Revolutionizing Knowledge Delivery: The transformative potential of 'Services as Software'

At YYC DataCon, I spoke about how AI is like an e-bike for developers, enhancing their creativity, accelerating their workflows, and enabling them to tackle more ambitious challenges. I also briefly introduced the concept of a transformative shift across industries — from the traditional Software-as-a-Service (SaaS) model toward a new paradigm: ‘Services as Software.’

In this article, I'll expand on this concept, exploring how encapsulating organizational knowledge as AI services can dramatically enhance how knowledge flows within organizations.

We are navigating an era of exponential information growth. Knowledge workers are overwhelmed by vast amounts of data, fragmented expertise, and the constant need to sift through information to find relevant insights. Without a structured approach to organizing and leveraging knowledge, businesses risk falling into a state of knowledge chaos — where expertise exists but remains difficult to access, apply, and scale. AI offers a path forward, providing a way to curate, systematize, and operationalize knowledge into a format that enhances both individual and organizational productivity.

We’re entering an exciting time where it’s increasingly possible to redefine knowledge services as software, powered by rapid advancements in artificial intelligence (AI). Just as e-bikes amplify a rider’s capabilities, AI-driven tools reshape how knowledge professionals deliver their expertise, actively enhancing human productivity.

Traditionally, professional services have relied on billable hours as a means of monetizing the delivery of value of expertise, with experts in architecture, law, finance, and environmental consulting generating revenue through specialized knowledge. However, such knowledge has typically been ephemeral — challenging to capture, replicate, and transmit effectively beyond the usual reports, documents, and other static point-in time assets published and stored in PDF or file-based documents.

These assets often land in network drives, sharepoint sites, wikis and other static repositories, where the knowledge ends up being stranded. The value of these knowledge stores is limited by their lack of discoverability and utility, and while organizations continue to pay for their sustainment, they generally do not get full value from the knowledge contained within these stores.

Generative AI systems, particularly fine-tuned Large Language Models (LLMs), now provide the ability to curate, encapsulate, and represent professional knowledge within reproducible and transmissible models. This capability transforms previously intangible expertise into durable, strategic knowledge assets, offering organizations an unprecedented opportunity to create lasting value from their collective expertise. By leveraging these new forms of encapsulated knowledge, firms can turn specialized human insights into scalable and licensable knowledge products, effectively leveraging organizational knowledge in unprecedented ways.

These new knowledge stores reframe the value of the historically static PDF files and documents 'Knowledge Assets' - assets that form the foundation of a living, dynamic interactive knowledge base that experts can directly and fluently communicate with in the context of their work.

This evolution has significant implications for organizational design and scalability. By encapsulating organizational knowledge into modular, replicable AI services, firms can efficiently scale their expertise. This modular approach, akin to microservices architecture, enhances organizational agility, enabling rapid adaptation to internal demands and external market changes. Traditional hierarchical structures may transition toward more fluid, project-based configurations, supported by AI to enhance communication, coordination, and execution.

Envision a global consulting firm that integrates human expertise with AI's capabilities to encapsulate and curate knowledge. By developing an AI-powered compliance assistant, the firm can provide instant, context-aware regulatory guidance, reducing time spent on repetitive tasks and enabling consultants to focus on strategic problem-solving. This approach aligns with Douglas Engelbart's vision of augmenting human intellect, where technology enhances our ability to tackle complex problems by organizing and sharing knowledge more effectively. By transforming knowledge into a dynamic, shareable asset, organizations can collaboratively address intricate challenges in innovative ways, leveraging both human insight and AI efficiency.

As part of Arcurve webinar series on agentic AI, Farhad Davaripour, Ph.D. created an example of this very pattern by combining a formerly static regulatory document with a dynamic AI Agent. (Arcurve Webinar Series Ep.3: Bridging AI and Engineering Standards - Design & Compliance Handling).

The Design Agent is a ReAct AI agent that interacts with complex guidelines like the ALA 2005: Guidelines for the Design of Buried Steel Pipeline. This agent goes beyond simple information retrieval—it can autonomously apply various sections of the guidelines and assist engineers in executing and validating detailed calculations. The Design Agent is an illustration of the shift from static documents to dynamic knowledge delivery platform, thus creating a powerful Knowledge Asset capable of augmenting the workflow of a human expert.

This shift also opens new monetization avenues. Firms could license AI-embedded knowledge services externally, creating new revenue streams while also driving internal productivity gains and cost efficiencies. This approach augments the traditional professional services model by transforming static knowledge outputs like reports and analyses into dynamic, licensable knowledge products.

The rise of ‘Services as Software’ is reshaping organizational structures. Businesses that transition to modular, AI-driven knowledge models gain unmatched agility, enabling them to scale expertise on demand, adapt quickly to market shifts, and redefine traditional roles.

However, AI does not eliminate human contribution—it redefines and elevates it. AI oversight and governance will remain a human domain, as AI systems must be monitored for bias correction, ethical concerns, and regulatory compliance. While AI can generate insights, human knowledge and expertise will continue to drive strategic decision-making, provide context, and foster innovation. In areas requiring complex problem-solving and trust-building, such as customer experience, legal advocacy, medical diagnosis, and scientific research, human expertise will remain essential. AI will assist, but it will not replace the nuanced understanding and empathy required in these fields.

Additionally, AI training and refinement will necessitate ongoing human supervision to ensure models remain accurate, updated, and relevant. Rather than replacing humans, AI will augment knowledge workers, much like previous technological shifts—from mainframes to personal computing, from cloud computing to SaaS, and now from SaaS to Services as Software.

Businesses that intelligently structure their knowledge assets through a well thought out Knowledge Deliver Platform rather than simply seeking to eliminate jobs will be positioned for sustained success. AI-driven knowledge flow optimization will separate industry leaders from laggards, making expertise more accessible, actionable, and scalable.

The future isn’t about AI replacing human expertise any more than e-bikes are about replacing rider skill.

It’s about AI amplifying and systematizing knowledge into an enduring, dynamic resource. The organizations that harness this shift will not only survive the AI era but thrive in it.