Generative AI is a paradigm shift in technology and will drive a massive shift in business spending over the next decade and beyond. Transformations of this magnitude can feel rapid on the surface, especially when they make huge splashes like genetic AI has in recent months, but it’s a steep and steady climb to break through the layers of the business technology stack.
The infrastructure layer captures the initial outlay as companies assemble the building blocks for power and performance – the capital pouring into Nvidia and GPU concentrators today shows that this is off to a good start. As adoption (and dollars) climb the stack, the focus of development will shift to the new experiences and products that will reshape each successive layer.
We’re just getting a glimpse of how this transformation will unfold at the application level, and early signs suggest that the disruption will be profound.
Long before the creation of artificial intelligence, enterprise applications began to offer more consumer-like experiences, improving user interfaces and introducing interactive elements that would engage everyday users and speed up the workflow. This has driven a shift from “system of record” apps like Salesforce and Workday to “system of engagement” apps like Slack and Notion.
As genetic AI shapes the next generation of application products, we can expect even more sweeping development.
Collaboration was a defining feature of this new line of business tools, with features like multiplayer, commenting functionality, version history and metadata. These apps also leveraged viral native consumer data to drive adoption and enable seamless content sharing within and across organizations. The master record retained its inherent value in these systems of engagement and served as the foundation for the growing body of information created at the engagement level.
As genetic AI shapes the next generation of application products, we can expect even more sweeping development. Early players are a lot like ChatGPT integrators, building lightweight tools directly on top of production models that provide immediate but fleeting value. We’ve already seen a variety of productive AI products that have explosive initial growth, but also extremely high churn due to limited workflow or lack of additional functionality. These applications typically produce a production output that is a disposable type of content or media (ie, not integrated into a user’s daily workflow), and their value is based on off-the-shelf production models that are widely available to others in the market.
The second wave of genetic AI applications, which is just beginning to take shape, will leverage generative models to integrate the structured data found in system of record applications and the unstructured data found in system of capture applications.
Developers of these products will have more potential to build resilient companies than first-wave entrants, but only if they can find a way to “own” the layer above loyalty and logging apps — not no mean feat when incumbents like Salesforce are already trying to apply genetic AI to create a protective moat around their underlying layers.
This leads to the third wave, where participants create their own, defensible level of “system intelligence”. Start-ups will first introduce new product offerings that deliver value by leveraging existing system of record and loyalty system capabilities. Once a strong use case is established, they will then create workflows that can eventually stand on their own as a true enterprise application.
This does not necessarily mean replacing the existing interactive or database layers. Instead, they will create new structured and unstructured data where generative models use these new datasets to enhance the product experience — essentially creating a new category of “superdatasets”.
A key focus for these products should be integrations with the ability to capture, clean and label data. For example, to create a new customer support experience, it is not enough to simply take the knowledge base of existing customer support tickets. A truly compelling product should also include bug tracking, product documentation, internal team communications, and more. He will know how to extract relevant information, label it and weigh it to generate new ideas. It will have a feedback loop that allows it to improve with training and use, not just within an organization but across multiple organizations.
When a product does all this, switching to a competitor becomes very difficult — weighted, cleaned data is extremely valuable, and it would take a long time to achieve the same quality as a new product.
At this point, the intelligence is not only in the product or model, but also in the relative hierarchy, tags and weights. Insights will take minutes instead of days to deliver, with a focus on actions and decisions rather than simply synthesizing information. These will be the actual intelligence system products that leverage genetic artificial intelligence, characterized by these defining characteristics:
- Have deep integration with company workflows and ability to capture newly generated structured and unstructured data.
- Be sophisticated about characterizing and digesting data through hierarchy, labels, and weights.
- Create data feedback loops within and between customers to improve the product experience.
A key question I like to ask clients is, “Where does a new product stack rank with the other tools you use?” Normally the system product of record is the most important, followed by the system product of engagement, with additional tools at the bottom of the list.
The least important product will be the first to be cut when the budget is tight, so emerging AI products must provide lasting value to survive. They will also face stiff competition from incumbents who will build AI-enabled intelligence capabilities into their products. It will be up to the new wave of system intelligence to combine their offerings with high-value workflows, collaboration and the introduction of super data sets for resilience.
The transformation of the AI space has accelerated over the past 12 months, and the industry is learning fast. Open source models are proliferating and closed proprietary models are also evolving at an unusually fast pace. Now it’s up to founders to build resilient AI products on top of this rapidly changing landscape — and when done right, the business impact will be extraordinary.
