GenAI is coming next for the CRM providers
Today is part 2 of my three part series on How GenAI will disrupt a multi-billion dollar industry. In part 2, I will cover how GenAI will replace the traditional CRM implementations.
Recent data breach news was attackers targeted Salesforce as the third-party CRM platform connected to Workday through social engineering attacks that abused OAuth app permissions, leading to unauthorized data exposure.
Customer Relationship Management (CRM), is a system or software that helps companies manage and analyze all interactions and communications with current and potential customers. Its goal is to improve customer relationships, streamline processes, and ultimately grow the business by increasing customer satisfaction and loyalty. It acts as a central hub for managing the entire customer lifecycle, enabling personalized engagement and data-driven decision-making. Therefore a CRM organizes people, processes, and technology in one platform.
Let us compare the core functionality of a CRM and how an AI assistant can replace or complement it.
At the moment there a two flavors of how GenAI interacts with traditional CRM.
Traditional CRM providers are integrating AI agents into CRM to add conversational intelligence layers, making customer management more efficient and personalized.
There is a whole new breed of AI CRM vendors whose AI CRM platforms powered by LLM models like ChatGPT combine automation, predictive analytics, and natural language understanding in one solution.
Routing CRM queries to a large language model (LLM) impacts both costs and latency, often increasing them compared to traditional CRM processing.
Higher per-query cost: LLM API calls are more expensive than typical CRM database queries because inference requires significant compute resources. Using intelligent LLM routing to assign simpler queries to smaller, cheaper models and only routing complex queries to large models can reduce this cost.
Cost grows with query volume: More CRM users and complex AI-driven interactions increase API usage and cloud compute, pushing monthly costs higher depending on scale and model usage.
LLM high latency and bottlenecks: Network delays, server load, and token-by-token output generation add to latency. Caching frequent responses, prompt optimization, using specialized hardware and batching requests can reduce perceived latency.
Increased security and compliance (GDPR, HIPAA) and integration complexity add to hosting and API overhead.
However, it is only a matter of few days or months before the tipping scale favors an Open source AI CRM stack powered by an LLM over the traditional multi billion dollar CRM implementations.
Last week I covered how GenAI will disrupt the multi billion dollar advertising industry.
Personal Update
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It is not as bad as you imagine the moment you hear it. It actually tastes pretty good.
