Technology is changing dramatically with the advent of generative AI, which will cause a significant change in corporate spending over the next ten years and beyond. Large-scale changes may seem to happen quickly at first, particularly when they create as much of a stir as generative AI has in recent months, but they take time to seep through the layers of the corporate IT stack.
When businesses put together the components for power and performance, they first invest in the infrastructure layer; judging by the money going into Nvidia and GPU aggregators today, this process is well under way. The emphasis of development will transfer to the new goods and experiences that will transform each layer below as adoption (and money) advance up the stack.
The application layer change is still in its early stages, but early indications point to a significant disruption. 
Enterprise apps started to provide more consumer-like experiences even before generative AI emerged by enhancing user interfaces and adding interactive components that would interest regular users and speed up productivity. Applications that were designed as “systems of record,” like Workday and Salesforce, began to give way to “systems of engagement,” like Slack and Notion.
We may anticipate even more profound change as generative AI influences the next generation of application products.
This new generation of workplace software, with features like version history, annotation capabilities, multiplayer mode, and metadata, was all about collaboration. These applications also made use of consumer-native viral elements to promote uptake and facilitate easy content sharing both within and across enterprises. Within these systems of interaction, the core record maintained its inherent value and functioned as a foundation for the increasing amount of data generated at the engagement layer.
The first players have a striking resemblance to ChatGPT integrators in that they construct simple tools right on top of generative models, providing brief but instantaneous benefit. Numerous generative AI solutions have already surfaced; they have shown tremendous early growth, but they also have very high churn rates because to their constrained workflows or lack of further features. These programs usually result in a generative output that is a one-time piece of media or material (i.e., not integrated into a user’s regular workflow), and their usefulness depends on readily accessible off-the-shelf generative models.
Just starting to take form, the second wave of generative AI applications will use generative models to connect the unstructured data found in system-of-engagement apps with the structured data found in system-of-record applications.
Those who develop these products will have a greater chance of building long-lasting businesses than those who enter the market in the first wave, but only if they can figure out how to “own” the layer above the system-of-engagement and system-of-record applications. This is a difficult task given that competitors like Salesforce are already rushing to use generative AI to fortify their underlying layers.
This brings us to the third wave, in which new players develop a defendable “system of intelligence” layer of their own. First, startups will launch new products that use the system-of-record and system-of-engagement capabilities already in place to create value. After establishing a compelling use case, they will develop processes that, in the end, function as standalone corporate applications.
The current interactive and database layers won’t necessarily need to be replaced; instead, new structured and unstructured data will be created, which generative models will use to improve the user experience of the product. In essence, this will result in the creation of a new class of “super datasets.”
These solutions should have integrations that can acquire, clean, and label data as a primary emphasis. For instance, consuming the knowledge base of already-opened customer support issues is insufficient to create a fresh customer support experience. Bug tracking, product documentation, internal team communications, and a host of other features should all be included in a really appealing product. It will be able to extract the pertinent data, label it, and calculate its weight to provide original insights. It will be equipped with a feedback loop that enables it to improve with use and training both inside and across enterprises.
When a product does all of this, it becomes very difficult to move to a rival since the cleaned and weighted data is quite important and it would take too long to get the same quality with a new product.
At this stage, the hierarchy, labels, and weights that go along with the product or model are just as intelligent as the actual product or model. Delivering insights will happen in minutes as opposed to days, with an emphasis on choices and actions rather than just the synthesis of data. These will be the real generative AI system-of-intelligence products, identifiable by these distinguishing characteristics:
possess a thorough understanding of business processes and the capacity to collect recently generated structured and unstructured data.
Be astute when it comes to using hierarchy, labels, and weights to characterize and process data.
Establish data feedback loops between and among consumers to improve their experience with the product.
One important question I often pose to clients is, “How does a new product stack up against the other tools you use?” Typically, the most crucial product is the system-of-record, which is followed by the system-of-engagement, and other tools at the bottom of the hierarchy.
When money is scarce, the least valuable product will be the first to be trimmed, therefore developing systems of intelligence must provide long-term benefits to be successful. Additionally, they will have fierce rivalry from market leaders who will include intelligence capabilities afforded by generative AI into their goods. To survive, the next generation of system-of-intelligence products will need to combine high-value processes, teamwork, and the addition of super datasets.
Over the last 12 months, the AI area has undergone rapid transformation, and the industry is picking up new skills quickly. Both closed proprietary models and open source models are developing at very fast rates. It is now the responsibility of entrepreneurs to create long-lasting system-of-intelligence products atop this quickly changing environment; if done well, the effects on businesses will be remarkable.
