most AI hype is centred around horizontal B2C use cases, the real opportunity lies in vertical B2B AI.
This post unpacks the the opportunity of vertical AI and how companies can win this game.
Short-term and long-term games in vertical AI
Most vertical AI players today focus on developing vertical advantage with a proprietary fine-tuned model and a targeted UX.
This advantage creates a flywheel as I explain in How to win at Generative AI:
Smaller models, trained on domain-specific data deliver better performance on latency, accuracy, and cost than larger foundational models. This verticalization has its own reinforcing feedback effect. The more you develop vertical advantage, the more competitive you get on all parameters.
To deliver the most compelling vertical solution over time, the more the model is fine-tuned, the more deeply coupled future UX changes should be with the model in order to deliver the benefits of that model into the user workflow.
The advantage that vertical AI players have is that they operate full-stack — which is to say they provide a fully-integrated solution across the interface, proprietary models, and proprietary data. This creates the above flywheel of increasing defensibility as continued right to own the interface grants the tight to keep gathering proprietary data that helps further fine-tune the model.
This is how you win the short-term game in vertical AI.
But that doesn’t guarantee a long-term win.
Despite this flywheel, all vertical plays eventually end up participating in someone else’s ecosystem. This is a natural outcome of the abundance of vertical solutions that eventually proliferate. The end user looks to get their work done across one or two interfaces, instead of hopping across individual interfaces for every new use case.
Eventually, the only way to win in the long term with a vertical solution is to go horizontal.
Vertical strategies and AI-driven rebundling
To understand the opportunity in vertical AI, it’s helpful to look at the rise of vertical Saas over the past decade.
Vertical software repeatedly followed a playbook of
(1) gaining adoption with a focused use case, and
(2) rapidly rebundling adjacent capabilities around that initial use case.
Players like Square (with payments as a starting point), Toast (with POS software as a starting point) or ServiceTitan (with estimates as a starting points) have followed this playbook to scale out.
Eventually, all vertical plays seek to go horizontal.
The reason for that is as follows:
Most ‘disruption’ of the status quo happens through unbundling. But most venture returns are realized through rebundling.
Vertical plays unbundle. But in order to capture value at scale, you need to go horizontal and rebundle.
As I explain in How to win at Generative AI:
There is no sustainable value capture in unbundling. Unbundling unseats incumbents but doesn’t create scalable and defensible value pools.
That is achieved through rebundling. Rebundling involves bundling multiple unbundled capabilities into a cohesive customer-centric offering.
Most important, the successful ‘rebundlers’ establish a hub position and gain primacy of user relationship.
Venture capital chases unbundling because unbundlers hold the promise of rebundling and capturing value. Yet, most venture money is lost because a tiny handful rebundle.
This is the end game with Vertical AI as well.
You go vertical in order to go horizontal.
AI creates a new locus of rebundling
The opportunity for creating long-term competitive advantage in vertical AI is not so much in solving an initial pain point deeply and effectively. A lot of players will succeed in creating proprietary, fine-tuned models to solve for that. It’s a necessary requirement, but it’s not sufficient.
Long-term competitive moats in vertical AI are created by rebundling around the initial use case, using the unique rebundling benefits that AI provides.
AI creates a new locus for rebundling and the players that effectively leverage it around initial success in solving an early use case will be the ones that effectively dominate vertical AI.
Rebundling workflows in the age of AI
Business workflows are scattered across silo-ed software.
To actually get work done, these workflows have to be rebundled towards a business goal. You take the output of workflow X, Y, and Z, and make decisions or take actions towards a goal. This bundling of workflows towards a goal is performed by human managers.
Managers in organisations achieve this bundling by solving two problems:
- The plumbing problem i.e. coordinating across silo-ed workflows
- The goal-seeking problem i.e. using diverse workflows to achieve an organisational goal
In today’s B2B workflow, managerial resources in an organization serve as the locus of rebundling.
But technology is transforming the landscape of B2B workflows in two very important ways.
1. Increasing interoperability
First, workflow interoperability increases as we move towards increasing usage of API-based distribution of business capabilities.
I explain this in detail in DX is the new UX:
Business capabilities are increasingly being opened out through APIs for external consumption.
An API provides an interface to a business process or capability, while also defining the contract for engaging at that interface. More here.
APIs are pipes through which capabilities can flow beyond enterprise boundaries. As these pipes multiply, they create immense opportunity for plumbing. Different pipes can be plumbed together, with innovative business logic, to create fundamentally new workflows and capability bundles.
We’ve seen two important changes to the rebundling of workflows as a result.
First, companies like Salesforce and Hubspot, which sit at a central position in the workflow enable integration of other players to create a seamlessly integrated workflow.
Second, companies like IFTTT and Zapier allow users to create if-then trigger-based workflows, combining across multiple workflows.
Both these solutions solve the plumbing problem of coordinating across diverse workflows.
2.The rise of AI agents
Increasing interoperability solves the plumbing problem. It doesn’t solve the goal-seeking problem.
This is where AI agents create a unique opportunity for vertical AI.
AI agents solve the goal-seeking problem.
As I explain in AI won’t eat your job, but it will eat your salary:
Agents are goal-seeking, and that’s what makes them different. While most technology aims at task substitution, agents go beyond tasks to seek goals.
Every agent operates with at least one goal (and possibly more than one).
In order to accomplish this goal, an agent must (1) scan the environment, (2) plan and deconstruct the goal into constituent tasks, and (3) act out the plan leveraging other agents and digital resources.
In an environment where companies are increasingly provisioning APIs to serve key resources and capabilities, an agent performs the three functions above as follows:
(1) Scan the environment: It scans its environment to identify resources (including third party APIs) available at its disposal.
(2) Plan and deconstruct: It plans and deconstructs the goal into constituent tasks and sequences and roll-ups in which they should be performed.
(3) Act out the plan: Leveraging the resources (including third party APIs) available at its disposal, the agent acts out the plan to achieve the goal.
In effect, agents rebundle workflows across APIs to achieve an end outcome. This is a powerful new vector of performance.
The most powerful outcomes of vertical AI will be observed in domains of high interoperability where sophisticated and well-trained agents operate across the open resources.
Agents create a new locus of rebundling.
They enable vertical AI players to go horizontal by coordinating across multiple workflows.
This is already happening. Have a look at this Twitter thread on Self-Learning Agent for Performing APIs (SLAPA), from March 2023:
The rebundling power of AI agents
Most software focuses on automating tasks in a workflow.
Agents rebundle tasks towards goals.
As I explain in How AI agents rewire the organization:
Every goal is a bundle of tasks. And a managerial role — responsible for getting work done in an organization — is a bundle of goals.
When technology substitutes underlying tasks (red boxes below), the scope of the role remains largely unaffected as long as goal-seeking is critical to the performance of the role.
Let’s take the travel planning example. As new tools come in — travel booking tools, calendar management tools, payment tools etc, — specific tasks get simplified and even substituted by technology, but the goals within which these tasks sit are still managed by humans.
AI agents are different.
AI agents attack the goal. If an agent effectively takes over a goal, that goal no longer needs to be performed by the worker and no longer needs to be bundled into the human-managed role.
Effectively, a goal-seeking AI agent can unbundle a goal from the role.
The AI agent creates a new locus of rebundling.
There is fundamentally what “Sell work, not software” — a popular AI rallying cry — is all about.
Vertical AI players have a unique opportunity and that opportunity is not limited to merely developing a proprietary fine-tuned model. The real opportunity here lies in leveraging AI agents as a new locus of rebundling to integrate diverse workflows and coordinate work across them.
The vertical AI opportunity: Winners and losers
What factors determine winners and losers in vertical AI?
Vertical AI — leveraging rebundling of workflows through an agent — effectively replaces managerial resources in an organization context.
To understand how vertical AI players compete, think of what determines effectiveness of any managerial resource in an organization:
- The managerial capabilities required to get the job done
- Ease of access to the resources needed to get the job done
The ability of vertical AI to successfully serve as a point of rebundling, similarly, depends on two key factors:
- The sophistication of the AI agent
- The degree of interoperability and open access to third party resources in the domain in question.
Accordingly, not all verticals lend themselves equally well to vertical AI opportunities.
The extent to which you can create value through vertical AI depends on where that particular use case across the playing field shown below.
Vertical AI players benefit from high rebundling advantage in domains where there is high interoperability and high agent sophistication.
Most industries today operate in one of the other three quadrants. They lack domain-wide interoperability, agent sophistication, or both.
Given this reality, some industries and domains naturally favour the incumbents. This is likely to be the case in domains with high interoperability where a SAAS incumbent has already successfully established a hub position.
Conversely, vertical AI upstarts have a stronger entry point in domains with low interoperability.
Winning across B2B workflows
The starting point, in the bottom left quadrant, involves a situation where the user applies managerial effort across multiple B2B workflows which do not speak with each other. There is fragmentation in interfaces and in resources.
As domain interoperability or agent sophistication increase, this fragmentation is resolved by a particular player that establishes a hub position.
With increasing interoperability, the hub position is achieved through API integration. With increasing sophistication of an AI agent, that hub position is achieved by virtue of the fact that the agent takes over managerial effort so the user primarily engages with workflows through the agent.
Eventually, the paths converge to an end state with high agent sophistication and high interoperability.
The incumbent SAAS advantage
Consider the incumbent case first.
Domains where most players have already opened out APIs already have well-established workflow hubs. Players like Toast, Shopify, Hubspot, Salesforce, and others, successfully established themselves in a hub position, as diverse capabilities around them started getting provisioned as APIs. By gaining the right to customer relationship and the right to the core data identifier (e.g. the Salesforce ID), they rebundled all other workflows (through API integration) around this core position.
These incumbents are well-positioned to leverage their dominant hub position and bundle an AI agent on top of it. All the resources and capabilities for the agent are already well integrated into the hub position. The customer relationship is already owned by the workflow hub. The agent simply gets bundled into this position to strengthen the incumbent’s position.
The vertical AI upstart advantage
Ironically, the vertical AI advantage will play out in favour of startups in domains that have thus far resisted interoperability.
A superior AI agent being created in these domains disturbs the status quo. If such an agent effectively attracts the customer relationship and the agent increasingly starts operating as the workflow hub, the incentives for other players to open out APIs and provision their capabilities and resources increases as the customer relationship and workflow starts getting mediated through the AI agent.
Consider, for instance, an industry like healthcare where domain interoperability is very low. An AI agent emerging to facilitate in-home care for chronic patients may create the gravity needed for various players to open out their APIs and resources to such an agent in order to benefit from growing demand in the in-home care market.
The End Game: Go vertical to go horizontal
The end game of vertical AI is to develop horizontal advantage, not as horizontal AI but as a horizontal hub position.
That hub position, as we see above, is best established by leveraging agents. Agents provide a unique AI-native tools for rebundling.
As I explain in How to win at Generative AI, this is how the overall pattern plays out:
Step 1: Leverage the horizontal to go vertical
A range of ‘startups’ emerge at the workflow layer to verticalize this horizontal model.
You need unique insight into a vertical customer problem to get started here.
Step 2: Develop the right to win vertically
Develop some sort of vertical advantage (a proprietary fine-tuned model, access to vertical data sets, a vertical-specific UX advantage, or some combination of all the above) and leverage that advantage to pull value into a vertical play.
You can build advantage here by creating a full-stack vertical solution integrating across the model and workflow.
Step 3: Develop the right to win horizontally
Having developed vertical advantage, a very small number of the firms that succeed in Step 2 will find themselves owning a key control point through their vertical specialization — an advantage that gives them primacy of user relationship in that vertical.
These few firms will leverage this newly developed control point to start rebundling ‘over the top’.
The unique advantage that AI provides here is rebundling using AI agents.
Step 4: Leverage the vertical to go horizontal
As rebundling progresses around the control point, this player now emerges as the hub into which other players connect. It successfully creates an ‘over-the-top’ layer coordinating across multiple capabilities.
As the agent sophistication increases and domain interoperability increases, horizontal power accrues to this player.
Engage deeper on B2B platforms and AI
If you’ve enjoyed this analysis and my larger work on this topic (some of which is linked from this post), you might want to join my upcoming cohort-based deep-dive program on “B2B platform strategy in the age of AI”.
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