Generative ai is a paradigm shift in technology and will spur a massive shift in business spending over the next decade and beyond. Transformations of this magnitude may seem rapid on the surface, especially when they make a big splash like generative ai has done in recent months, but it's a steep and steady climb to permeate the layers of the enterprise technology stack.
The infrastructure layer captures the upfront spending as companies assemble the building blocks for power and performance; The capital pouring into Nvidia and GPU aggregators today indicates that this is underway. As adoption (and money) increases, the focus of development will shift to new experiences and products that will reshape each subsequent layer.
We are just getting a glimpse of how this transformation will play out at the application layer, and early signs suggest that the disruption will be profound.
Long before generative ai, enterprise applications began to offer more consumer-like experiences by improving user interfaces and introducing interactive elements that would engage everyday users and speed up workflow. This drove a shift from “system of record” apps like Salesforce and Workday to “system of engagement” apps like Slack and Notion.
As generative ai shapes the next generation of application products, we can expect even more radical evolution.
Collaboration was a defining feature of this new generation of enterprise tools, with features such as multiplayer, annotation functionality, version history and metadata. These applications also leveraged native consumer viral components to drive adoption and enable seamless content sharing within and between organizations. The central registry retained its intrinsic value within these participation systems and served as the basis for the increasing volume of information created in the participation layer.
As generative ai shapes the next generation of application products, we can expect even more radical evolution. Early players are a lot like ChatGPT integrators, building lightweight tools directly on top of generative models that deliver immediate but fleeting value. We've already seen a variety of generative ai products emerge that have explosive initial growth, but also extremely high churn due to limited workflow or lack of additional functionality. These applications typically produce a generative output that is a single-use type of content or media (i.e., not integrated into a user's daily workflow), and its value depends on commercially available generative models that are widely available. available to others in the sector. market.
The second wave of generative ai applications, which is just beginning to take shape, will leverage generative models to integrate structured data found within system of record applications and unstructured data found within system of record applications. participation system.
The developers of these products will have more potential to create lasting companies than the first wave participants, but only if they can find a way to “own” the layer above the system of participation and system of record applications, which It is not an easy task. when traditional companies like Salesforce are already scrambling to implement generative ai to create a protective moat around their underlying layers.
This leads to the third wave, where entrants create their own defensible “intelligence system” layer. Startups will first introduce novel product offerings that deliver value by leveraging existing system of record and system of engagement capabilities. Once a solid use case is established, they will develop workflows that can ultimately stand on their own as a true enterprise application.
This does not necessarily mean replacing existing interactive or database layers; instead, they will create new structured and unstructured data where generative models use these new data sets to improve the product experience, essentially creating a new class of “super data sets.”
A central focus for these products should be integration with the ability to ingest, clean, and label data. For example, to create a new customer service experience, it is not enough to ingest the knowledge base of existing customer service tickets. A truly compelling product should also incorporate bug tracking, product documentation, internal team communications, and much more. You will know how to extract relevant information, label it, and weigh it to create novel insights. You will have a feedback loop that allows you to improve with training and use, not only within one organization but also across multiple organizations.
When a product achieves all this, switching to a competitor becomes very difficult: clean, weighted data is very valuable and it would take too long to achieve the same quality with a new product.
At this point, the intelligence not only resides in the product or model, but also in the hierarchy, labels and associated weights. Insight delivery will take minutes rather than days, and will focus on actions and decisions rather than simply the synthesis of information. These will be the true intelligence system products that will leverage generative ai, marked by these defining traits:
- Have deep integration with enterprise workflows and the ability to capture newly created structured and unstructured data.
- Be sophisticated in characterizing and digesting data through hierarchies, labels, and weights.
- Create data feedback loops within and between customers to improve the product experience.
A key question I love to ask clients is, “Where does a new product stack sit relative to the other tools you use?” Typically, the registration system product is the most important, followed by the engagement system product, with additional tools at the end of the list.
The least important product will be the first to be cut when the budget is tight, so emerging intelligence systems products must provide lasting value to survive. They will also face stiff competition from incumbents who will incorporate ai-enabled generative intelligence capabilities into their products. It will be up to the new wave of intelligence systems to combine their offerings with high-value workflows, collaboration and the introduction of super data sets to endure.
The transformation of the ai space has accelerated in the last 12 months and the industry is learning quickly. Open source models are proliferating and closed proprietary models are also evolving at an atypically rapid pace. It is now up to founders to build durable intelligence systems products on top of this rapidly changing landscape, and when done right, the impact on businesses will be extraordinary.
