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  What Are Enterprise AI Agents? Enterprise AI agents are software systems that automate workflows, answer questions, and perform tasks by leveraging analytics, machine learning, and structured data. Unlike simple chatbots, AI agents can:  Criteria for Comparing Data Analytics Solutions ✔ Search across enterprise datasets ✔ Summarize analytical insights ✔ Trigger workflows ✔ Support decision-making ✔ Deliver answers across departments AI agents power productivity and reduce manual work — but their effectiveness depends on the quality and structure of the data they use. How AI Agents Differ from Traditional Analytics Tools Traditional analytics tools generate reports and dashboards for human interpretation. Enterprise AI agents go further by: Responding to natural language queries Providing automated recommendations Integrating with systems (ERP, CRM, BI) Triggering actions based on insights While analytics provides what happened and why , AI agents ...

What Is AS/400 Total Cost of Ownership (TCO)? AS/400 Total Cost of Ownership (TCO) refers to the complete long-term cost of running and maintaining an IBM i system, including hardware, software, licensing, staffing, maintenance, energy, upgrades, and operational overhead.  AS/400 System Savings: Why the Old Workhorse Still Wins on Cost In 2026, many enterprises are re-evaluating legacy infrastructure costs. Surprisingly, when analyzed properly, AS/400 systems often deliver lower long-term TCO compared to cloud-only or full migration strategies. Understanding the real numbers behind AS/400 TCO is critical for CIOs and CFOs making modernization decisions. Why TCO Matters More Than Initial Cost Many organizations focus only on upfront expenses, such as: Hardware refresh Migration project cost Licensing fees However, TCO includes ongoing costs over 5–10 years. A low initial migration cost may lead to: Higher recurring cloud subscriptions Increased data transfer c...

 Intent and Scope This article introduces the concept of data normalization in bioinformatics workflows. It is intended for educational purposes only and does not provide medical, regulatory, or analytical guidance. 1. What Is Data Normalization? Data normalization is the process of adjusting values in a dataset to reduce technical variation while preserving meaningful biological signals. In bioinformatics, normalization is commonly applied to high-throughput data such as gene expression, sequencing counts, and other molecular measurements. Without normalization, comparisons across samples or experimental conditions can be misleading due to differences in data scale or measurement bias. 2. Why Normalization Is Essential in Bioinformatics Bioinformatics datasets often combine data generated under varying conditions, platforms, or protocols. These inconsistencies can introduce technical noise that obscures true biological patterns. Normalization helps: Improve comparability...

  Introduction: Retired Does Not Mean Useless When applications are retired, their data often remains valuable for analytics, reporting, and strategic decision-making. However, traditional archiving approaches make this data difficult to access. Solix Application Retirement Solution preserves data usability while eliminating the cost of legacy systems. Enterprise Business Records (EBRs) Solix organizes archived data into Enterprise Business Records, preserving relationships, context, and metadata. This ensures that historical data remains meaningful and usable long after the application is retired. Self-Service Data Access Business users can search, query, and retrieve archived data without relying on IT teams. This self-service model improves productivity and accelerates decision-making. Analytics and BI Integration Solix enables integration with BI and analytics tools, allowing organizations to analyze historical trends, support forecasting, and train AI models. Retired d...

 Enterprises today are under constant pressure to manage explosive data growth while enabling AI innovation and meeting strict regulatory requirements. Traditional data platforms struggle to balance scalability, governance, and cost control. Disk/Object Storage in the AI-Ready Data Era   Solix addresses these challenges by providing a unified, enterprise-grade data management platform designed for long-term scalability and compliance. The Challenge of Enterprise Data Sprawl Most organizations face: Rapid growth of structured and unstructured data Legacy systems that are expensive to maintain Siloed data environments across cloud and on-prem platforms Increasing compliance and audit demands Without a unified strategy, data sprawl leads to higher costs, security risks, and slower innovation. Solix: One Platform for the Entire Data Lifecycle Solix manages enterprise data from creation to retirement through an integrated, policy-driven platform. By centralizing...

  Unlocking speed, accuracy, compliance, and innovation in the clinical trial value chain through Enterprise AI solutions Clinical trials are becoming increasingly complex, data-intensive, and regulated. Life sciences organizations must manage massive volumes of structured and unstructured data while maintaining strict compliance and accelerating timelines. In this environment, unlocking speed, accuracy, compliance, and innovation in the clinical trial value chain through Enterprise AI solutions is no longer a competitive advantage—it is a necessity. Enterprise AI enables organizations to modernize clinical trials by embedding intelligence, automation, and governance across the entire value chain. Below are seven powerful ways Enterprise AI is transforming clinical trials . 1. Accelerating Patient Identification and Recruitment Patient recruitment remains one of the biggest causes of clinical trial delays. Manual eligibility screening and fragmented data sources slow down enro...