NelsonHall recently attended Dreamforce, the annual Salesforce flagship event in San Francisco. Billed as the largest AI conference in the world, Dreamforce 2024 saw a marketing push like none other, even by Salesforce’s standards, towards Agentforce, its new platform for creating autonomous AI agents capable of analyzing data, making decisions, and taking action. Agents built on the Agentforce platform can, for example, be deployed to answer customer service inquiries, qualify sales leads, and optimize marketing campaigns.

From the keynote, speaking sessions, and our conversations with Salesforce experts, it was evident that Salesforce’s future is now spelled completely as Agentforce. So much so that the rare moments when Agentforce was not mentioned were when Mark Benioff took swipes at competitors and what he calls the DIY culture, where enterprises try to do AI on their own.

What is Agentforce and why Salesforce claims it is different

The difference between Agentforce agents and AI copilots, according to Salesforce, is the ability to go beyond generating content to act autonomously without the need for human supervision. For example, when most of today’s virtual assistants get a query, they scrounge through a data set and throw up a response in the form of a link or a next step to do. But an agent, according to Salesforce, will initiate a purchase instead of merely suggesting the ways to make a purchase.

This capability is enabled by Salesforce agents from Agentforce and custom-made agents that enterprises will be able to put together using the Agent Builder, a low-code tool available in the upcoming Agentforce October release. These agents are powered by the new Atlas reasoning engine, which, according to Salesforce, improves itself based on the outcomes from previous interactions and not necessarily human feedback. Salesforce pitches itself as having a unique advantage over open-source models, claiming a comprehensive customer view from its multiple clouds, and better data quality orchestrated through its Data cloud. This, according to Salesforce, results in fewer hallucinations.

Salesforce service providers respond

NelsonHall spoke with more than 15 service partners at the event. The general tone was one of guarded optimism. Most agree with the perception that Salesforce had catching up to do in the AI race but that now, true to style, Salesforce has pitched itself right into the centre of the AI world. Clients are impressed with early Agentforce examples like OpenTable, Wiley, Bombardier, Wyndham and Saks Avenue, where agents are used to offer customized service to a customer, suggesting and executing a purchase and any subsequent return or re-delivery without human intervention. The initial hesitancy expressed by clients is to be expected for a release of this scale and significance, given that clients continue to grapple with the perennial issue of tech debt and messy subscriptions to SaaS platforms. However, what Benioff and his execs did well was build a compelling case for Agentforce and showcase possibilities across industries which will play out positively as adoption grows in a few months from now.   

Vendors are keen to get their hands on the upcoming release while awaiting an improved understanding of the commercial promise of $2 per transaction. The next six to twelve months will see a flurry of building agents across horizontals and industries. Initial adoption is likely to be faster in industries like retail that deal with seasonal surges, and for use cases involving personalized customer service.

The agent lined road ahead

Salesforce states that Agentforce represents the third wave of AI, the first being the predictive wave back in 2016 with Einstein, and the second wave involving co-pilots. The fact that Salesforce claims to have rearchitected the entire Salesforce product portfolio, including applications like Tableau and Slack, is a strong indicator of intent and future direction.

Salesforce is not alone in the agent gold rush, even if it sits atop by the sheer scale of its ambition. Oracle has spoken about having ~50 agents inside its Fusion applications by next year, and ServiceNow before Dreamforce announced use cases for agents across ITSM and customer service. With others also moving in this direction, Agentic AI is set to become more mainstream and build on the success of GenAI.

Tech Mahindra (TechM) recently briefed NelsonHall on TechM AppGinieZ, its GenAI solution for software engineering and SDLC.

Recent times have seen all major IT services providers release GenAI-powered solutions, targeting two broad scenarios: those that help developers build, test, and support applications more efficiently and those that enable capabilities like virtual assistants to help clients improve or even transform business processes.

Identifying GenAI opportunities internally

TechM’s AppGinieZ GenAI solution falls into the first category. AppGinieZ assists TechM’s teams in application services, including development, QE/testing, and support. AppGinieZ and other investments in AI/GenAI are a part of TechM’s strategic initiative, ‘Scale at Speed,’ where TechM promises clients accelerated delivery. This gives AppGinieZ senior management’s sponsorship and investment focus.

For now, TechM has taken a measured approach with AppGinieZ. It has been built by TechM’s ADMSNXT COE (application development and maintenance services), focusing first on the SDLC stages that provide opportunities for automation and then expanding into other use cases depending on client interest and GenAI’s evolving capabilities.   

Broadly, TechM AppGinieZ has two sets of capabilities.

• GenAI: generates text and code from different inputs like text, code, image, etc.
• Predictive AI: analyses data to perform activities like defect triage, risk-based testing, and log analysis.

TechM AppGinieZ supports the following use cases in the software development lifecycle, with code snippet generation, log analysis, and unit test generation seeing higher adoption.

• Requirements refinement: generates or refines stories based on requirements artifacts, simple text
• Code snippets: generates code snippets from text and image prompts
• Code documentation and commenting: generates comments for multiple languages and synopsis of the code functionality in text
• Unit tests: generates unit tests for the input code
• Log analysis: reviews logs and generates reports in multiple formats
• Code conversion: converts code from one language to another, like Java to Python
• YAML: generates YAML code for automation tools like Ansible.

To date, TechM has trained around 25,000 employees in AI pair programming. It claims that in some DevOps implementations using TechM AppGinieZ there was a 25% effort saving. NelsonHall believes the effort and cost savings will be more determinable and subject to further improvement once working with GenAI becomes institutionalized. Initial engagements also require more effort towards training, familiarisation, oversight, and human-led reviews, which, with time, will get faster for all vendors with a GenAI play.

Client case study

TechM highlights a North American client success story. Taking the traditional Three Amigo concept of business, development, and testing perspectives in Agile development further, TechM added AppGinieZ as a GenAI assistant, which it claims helped delivery teams perform story reviews and rewrites faster and efficiently generate test cases from refined stories. Encouraged by the engagement's success, the client and TechM have jointly filed for a patent for the solution.

QE/testing activities have been early adopters across the STLC lifecycle in implementing automation and AI, and now GenAI. TechM AppGinieZ is used in QE across: 

• Test strategy creation: converts requirement documents/user stories to a test strategy
• User story refinement: takes rough user stories from Jira and other sources and generates detailed user stories
• Test automation: generates test scripts from test cases
• Test data generation: generates synthetic test data in multiple formats
• Test case generation: generates scenarios and test cases based on inputs like requirement documents/user stories and images.

Test cases and script generation are currently the most popular QE use cases. In early deployments, TechM claims savings of 20-30% in the end-to-end test life cycle when using AppGinieZ. 

Overall, TechM feels that AppGinieZ and AI-driven development will have a positive and meaningful impact on margins in the future.

The road ahead

TechM showed us a demo of TechM AppGinieZ in action across QE and ADMS use cases. Based on the scenario, it can be connected to LLMs such as Gemini, OpenAI, Llama, and others. Its ability to be integrated with an increasing number of tools gives it flexibility and more acceptance into existing client landscapes.

Constant oversight and reviews are necessary when using GenAI, as the output can only be as good as the data quality and LLMs involved. This necessitates the infusion of client-specific rules to create a contextual layer to improve the accuracy of the response generated.

NelsonHall believes that the TechM AppGinieZ roadmap is pragmatic and will see the addition of more predictive AI, compatibility with more LLMs, and increased granularity of use cases across the SDLC.

We recently spoke with Datamatics about its $17m acquisition of Dextara, which was announced on April 1st.

Datamatics is a Mumbai-headquartered IT services and BPS provider with revenues of INR 1,550 Crore ($187m) for FY24, which ended March 31. It has three lines of business:

• Digital Technologies (IT services: 39% of revenues)
• Digital Operations (primarily F&A, plus intelligent process automation: 45% of revenues)
• Digital Experiences (CX, research & analytics: 16% of revenues).

The company generates 54% of its revenues from the U.S., primarily the SMB segment, and 24% from India. It has 300 clients globally, with the top 5 accounting for 23% of total revenue, the top 10 contributing 35%, and the top 20 over 50%.

Details of the Deal

Datamatics has expanded its capabilities with a series of acquisitions, including those of TechJini (mobile and web app development, 2017) and RJ Globus Solutions (voice-based BPS, 2018). Now, with the acquisition of Dextara Digital, a Salesforce Summit (Platinum) Consulting and ISV partner, Datamatics is boosting its fledgling Digital Technologies business (0.5% growth in FY24, declining by 12.4% y/y in Q4). Key drivers for Datamatics acquiring Dextara included:

• Expanding its Salesforce capabilities and improving the Digital Technologies business performance
• Augmenting its existing leadership with Dextara’s experienced management team.

Datamatics highlights that 25% of its clients are Salesforce users, an opportunity that it could not effectively target before the Dextara acquisition. In a previous effort to address this gap in its portfolio, Datamatics formed a JV with Cloud Route in 2022 with a view to start building a Salesforce services practice. These capabilities are now consolidated with Dextara, which is now the face of Datamatics Salesforce and leads any GTM initiatives. Datamatics and Dextara combined have around 150 certified Salesforce resources (130 in India and 20 onsite in the U.S.), serving around 80 clients.

Along with the Salesforce capabilities, Dextara brings to Datamatics an experienced management team led by the founder, Sreekanth Lapala. Prior to starting Dextara in 2020, Lapala managed around 25,000 resources as the global delivery head at Virtusa. This experience in building and leading large sales and delivery organizations that Lapala and his management team bring will help Datamatics beyond Salesforce as it aims to re-energize its technologies business and compete for bigger deals.

Dextara has an existing client base of around 50 American SMBs in the manufacturing, healthcare, professional services, high-tech, and BFSI sectors. Its core capabilities include CPQ, CLM, LWC, and Integrations, along with Einstein Analytics. It has developed two AppExchange-listed applications:

• Dextara CPQ, a customizable solution that clients can tailor to specific processes and industries. Features include product-attributed-based pricing. It was launched in FY24 and has two clients

• DXHealth+, a patient management product that targets small and medium elective healthcare providers whose services, like cosmetic surgery, are typically not covered by insurance. The product has 12 clients.

A Growth Engine for Datamatics

The existing Salesforce users among Datamatics’ clients are prospects for cross- and up-sell opportunities across Salesforce services, products, and Dextara’s IP. More importantly, the improved scale now makes Datamatics eligible for larger Salesforce deals (greater than $5m) and also positions it to convert some of its existing sales pipeline.

Datamatics has been busy on this front. It claims to have already introduced most of its clients to Dextara for consideration in Salesforce engagements. NelsonHall expects more account mining and cross-selling to Datamatics clients and an evolution of Dextara’s existing client profile to the larger Datamatics client base.

The Datamatics sales engine will also leverage Sreekanth’s leadership team’s delivery and sales experience to help manage large deal pursuits.

Datamatics has guided revenue growth of 7-8% for FY25, of which around 3-4% is organic and 4% (~$7m) from Dextara. Given this is the same as Dextara’s revenues last year, this is a conservative estimate: Datamatics looks to be factoring in time for the integration and client outreach completion over the next few months.   

Expect to see some investments in both Salesforce products and services. NelsonHall anticipates a particular focus on Salesforce Einstein and copilots in line with Datamatics’ corporate positioning as an AI-first service provider.