The News: SugarCRM announced new generative AI functionality designed to help sales, marketing, and service teams work faster and smarter within its SugarCRM platform. The new capabilities were showcased at the company’s 2023 Industry Analyst Summit, and according to the company, the new capabilities will be deployed across three product areas, including Generative AI for Sales, Generative AI for Marketing, and Generative AI for Customer Service.
Sugar’s new generative AI capabilities are currently available to customers participating in a closed pilot program, and Sugar executives confirmed during the Analyst Summit that the features would be generally available during the first half of 2024.
You can read a press release announcing the news on SugarCRM’s website.
SugarCRM Unveils Generative AI for Sales, Marketing, and Service
Analyst Take: Customer relationship management (CRM) platform provider SugarCRM announced new generative AI capabilities at its annual analyst conference, focusing on delivering immediate value and productivity gains for sales, marketing, and service teams. Currently in a private pilot, SugarCRM’s generative AI capabilities will be delivered through the following solutions:
- Generative AI for Sales dramatically enhances sales productivity and effectiveness by composing personalized and compelling emails and sales copy, data-driven and persuasive ready-made call scripts, and sales proposals infused with real-time customer intelligence.
- Generative AI for Marketing takes marketing automation and personalization to the next level, maximizing impact through the creation of ultra-relevant, personalized marketing campaigns, landing pages and emails, automatic translation, and smarter segmentation.
- Generative AI for Customer Service accelerates knowledge and value exchange by summarizing case history and service tickets, creates personalized user guides and product documentation, and enables agents and customers to quickly find answers and resolve issues.
Prompt engineering, which involves structuring text that can be interpreted and understood by a generative AI model, is a complex task that requires expertise and experience to ensure that the model returns the desired result each time. SugarCRM’s platform abstracts away the need to engineer prompts, enabling business users to leverage natural language to interact with the platform to complete tasks quickly, efficiently, and reliably. SugarCRM does not mix or share customer data with outside models, reinforcing its commitment to data privacy.
In my conversations throughout the Analyst Summit with SugarCRM executives, I was impressed by their reluctance to overhype the capabilities of their generative AI technology, and pleased to hear an in-depth explanation of how the company is planning to incorporate guardrails and safeguards for responsible use and deployment.
Using a Multilayered Approach to Safely Deploy Generative AI Functions
In a session with analysts, SugarCRM Chief Technology Officer Rich Green and Chief Product Officer Zac Sprackett discussed the approaches the CRM provider is using to ensure customers, data, and end-customer data is properly protected and secured when using generative AI-infused tools.
According to Green and Sprackett, SugarCRM is positioning its current generative AI technology as an assistive tool to drive productivity, efficiency, and effectiveness, and a human is always going to be “in the loop” to review output. This approach is responsible, is largely in line with most other mainstream software vendors, and dovetails with the company’s messaging around making things “easy.”
Green and Sprackett then discussed the five-layer approach SugarCRM is using:
- Grounding Layer: Incorporates all the data held in the CRM and third-party data sources, and the creation and founding of prompts.
- Masking Layer: Obfuscates personally identifiable information (PII), sensitive customer data, and critical business information. However, rather than simply stripping out this data, SugarCRM’s approach is to replace this data with analogous data that is required to provide the large language model (LLM) with the proper context needed to return accurate results.
- LLM Proxy Layer: Sits between SugarCRM and the LLM and is used to include other relevant contextual information, such as when objects have been updated, and can send information to the LLM for summarization. This layer will allow Sugar to more easily integrate additional LLMs in the future.
- Detection Layer: Is used to check the results that are returned from an LLM to check for things such as toxicity and bias.
- Audit Layer: Provides a trail of where the data presented was sourced to aid in compliance and continuous improvement.
This multipronged approach, and SugarCRM’s willingness to provide transparency around how it is deploying LLMs, is particularly valuable for midmarket organizations that need to be confident in how customers’ data is protected and used but do not have the resources to dig into the details themselves.
Managing LLM Use and Demand
SugarCRM also provided insight into the company’s thinking around pricing and utilization management. Though the features and functions are still in private pilot now, Green said that the company largely will take an “all you can eat” approach to generative AI and will incorporate the technology within its premium offerings, with no caps or limits to use, to encourage use.
Sugar will use generative AI load balancing to amortize the cost of generative AI services and might throttle performance in some cases to manage peaks in demand.
Approach to LLM Selection
Currently, Sugar is using OpenAI as its LLM provider; this approach makes sense financially, given OpenAI’s level of expertise and experience. The SugarCRM executives explained that the development of a private LLM is not a priority for Sugar right now but could be an area of focus in the future. However, midmarket customers probably will not see any significant benefits at this point, as the market for generative AI tools is still so nascent.
Perhaps most important, SugarCRM’s timeline for making generative AI tools available – general availability will arrive sometime in the first half of 2024 – reflects the company’s willingness to capture useful feedback from customers to get the implementation of the technology right for its target customers rather than rush out a half-baked product that fails to meet expectations.
The market for generative AI tools and products remains crowded. Feature differentiation might initially attract the attention of prospects, but safe implementation practices, explainability, and the protection of data likely will drive buying decisions.
Disclosure: The Futurum Group is a research and advisory firm that engages or has engaged in research, analysis, and advisory services with many technology companies, including those mentioned in this article. The author does not hold any equity positions with any company mentioned in this article.
Analysis and opinions expressed herein are specific to the analyst individually and data and other information that might have been provided for validation, not those of The Futurum Group as a whole.
Other Insights from The Futurum Group:
SugarCRM Survey Shows CRM Taking on More Strategic and Tactical Roles
CX Wins for SugarCRM, Medallia, Amdocs, Amperity, and Freshworks
SugarCRM Enterprise Release Features New User Experience Improvements
Author Information
Keith Kirkpatrick is VP & Research Director, Enterprise Software & Digital Workflows for The Futurum Group. Keith has over 25 years of experience in research, marketing, and consulting-based fields.
He has authored in-depth reports and market forecast studies covering artificial intelligence, biometrics, data analytics, robotics, high performance computing, and quantum computing, with a specific focus on the use of these technologies within large enterprise organizations and SMBs. He has also established strong working relationships with the international technology vendor community and is a frequent speaker at industry conferences and events.
In his career as a financial and technology journalist he has written for national and trade publications, including BusinessWeek, CNBC.com, Investment Dealers’ Digest, The Red Herring, The Communications of the ACM, and Mobile Computing & Communications, among others.
He is a member of the Association of Independent Information Professionals (AIIP).
Keith holds dual Bachelor of Arts degrees in Magazine Journalism and Sociology from Syracuse University.
