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AI Assets and Intellectual Property Ownership

Ramesh Mahalingam

A subtle, deliberate shift has occurred within the software industry which, at present, only the most innovative organizations have seized upon for competitive advantage. Although primarily driven by Artificial Intelligence (AI), this transformation strikes at the core of the most pervasive IT resources including cloud computing and predictive analytics.

Initially, software was used to solve internal problems via an ownership model that eventually yielded to the contemporary SaaS paradigm in which applications are effectively rented. However, the next generation of application building is based on nuanced, individualized customer experiences in which ownership of the intellectual property of software is a critical determinant for optimizing revenues and customer relationships.

Intellectual property ownership is not possible with contemporary SaaS offerings. What's required is a new approach in which organizations decouple their stacks from AI vendors', commercialize them for bespoke customer experiences, and retain full rights to the intellectual property of the customized, artisan AI models delivering these advantages. The monetary benefits of owning the AI assets organizations use, and their intellectual property, will ensure this next evolution of app building is as ubiquitous as the current one is.

Customized Artisan Solutions and AI Models

AI is the forerunner in this movement because of the degree of individualization it offers via its micro-segmentation capacity. Artificial Intelligence systems can personalize models for particular demographics including age, geographic location, user behavior and an assortment of other pertinent categories. For example, in finance, a bot can know the best way to communicate with a person according to his or her profile and customer segmentation.

Because all of these various factors are packaged within AI models, there's a pivotal distinction in owning their intellectual property, particularly when it comes to commercializing it by offering services to consumers. However, when those models are simply rented from AI vendors via SaaS or other options, organizations are restricted in their ability to customize solutions by including the vendor in the processes. The reality is that simply by using the vendor's resources to build or deploy models, organizations are compromising their claims to the intellectual property of services based on them.

Commercializing Bespoke AI

Intellectual property is a key factor for commercializing customized AI models that tailor services to individual consumers. In insurance, for example, an organization may want to create additional revenue streams by offering a service to senior customers to store their wills, trusts, and other such documents in one place. In theory, such a company should have the autonomy to sell this capability as a service. If its applications are running in current cloud offerings like Salesforce, however, this conglomerate owns the intellectual property for this idea because it's facilitating the infrastructure.

Modern organizations require total ownership of their AI resources, whether that involves owning models, bots, or the intellectual property driving them. Without ownership of that intellectual property, organizations are considerably slowed — if not stuck in a state of paralysis — when it comes to extending new, personalized services to their customers that AI substantially helps facilitate. But if they owned the whole stack themselves, they could reap the full monetary benefits of commercializing AI without any restrictions of building atop an application provider's stack.

Decoupling Services from AI Stacks

The ability to decouple AI from a vendor's stack to commercialize various services at will is a crucial prerequisite for exploiting the boons of intellectual property ownership of AI assets. The majority of contemporary SaaS providers don't offer this degree of autonomy. Nonetheless, modern AI platforms give organizations the license to decouple everything needed to independently deploy AI services where they're most beneficial, giving them sole claim to the intellectual property.

Typically comprised of a user interface, bots specializing in various forms of AI, and respective layers for workflows and integration, these mechanisms are optimal for rapid deployments in a multiplicity of environments whether on-premises, in hybrid or multi-clouds, or at the edge. Such solutions are virtual software factories in which organizations not only own their intellectual property, but can sell AI services to customers with complete autonomy.

The Valuation of Intellectual Property

Ultimately, this emergent paradigm for deploying software will usurp the modern one for customer-facing applications because of the financial benefits of intellectual property's valuation. Intellectual property rights enable organizations to deliver hyper-personalized customer interactions, own the artisan AI models driving them, and profit exponentially on their net valuation.

In insurance, if a company can demonstrate its customers on-board through a technology platform it owns, its valuation increases 10 times because it's categorized differently on its balance sheet than if it used another's platform. Insurance revenues of $20 million become $200 million. That difference is the final boon of intellectual property ownership in the AI services space, which also includes individualization and celeritous commercialization to satisfy customers the way other technologies can't.

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AI Assets and Intellectual Property Ownership

Ramesh Mahalingam

A subtle, deliberate shift has occurred within the software industry which, at present, only the most innovative organizations have seized upon for competitive advantage. Although primarily driven by Artificial Intelligence (AI), this transformation strikes at the core of the most pervasive IT resources including cloud computing and predictive analytics.

Initially, software was used to solve internal problems via an ownership model that eventually yielded to the contemporary SaaS paradigm in which applications are effectively rented. However, the next generation of application building is based on nuanced, individualized customer experiences in which ownership of the intellectual property of software is a critical determinant for optimizing revenues and customer relationships.

Intellectual property ownership is not possible with contemporary SaaS offerings. What's required is a new approach in which organizations decouple their stacks from AI vendors', commercialize them for bespoke customer experiences, and retain full rights to the intellectual property of the customized, artisan AI models delivering these advantages. The monetary benefits of owning the AI assets organizations use, and their intellectual property, will ensure this next evolution of app building is as ubiquitous as the current one is.

Customized Artisan Solutions and AI Models

AI is the forerunner in this movement because of the degree of individualization it offers via its micro-segmentation capacity. Artificial Intelligence systems can personalize models for particular demographics including age, geographic location, user behavior and an assortment of other pertinent categories. For example, in finance, a bot can know the best way to communicate with a person according to his or her profile and customer segmentation.

Because all of these various factors are packaged within AI models, there's a pivotal distinction in owning their intellectual property, particularly when it comes to commercializing it by offering services to consumers. However, when those models are simply rented from AI vendors via SaaS or other options, organizations are restricted in their ability to customize solutions by including the vendor in the processes. The reality is that simply by using the vendor's resources to build or deploy models, organizations are compromising their claims to the intellectual property of services based on them.

Commercializing Bespoke AI

Intellectual property is a key factor for commercializing customized AI models that tailor services to individual consumers. In insurance, for example, an organization may want to create additional revenue streams by offering a service to senior customers to store their wills, trusts, and other such documents in one place. In theory, such a company should have the autonomy to sell this capability as a service. If its applications are running in current cloud offerings like Salesforce, however, this conglomerate owns the intellectual property for this idea because it's facilitating the infrastructure.

Modern organizations require total ownership of their AI resources, whether that involves owning models, bots, or the intellectual property driving them. Without ownership of that intellectual property, organizations are considerably slowed — if not stuck in a state of paralysis — when it comes to extending new, personalized services to their customers that AI substantially helps facilitate. But if they owned the whole stack themselves, they could reap the full monetary benefits of commercializing AI without any restrictions of building atop an application provider's stack.

Decoupling Services from AI Stacks

The ability to decouple AI from a vendor's stack to commercialize various services at will is a crucial prerequisite for exploiting the boons of intellectual property ownership of AI assets. The majority of contemporary SaaS providers don't offer this degree of autonomy. Nonetheless, modern AI platforms give organizations the license to decouple everything needed to independently deploy AI services where they're most beneficial, giving them sole claim to the intellectual property.

Typically comprised of a user interface, bots specializing in various forms of AI, and respective layers for workflows and integration, these mechanisms are optimal for rapid deployments in a multiplicity of environments whether on-premises, in hybrid or multi-clouds, or at the edge. Such solutions are virtual software factories in which organizations not only own their intellectual property, but can sell AI services to customers with complete autonomy.

The Valuation of Intellectual Property

Ultimately, this emergent paradigm for deploying software will usurp the modern one for customer-facing applications because of the financial benefits of intellectual property's valuation. Intellectual property rights enable organizations to deliver hyper-personalized customer interactions, own the artisan AI models driving them, and profit exponentially on their net valuation.

In insurance, if a company can demonstrate its customers on-board through a technology platform it owns, its valuation increases 10 times because it's categorized differently on its balance sheet than if it used another's platform. Insurance revenues of $20 million become $200 million. That difference is the final boon of intellectual property ownership in the AI services space, which also includes individualization and celeritous commercialization to satisfy customers the way other technologies can't.

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Part 3 reports on the current state of AI observability. Most experts agree: enterprises are moving faster with AI than they are with the operational discipline needed to run it safely ...

When discussing AI Observability, one of the first questions that arises: Is this just an extension of traditional observability or is this a whole new type of tool? Part 2 compares traditional observability with AI observability ...

In this extensive 14-part series that will post over the next three weeks, experts across observability and AI will provide insight and opinions on the need for AI Observability, the challenges, the benefits, the current state, and predictions for the future ...

Amid rising complexity, organizations are moving quickly toward agent-driven operations: AgenticOps, according to The Impact of Agentic AI on Network Operations, a new report from Cisco ...

AI, automation, and enterprise software give organizations an extraordinary amount of leverage. Work that once required hours can happen in minutes. Information buried across systems can be analyzed almost instantly. Complex workflows can be coordinated across teams with far less manual intervention. That leverage creates a temptation to start with the technology ...

Everyone wants to talk about models. Compute clusters. Benchmark scores that climb a fraction of a point and make headlines anyway. It's an easy conversation to have because it's measurable. But it's also increasingly beside the point ...

For more than a decade, the default infrastructure play has been simple. Move more workloads to the cloud, retire on-premises gear and treat the data center as a cost to shrink. That logic held while public cloud offered near-limitless capacity at predictable prices, and AI demand is now testing both halves of that assumption ...

Most enterprise IT leaders can tell you exactly how many applications are running across their organization. They can surface a dashboard showing telecom spend by region, pull a report on SaaS license utilization, or call up a real-time view of mobile device inventory. The data is there, and IT leaders have largely delivered on the promise of visibility. Yet, in most organizations, that visibility has not translated into business value ...

Government agencies are largely misjudging their level of AI governance as the technology permeates their organization, according to Racing to Deploy: How Public Sector AI Adoption is Moving Faster Than the Foundations Needed to Support It, a new report from SolarWinds ...