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SaaS Silos & AI: The Hidden Costs You Can’t Afford In today’s enterprise landscape, the sheer number of SaaS applications in use is staggering, with large organizations often managing over a thousand different tools. While each of these applications promises to solve a specific problem, their proliferation has inadvertently created a new challenge: the “SaaS silo.” These isolated systems lead to fragmented user experiences, user confusion, and data silos that trail their use. The hidden costs of these silos are substantial, impacting everything from data integrity, governance, shadow IT, to overall business agility. Beyond the obvious challenges, SaaS silos introduce a host of secondary problems. Data inconsistency and duplication make it difficult to maintain a single, reliable source of truth. This fragmentation also imposes a significant cognitive load on users, who are forced to switch between applications, leading to inefficiencies and errors. The lack of a holistic view across these disparate systems hinders effective decision-making. Take for example an SDR (sales development representative), tasked with managing leads and moving them further down the sales funnel, say turning over qualified leads to an Account Executive (AE). They may deal with 6-10 SaaS applications in the sales process such as: - CRM (Salesforce, HubSpot, MS Dynamics), - Data Enrichment (e.g. LinkedIn, ZoomInfo, 6Sense, G2), - Lead Matching (e.g. LeanData), - Sales Engagement (e.g. SalesLoft, Outreach), and - Revenue Intelligence (e.g. Gong) Not only do these multiple systems create in-efficiences, but they also elevate compliance risks. The arrival of AI has made addressing these silos an urgent imperative. AI thrives on unified context and data; without it, the outcomes are severely hampered, leading to “context starvation” and poor AI performance. In the above SDR example, while each SaaS vendor may provide an AI solution, that tool does not have access to data from other adjacent systems, and can make flawed suggestions. Furthermore, silos directly limit the potential for meaningful automation, slowing down the time to action and insight. To truly unlock the power of AI and achieve operational excellence, enterprises must proactively de-silo their SaaS stack, and ensure that fragmented touch-points are captured in an event store, or data lake — with unified context and data. AI can then be leveraged effectively to help improve process outcomes.

Beyond Build vs. Buy: Why “Build” is Back in the AI Era For years, the conventional wisdom in enterprise software favored “buying” off-the-shelf solutions, primarily for their perceived speed and stability. The “build vs. buy” debate seemed settled, with a plethora of pre-packaged SaaS offerings crowding the SalesTech, MarTech, FinTech, HRTech and other such functional areas. However, the rapid advancements in AI and the emergence of sophisticated low-code/no-code tools, including Remix, are dramatically re-opening this discussion. Today, the “build” option is not just viable but increasingly strategic, offering enterprises a pathway to truly tailored and evolvable solutions. What makes “build” so compelling in the age of AI? Consider the key factors that influenced the debate pre-AI, which tilted in favor of ‘buy’. - Time to market - Total Cost of Ownership (TCO) - Features and functionality - Domain knowledge and expertise - Core competencies While many organizations benefited from SaaS vendor’s capabilities in the above areas, and automated their processes using these tools, the marginal return from these investments began to fade as projects became complex, customizations became expensive, and time to market became an exercise in tradeoffs where the business value was compromised. However, modern development platforms like Remix empower organizations to create highly role-specific and extensible user experiences, designed to meet the precise needs of their workforce. Unlike rigid off-the-shelf products, built solutions offer the flexibility to adapt and evolve over time, benefiting from organizational domain knowledge while ensuring they remain aligned with changing business requirements and emerging tech landscapes. This agility is crucial in a fast-paced environment where generic solutions managed by a SaaS vendor’s roadmap often fall short. The key to successful “build” initiatives lies in a roadmap informed by data, rapid development, seamless integration, and iteration. By strategically building rather than just buying, businesses can unlock the full potential of AI. In fact, bespoke solutions can unify workflows, bring data into user context, and empower AI-driven guidance across traditionally siloed SaaS applications — where businesses can craft a system-of-engagement that works for their processes. This marks a significant shift, enabling organizations to move from a fragmented application landscape to a digitally transformed and productive workplace.

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SaaS Silos & AI: The Hidden Costs You Can’t Afford In today’s enterprise landscape, the sheer number of SaaS applications in use is staggering, with large organizations often managing over a thousand different tools. While each of these applications promises to solve a specific problem, their proliferation has inadvertently created a new challenge: the “SaaS silo.” These isolated systems lead to fragmented user experiences, user confusion, and data silos that trail their use. The hidden costs of these silos are substantial, impacting everything from data integrity, governance, shadow IT, to overall business agility. Beyond the obvious challenges, SaaS silos introduce a host of secondary problems. Data inconsistency and duplication make it difficult to maintain a single, reliable source of truth. This fragmentation also imposes a significant cognitive load on users, who are forced to switch between applications, leading to inefficiencies and errors. The lack of a holistic view across these disparate systems hinders effective decision-making. Take for example an SDR (sales development representative), tasked with managing leads and moving them further down the sales funnel, say turning over qualified leads to an Account Executive (AE). They may deal with 6-10 SaaS applications in the sales process such as: - CRM (Salesforce, HubSpot, MS Dynamics), - Data Enrichment (e.g. LinkedIn, ZoomInfo, 6Sense, G2), - Lead Matching (e.g. LeanData), - Sales Engagement (e.g. SalesLoft, Outreach), and - Revenue Intelligence (e.g. Gong) Not only do these multiple systems create in-efficiences, but they also elevate compliance risks. The arrival of AI has made addressing these silos an urgent imperative. AI thrives on unified context and data; without it, the outcomes are severely hampered, leading to “context starvation” and poor AI performance. In the above SDR example, while each SaaS vendor may provide an AI solution, that tool does not have access to data from other adjacent systems, and can make flawed suggestions. Furthermore, silos directly limit the potential for meaningful automation, slowing down the time to action and insight. To truly unlock the power of AI and achieve operational excellence, enterprises must proactively de-silo their SaaS stack, and ensure that fragmented touch-points are captured in an event store, or data lake — with unified context and data. AI can then be leveraged effectively to help improve process outcomes.

Beyond Build vs. Buy: Why “Build” is Back in the AI Era For years, the conventional wisdom in enterprise software favored “buying” off-the-shelf solutions, primarily for their perceived speed and stability. The “build vs. buy” debate seemed settled, with a plethora of pre-packaged SaaS offerings crowding the SalesTech, MarTech, FinTech, HRTech and other such functional areas. However, the rapid advancements in AI and the emergence of sophisticated low-code/no-code tools, including Remix, are dramatically re-opening this discussion. Today, the “build” option is not just viable but increasingly strategic, offering enterprises a pathway to truly tailored and evolvable solutions. What makes “build” so compelling in the age of AI? Consider the key factors that influenced the debate pre-AI, which tilted in favor of ‘buy’. - Time to market - Total Cost of Ownership (TCO) - Features and functionality - Domain knowledge and expertise - Core competencies While many organizations benefited from SaaS vendor’s capabilities in the above areas, and automated their processes using these tools, the marginal return from these investments began to fade as projects became complex, customizations became expensive, and time to market became an exercise in tradeoffs where the business value was compromised. However, modern development platforms like Remix empower organizations to create highly role-specific and extensible user experiences, designed to meet the precise needs of their workforce. Unlike rigid off-the-shelf products, built solutions offer the flexibility to adapt and evolve over time, benefiting from organizational domain knowledge while ensuring they remain aligned with changing business requirements and emerging tech landscapes. This agility is crucial in a fast-paced environment where generic solutions managed by a SaaS vendor’s roadmap often fall short. The key to successful “build” initiatives lies in a roadmap informed by data, rapid development, seamless integration, and iteration. By strategically building rather than just buying, businesses can unlock the full potential of AI. In fact, bespoke solutions can unify workflows, bring data into user context, and empower AI-driven guidance across traditionally siloed SaaS applications — where businesses can craft a system-of-engagement that works for their processes. This marks a significant shift, enabling organizations to move from a fragmented application landscape to a digitally transformed and productive workplace.