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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

I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...

Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...