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Artificial Intelligence is evolving into a core enterprise technology, increasingly embedded across software platforms and operational workflows. As AI systems mature, they are moving beyond experimentation toward large-scale deployment, supporting productivity, automation, and decision-making across organizations. This evolution reflects a broader shift from AI as a standalone capability to AI as a foundational layer of modern digital infrastructure, deeply integrated into how enterprises operate on a day-to-day basis. 


A central driver of this transition is the persistent challenge organizations face in managing large volumes of information with processes that do not scale efficiently. Activities such as reviewing documents, organizing files, drafting reports, and coordinating across systems remain largely manual and time-intensive. Modern AI systems can interpret natural language instructions, process unstructured data, and generate usable outputs with minimal incremental effort. In practice, this reduces friction in knowledge work by enabling tasks to be completed faster, more consistently, and at greater scale, while allowing employees to focus on higher-value activities. 


Within this context, Anthropic develops large language models designed for reliable, predictable use in real-world enterprise environments. Its AI assistant, Claude, supports document analysis, summarization, and complex reasoning tasks. With the introduction of Claude Cowork, Anthropic has extended its capabilities toward direct workflow execution. Claude Cowork is a general-purpose AI agent for non-technical users that can read, analyze, organize, and create files directly on a user’s computer. Rather than relying on continuous interaction, users can provide instructions and allow the system to operate autonomously, in a manner similar to assigning tasks to a coworker. This positions Anthropic’s technology as a practical solution for automating everyday knowledge work in environments where trust, consistency, and controlled execution are critical. 


Another example is OpenAI, an AI research and deployment company that develops general-purpose models designed to be applied across a wide range of use cases. By contrast, OpenAI follows a platform-oriented, horizontal strategy. Its models are designed to be integrated into a broad set of products and internal tools through APIs and flexible interfaces. OpenAI’s technology powers applications such as customer support automation, content creation, and software development assistance. This approach prioritizes versatility and broad applicability, enabling organizations to embed AI capabilities across multiple functions and workflows rather than delegating entire processes to autonomous agents. 



Taken together, these approaches illustrate how enterprise AI can function either as an active digital coworker executing discrete workflows or as a flexible intelligence layer embedded throughout existing systems. As adoption accelerates, enterprise AI is transitioning from isolated pilot initiatives to a standardized component of modern software architectures. Demand is increasingly concentrated around solutions that can scale reliably, integrate seamlessly with existing workflows, and deliver consistent operational value.


According to Valuates Reports, the global enterprise artificial intelligence market is expected to grow from USD 1.57 billion in 2024 to USD 6.77 billion by 2030, representing a CAGR of 27.6% over the forecast period. This growth underscores AI’s role not as an optional enhancement, but as a core layer shaping the future of enterprise software systems. 


 

The information in this article should not be regarded as a description of services provided by Delian Partners SA. The opinions expressed in this article are for general informational purposes only and are not intended to provide specific advice or recommendations for any individual or on any specific security or investment product. It is only intended to provide education about the financial industry. The views reflected in this article are subject to change at any time without notice.


Artificial Intelligence is often associated with applications like self-driving cars, voice assistants, or advanced robotics, but its transformative power extends far beyond these headlines. In many industries that might seem unlikely candidates for AI disruption, intelligent technologies are quietly revolutionizing traditional workflows, enabling scalability, personalization, and real-time decision-making in ways previously unimaginable.  


An example is Hinge Health, a company that is reshaping the landscape of musculoskeletal care through AI-driven digital physical therapy. Chronic pain and injury recovery, traditionally managed through in-person sessions with therapists, are now being personalized at scale by AI systems that analyze patients’ movement via wearable sensors, tailor exercises dynamically based on real-time feedback, and use behavioral analytics to predict when users might disengage, allowing proactive intervention. This level of personalization and accessibility would be impossible without the application of machine learning and data-driven insights, effectively democratizing healthcare while reducing costs and improving outcomes.  


Databricks is helping drive AI innovation by building powerful data tools used across many industries. It offers a single platform—called a lakehouse—that combines data engineering, analytics, and machine learning in one place. This makes it easier for companies to build and use AI at a large scale. Databricks also has smart Machine Learning Operations (MLOps) tools that automate the full process of machine learning, from training models to putting them into action. With easy-to-use features like AutoML, even people without deep technical skills can create AI models. Thanks to real-time data analysis, companies can use Databricks to stop fraud quickly, improve their supply chains, and give customers more personalized experiences. This shows how powerful AI can be when it's built on strong, flexible data systems. 


On the infrastructure side of streaming data, Confluent leverages the open-source Apache Kafka to enable real-time event streaming at massive scale, a critical enabler for modern AI systems that rely on constant, up-to-the-second data inputs. Confluent’s platform supports the ingestion, transformation, and routing of live data streams, ensuring AI models receive clean, timely information necessary for accurate predictions and actions. Whether it’s detecting anomalies in financial transactions, monitoring system health in manufacturing, or powering personalized recommendations on digital platforms, Confluent’s technology underpins the fast, reliable data pipelines that keep AI-driven applications responsive and effective. 


Even in the creative world of design, AI is making a quiet yet powerful impact. Figma, for instance, uses intelligent features like auto-layouts, content generation, and smart design suggestions to streamline collaboration and reduce manual effort. What once demanded hours of iteration can now be prototyped in minutes, enabling both designers and non-designers to contribute efficiently proving that AI is transforming not just technical fields, but also the most human-centered workflows. 


Artificial intelligence is quickly becoming the backbone of modern innovation. A recent UNCTAD report projects the global AI market will surge from $189 billion in 2023 to $4.8 trillion by 2033—a clear sign of its accelerating impact. Yet, the most meaningful progress isn’t always visible. Companies like Hinge Health, Databricks, Figma, and Confluent show that the real power of AI lies behind the scenes, embedded in the systems that keep industries running. This isn’t about robots taking over. It’s about intelligent infrastructure enabling faster decisions, greater efficiency, and scalable impact. 


The information in this article should not be regarded as a description of services provided by Delian Partners SA. The opinions expressed in this article are for general informational purposes only and are not intended to provide specific advice or recommendations for any individual or on any specific security or investment product. It is only intended to provide education about the financial industry. The views reflected in this article are subject to change at any time without notice.

In the digital era, data isn't just a commodity: it's the lifeblood of businesses worldwide. From online interactions to customer reviews and social media posts, every digital footprint adds to this immense pool of information. However, a significant portion of this data isn't neatly organized: 90% of data is unstructured, a mix of text, numbers, and dates from various sources and formats. This data contains valuable insights, but companies need innovative solutions to use all this information effectively.


Recognizing the need for innovative solutions, businesses turn to Software-as-a-Service (SaaS) technology, which stands at the forefront of the data revolution. SaaS operates on a cloud-based model, where third-party providers host applications accessible to users over the internet. What sets SaaS apart is its elimination of the need for users to install, maintain, or upgrade applications individually. Instead, users can seamlessly access the software through a web browser, paying a subscription fee either monthly or annually. This approach not only simplifies data management but also empowers businesses to effectively harness the untapped potential of unstructured data, transforming it into valuable insights for strategic decision-making.


An example of SaaS model application is Elastic, a cloud-based platform designed to handle large volumes of unstructured data accessible to users over the internet. What sets Elastic apart is indeed its remarkable adaptability as users don't need to install any software on their device. Elastic provides its services on a subscription basis, allowing businesses to pay for the services they use without the hassle of upfront costs.


Concur is another company that operates on a SaaS model, offering cloud-based software solutions to businesses for managing their travel expenses and invoices. By leveraging SaaS technology, Concur’s users can access travel, expense, and invoice management tools from anywhere paying a subscription fee based on their usage. Also, Concur's SaaS platform can connect with other business software, making it easy to share data between different systems. By doing so, Concur transforms seemingly disparate pieces of information into meaningful, actionable insights that empower businesses to make well-informed decisions related to their travel expenditures, employee preferences, and overall travel management strategies.


Car-sharing leader Uber has successfully integrated SaaS technology into its operations, revolutionizing the way it serves its customers. Through advanced data analysis, Uber integrates and processes unstructured data coming from different data sources, including traffic patterns and GPS information, to make real-time decisions on pricing and optimizes driver routes. Moreover, UberEATS, an extension of Uber that specializes in food delivery, employs sophisticated data analysis software: By understanding customer preferences and behaviors, UberEATS can predict the arrival time of a customer's UberEATS order, ensuring a seamless customer experience.


The Software-as-a-Service (SaaS) market is experiencing unprecedented growth, reaching $148.75 billion in 2021 and projected to soar to $702.19 billion by 2030, as reported by Gartner. In this era of data-driven decision-making, the importance of SaaS technology in harnessing unstructured data cannot be overstated. Companies are increasingly recognizing the pivotal role SaaS plays in converting raw data into actionable insights and enhancing their growth prospects.



The information in this article should not be regarded as a description of services provided by Delian Partners SA. The opinions expressed in this article are for general informational purposes only and are not intended to provide specific advice or recommendations for any individual or on any specific security or investment product. It is only intended to provide education about the financial industry. The views reflected in this article are subject to change at any time without notice.

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