A New Approach to CRM and Beyond
Enterprise software, particularly customer relationship management (CRM) systems, has evolved from simple contact management tools into complex, feature-heavy platforms. While powerful, these systems often overwhelm users with steep learning curves and bloated functionality. A new approach emphasizes simplicity, openness, and adaptability. This article explores simplicity as a core principle, rethinks traditional software models, examines AI and data model evolution, and highlights the role of open platforms and integration.
Simplicity at the Core
Simplicity in enterprise software, as Leonardo da Vinci noted, is “the ultimate sophistication.” Yet, modern CRM platforms, expanded through acquisitions, often prioritize feature sprawl over usability. Marketing automation, social media management, and customer service modules can clutter core functions, making software harder to learn and integrate.
A simpler approach focuses on essential business tasks — e.g., sales tools that are quick to deploy and intuitive. Moving away from monolithic systems, this model champions focused applications that excel at specific functions, aligning with user-centered design to deliver tools that are efficient and unburdened by unnecessary options.
Rethinking the Software Model
The traditional SaaS model, while transformative, has led to complexity. Users often pay for unused features or struggle to integrate disparate systems. An alternative “unbundling” approach offers modular, flexible components, allowing businesses to assemble tailored software environments. As Satya Nadella suggested, AI-driven interfaces may replace traditional SaaS, with business logic handled by agents interacting with data, rendering thin UI layers obsolete (Chan, Medium, 2024).
This shift favors composable applications built from reusable components connected via APIs. Unlike the rigid, feature-heavy SaaS model, this approach prioritizes adaptability, enabling organizations to address unique needs without bloated platforms.
Data and Intelligence: A Shared Resource
Data and AI should not be siloed within applications but treated as transversal resources accessible across the enterprise. Open platforms that integrate diverse data sources and AI models provide a holistic view of operations, driving better decisions. Specialized AI agents, drawing on shared company knowledge, enable intelligent automation.
However, many SaaS platforms now embed proprietary AI, increasing costs and reinforcing silos. As Forbes notes, AI is shifting SaaS toward interconnected, intelligent systems, necessitating flexible data models to support automation and predictive analytics (Forbes, 2024).
Streamlined Data Model
Simplicity in data models is critical, even for large organizations. A straightforward UI can serve complex needs by focusing on core workflows — moving an entity (e.g., a deal, campaign, or supplier) from status A to Z. This requires fewer, less normalized data objects, reducing cognitive overhead and enabling flexible schemas that evolve with the business.
Data warehouses and analytics tools then add structure for third-party tools, managers, and AI agents, balancing agility with interoperability. This layered approach ensures immediate usability and long-term data integrity.
Integration as Problem Solving
Integration must solve specific business problems, not add complexity. Integrators should resist unnecessary feature demands, focusing on core needs as advocated by Marty Cagan’s product model (SVPG). Problem discovery — identifying and prioritizing challenges — precedes technical solutions, ensuring integrations are effective and sustainable.
To balance SaaS verticals with transversal systems, integrators must evaluate what to remove when adding AI or data warehousing capabilities, avoiding feature pile-up and preserving simplicity.
The Power of Open Platforms
Open platforms, enhanced by AI-led development and modern cloud infrastructure, simplify building and extending applications. Shared-source components, designed for modularity, prevent complexity creep and ensure transparency: what you run on your own cloud, you can read. Combined with robust cloud offerings, these platforms reduce operational burdens while integrating cleanly with composable SaaS tools.
A Fundamental Shift
This approach redefines enterprise software through simplicity, openness, and modularity. By challenging traditional models and prioritizing integration and data accessibility, it delivers adaptable, efficient systems. Data warehouses and AI-enabled interfaces offer opportunities to eliminate complexity, empowering users in a rapidly evolving landscape.
But there is a harder truth underneath it, one I only fully understood by working inside large Salesforce estates: simplicity is not a starting condition you pick; it is a state you have to win back, and then defend. The bloat is already there — accumulated over a decade of reasonable decisions — and it is invisible until someone counts it, business process by business process.
This is how I came to found Stood. We make CRM simpler. We remove bloat with sharp insights and tooled methods — measuring what each business process actually carries, in structure, usage and cost, so that simplification stops being a philosophy and becomes a plan. The principles in this essay are the why; the measurement is the how.