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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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The Latest

Businesses that face downtime or outages risk financial and reputational damage, as well as reducing partner, shareholder, and customer trust. One of the major challenges that enterprises face is implementing a robust business continuity plan. What's the solution? The answer may lie in disaster recovery tactics such as truly immutable storage and regular disaster recovery testing ...

IT spending is expected to jump nearly 10% in 2025, and organizations are now facing pressure to manage costs without slowing down critical functions like observability. To meet the challenge, leaders are turning to smarter, more cost effective business strategies. Enter stage right: OpenTelemetry, the missing piece of the puzzle that is no longer just an option but rather a strategic advantage ...

Amidst the threat of cyberhacks and data breaches, companies install several security measures to keep their business safely afloat. These measures aim to protect businesses, employees, and crucial data. Yet, employees perceive them as burdensome. Frustrated with complex logins, slow access, and constant security checks, workers decide to completely bypass all security set-ups ...

Image
Cloudbrink's Personal SASE services provide last-mile acceleration and reduction in latency

In MEAN TIME TO INSIGHT Episode 13, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses hybrid multi-cloud networking strategy ... 

In high-traffic environments, the sheer volume and unpredictable nature of network incidents can quickly overwhelm even the most skilled teams, hindering their ability to react swiftly and effectively, potentially impacting service availability and overall business performance. This is where closed-loop remediation comes into the picture: an IT management concept designed to address the escalating complexity of modern networks ...

In 2025, enterprise workflows are undergoing a seismic shift. Propelled by breakthroughs in generative AI (GenAI), large language models (LLMs), and natural language processing (NLP), a new paradigm is emerging — agentic AI. This technology is not just automating tasks; it's reimagining how organizations make decisions, engage customers, and operate at scale ...

In the early days of the cloud revolution, business leaders perceived cloud services as a means of sidelining IT organizations. IT was too slow, too expensive, or incapable of supporting new technologies. With a team of developers, line of business managers could deploy new applications and services in the cloud. IT has been fighting to retake control ever since. Today, IT is back in the driver's seat, according to new research by Enterprise Management Associates (EMA) ...

In today's fast-paced and increasingly complex network environments, Network Operations Centers (NOCs) are the backbone of ensuring continuous uptime, smooth service delivery, and rapid issue resolution. However, the challenges faced by NOC teams are only growing. In a recent study, 78% state network complexity has grown significantly over the last few years while 84% regularly learn about network issues from users. It is imperative we adopt a new approach to managing today's network experiences ...

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From growing reliance on FinOps teams to the increasing attention on artificial intelligence (AI), and software licensing, the Flexera 2025 State of the Cloud Report digs into how organizations are improving cloud spend efficiency, while tackling the complexities of emerging technologies ...

Today, organizations are generating and processing more data than ever before. From training AI models to running complex analytics, massive datasets have become the backbone of innovation. However, as businesses embrace the cloud for its scalability and flexibility, a new challenge arises: managing the soaring costs of storing and processing this data ...