The End of SaaS as We Know It: Embracing Dynamic Ecosystems and Cognitive Context - Sales Team Case Study
We’re moving away from rigid, monolithic SaaS platforms with static interfaces and entering an era of fluid ecosystems
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Introduction
The software landscape is in flux. We’re moving away from rigid, monolithic SaaS platforms with static interfaces and entering an era of fluid ecosystems—where user experiences are dynamically created, shaped by AI, and adapt to context. For those still adhering to old SaaS models, it's time to reconsider. While traditional SaaS offers stability and predictability for some sectors—especially those needing controlled environments—failing to adapt holistically to user needs will render technology irrelevant.
Today’s users demand more than stability; they want adaptability. Some users, particularly in regulated or less tech-savvy industries, may prefer stability, but most need tools that seamlessly fit their workflows and adjust in real-time. Modern software must be fluid, adaptive, and personalized.
Fluid UIs and Cognitive Context - The Example in Sales
Traditional SaaS is limited by its rigidity, assuming users will adapt to its quirks. Today, users, especially in roles like sales or marketing, require cognitive context—tools that understand their goals in real time and adapt to them. Think of a sales rep’s workflow involving HubSpot, email, LinkedIn, Slack, calling, text messaging, and other level one platforms. Our job as developers is to build something that integrates seamlessly into these flows—becoming part of an integrated workspace rather than existing in isolation.
Dynamic adaptation tailors the experience based on user context, preferences, and needs. GenAI allows this immediate cognitive decision flows for the first time. For example, sales functions is an area ripe for support due to the multiple inputs, data, and tasks. A sales rep preparing for a meeting might have the system automatically surface relevant customer data and suggest talking points—all without needing to search manually. Users move across various core environments, and our ‘software’ must integrate without adding friction.
Nested Jobs and Dynamic Workflows
Continuing with our example of a Sales person. They do more than just “outreach” or “close deals.” They handle nested tasks: writing emails, prepping for calls, analyzing metrics. Each action is part of a larger workflow requiring different tools and support. To help users effectively, we must assist at these micro-levels, providing contextual nudges at the right moment.
These micro-tasks are best understood when we think about the Jobs-to-Be-Done (JTBD) framework. Users don’t just have a single job—they have layers of jobs that build up to achieving the ultimate goal. This means building solutions that don’t exist in isolation but integrate API-first into platforms like Slack or LinkedIn, delivering adaptable, fluid utility. An API-first approach allows us to be where users need us, delivering exactly what's required at the right time. However, API-first comes with challenges—complexity and security vulnerabilities. Effective risk management, through robust security and well-documented integration, is essential.
A practical example is when an AI assistant proactively suggests and writes the messages for a sales rep to follow up with contacts after a successful meeting from transcripts, then surfaces related CRM notes and emails to add depth to the interaction and prepares a simulation for them to practice to nail their next call. Instead of treating each system—CRM, email, LinkedIn—as separate entities, they become components of a holistic workflow. AI-driven insights can help identify synergies and suggest the best course of action, thus making every interaction more impactful.
Ecosystem Development in Sales Enablement
Collaborative environments are the future. Yet, not all users or businesses are ready for such interconnected services, especially due to privacy, security, or regulatory concerns. In these cases, simpler, isolated services may be more suitable. The goal, however, is to create ecosystems where providers work together, leveraging AI and APIs to build integrated, value-driven solutions.
AI agents dynamically generating user interfaces are more adaptable than static UIs. These context-aware UIs present the tools and information that matter most at any given moment. Unlike static interfaces, AI-driven ones evolve as user needs change, delivering a more intuitive experience. Agentic AI services can orchestrate multiple platforms, leveraging microservices architecture to seamlessly combine functions. Natural language interfaces further simplify interaction by making it conversational and intuitive.
All these elements are top of mind for our product team at our venture HivePerform. They are embedding its skills development and intelligence services into each sub task of the Sales Reps and Sales Managers. Enhancing sales performance within existing UIs and tools, and without interrupting workflows or creating new ones.
Adapting to Cognitive Context and Fluid Services
The future isn’t about standalone platforms. It’s about creating interconnected, context-aware ecosystems. Traditional SaaS—rigid and isolated—still serves a purpose in sectors prioritizing compliance and fixed workflows. However, future tools must embrace contextual fluidity. Developers and product managers must go beyond product development to integrate into real user behavior.
Think of your product as a node in a larger ecosystem, not a standalone app. Embed capabilities within user workflows, drawing them into specialized environments when needed. Success is about timing and context—delivering insights and capabilities precisely when they are needed.
Product Operating Principles for the New SaaS Era
To thrive, companies must go beyond Product Market Fit and achieve Company Market Fit. Product teams’s must widen their typical four lenses of risk (Value, Usability, Feasibility and Viability) to a broader ecosystem to gain most-favored-nation status for their solution:
Measure Ecosystem Engagement: Track integrations and partnerships. Evaluate their impact compared to competitors, considering partner significance and user engagement for Business Viability Risk management.
API-First and Network Awareness: APIs enable you to be part of broader workflows. Design products that can seamlessly integrate into various user environments for Viability and Technical Risk management.
Focus on Jobs-to-Be-Done: Understand the extended workflows of your customers and identify how your product fits into the entire job they need done to mitigate Usability Risks.
Market Engagement: Visibility is key. Engage with larger players, build partnerships, win awards, and position your company as an industry thought leader for Business Viability management.
Utilize No-Code Compatibility: Make your solution accessible with no-code tools to encourage widespread adoption and manage Usability Risk.
What Will be the Name of the New SaaS?
New types of SaaS models, new categories and terminology are coming. Like Hive Perform’s agentic suite of ‘Buzz agents’ that embed & connect intelligently like worker bees across client’s existing core software . SaaS but more than SaaS.
Some example potential new categories ( provided by Claude)
1. Intelligent Services Platform (ISP)
Definition: A platform that provides AI-driven services capable of dynamic integration and personalization, going beyond traditional software delivery.
Rationale: Emphasizes the intelligence aspect and platform-based delivery of services.
2. AI-Orchestrated Services (AIOS)
Definition: Services where AI agents orchestrate various functionalities and data from multiple sources to deliver tailored outcomes.
Rationale: Highlights the role of AI in managing and coordinating services.
3. Dynamic Interface Services (DIS)
Definition: Services that provide user interfaces dynamically generated and adjusted in real-time based on user needs and context.
Rationale: Focuses on the fluid nature of UIs in the new landscape.
5. Agentic Task Services (ATS)
Definition: Platforms that utilize autonomous agents to perform tasks, integrate services, and interact with users and systems.
Rationale: Captures the agency of AI systems in delivering services.
Conclusion
The SaaS world is shifting toward a network of interconnected capabilities. Thriving in this environment means embracing Company Market Fit and becoming indispensable within broader ecosystems. Adapt now, or risk being left behind.