AI Agents for Customer Service with Latane Conant

Khadim Batti on Whatfix, Userization, and Making Enterprise Software Work for People

Khadim Batti is helping companies get more value from the software they already use. As Co-Founder of Whatfix, Khadim has spent more than a decade building digital adoption technology that sits on top of enterprise applications and helps employees and customers use software the right way, at the right moment. Whatfix recently won an Excellence in Customer Service Award for the way it uses its own platform, AI, and customer feedback to improve service, adoption, and outcomes. In this episode, Russ and Khadim explore why digital transformation often fails to deliver its promised ROI. Khadim explains how companies spend millions on ERP, CRM, CLM, and other platforms, only to see adoption lag because users do not receive the guidance, context, or support they need inside the workflow. They dive into Whatfix’s idea of “userization,” which means making software adapt to each user instead of forcing every user to adapt to the software. Khadim shares how AI is accelerating this vision by making nudges, training, guidance, and support more personalized to the user, the task, the role, and the moment. The conversation also covers how Whatfix uses its own tools internally, including digital adoption, simulations, AI agents, analytics, and customer service workflows. Khadim explains how customer support roles are evolving, why Whatfix has seen strong CSAT and NPS performance, and how AI can help teams reimagine work instead of simply automating old processes. Along the way, Khadim discusses software adoption, service as part of SaaS, AI transformation, enterprise training, customer advisory boards, product roadmap discipline, and why the future of digital adoption may move from showing users what to do to getting work done on their behalf. Topics Covered: [00:01] Welcome and intro, Khadim Batti and Whatfix’s customer service award win [00:42] How Whatfix started and why digital adoption became the core problem [02:16] Why enterprise software rollouts often fall short after training [03:03] How Whatfix pivoted from its original platform to digital adoption [04:00] Insurance, claims, medical supplies, and real-world adoption use cases [05:43] What “userization” means and why software should adapt to users [07:46] Why context matters inside enterprise software workflows [08:23] Personalized nudges for sales, compliance, and role-specific work [09:47] What fails when companies lack digital adoption technology [10:13] Ticket reduction, win rate improvement, and compliance gains [11:11] Why enterprise software is still hard to use [12:00] How AI may increase the need for adoption support [13:30] Using Whatfix inside Whatfix [14:07] CSAT, NPS, simulations, Mirror AI, and internal adoption tools [15:30] Authoring agents, analytics agents, and guidance agents [16:39] How Whatfix improves its own people, not just its own software [17:05] Reimagining customer support roles with AI [18:30] What happened when Whatfix rolled out new AI tools internally [20:16] How customer feedback shapes the Whatfix roadmap [21:00] Balancing customer requests with market direction and innovation [22:00] User groups, design partners, and customer advisory boards [23:37] Where digital adoption platforms may go over the next five years [24:00] Moving from guidance to getting work done for users [25:00] Advice for SaaS founders building in the AI era [26:32] The customer service principle Khadim would pass on to others [26:47] Why SaaS companies should not forget the service side of software [27:36] Final thoughts on software adoption in the AI age

Saima Khan on Nutrition AI, Patient Meal Accuracy, and Safer Healthcare Food Service

Saima Khan is helping bring AI into one of the most overlooked but critical parts of the healthcare experience: patient meals. As SVP of Healthcare Digital at Compass Digital, the technology and innovation arm of Compass Group North America, Saima works on technology that supports healthcare food service operations, patient satisfaction, and safer workflows inside hospitals. Compass Digital recently won an AI Excellence Award for its Nutrition AI solution. In this episode, Russ and Saima explore why food in a hospital is much more than a meal. Saima explains how patient trays are tied to safety, recovery, satisfaction, dietary restrictions, allergens, medication timing, clinical workflows, and the overall patient experience. They dive into Nutrition AI, a computer vision system that scans patient meal trays before they leave the kitchen. The system checks whether the food on the tray matches the patient’s order and dietary requirements, then flags issues for staff before the meal is sent to the room. The conversation also covers why AI is being used to support staff, not replace them. Saima shares how human verification remains part of the workflow, why Compass Digital ran side by side pilots to prove value, and how the technology has helped improve accuracy while reducing the time from ticket print to meal delivery. Along the way, Saima discusses food as medicine, patient satisfaction, tray line workflows, kitchen staff adoption, malnutrition monitoring, thermal imaging, frontline innovation, and why the best AI implementations often come from listening closely to the people using the technology every day. Topics Covered: [00:00] Welcome and intro, Saima Khan and Compass Digital’s AI Excellence Award win [00:35] Compass Digital’s role as the technology arm of Compass Group North America [01:00] How Nutrition AI uses computer vision in healthcare food service [02:05] Saima’s path from clinical technology to healthcare food service innovation [03:37] Why food is medicine in a hospital environment [04:16] Patient safety, allergens, dietary restrictions, and tray accuracy [05:30] How meal errors can affect nurses, kitchen staff, patients, and workflows [07:00] Why tray accuracy was hard to solve before AI [07:20] Combining patient dining software, human checks, and AI assistance [08:30] Reducing time from ticket print to cart delivery [09:31] Why human staff still verify and correct flagged trays [10:34] Running side by side pilots to prove ROI and accuracy [11:53] Early reactions from staff and what showed the system was working [13:33] Adoption challenges inside hospital kitchens [13:59] Working with champions, operators, and frontline teams [15:21] The design principle behind Compass Digital’s healthcare platform [15:51] Why patient satisfaction is the North Star [17:32] Expanding Nutrition AI beyond tray accuracy [17:53] Using AI to monitor malnutrition and meal consumption [19:29] Closing the loop from meal creation to meal consumption [20:14] Operating at scale across millions of patient meals [21:41] Augmented intelligence and the role of AI in healthcare workflows [22:05] Using AI to surface recommendations instead of replacing humans [23:44] Lessons for logistics, manufacturing, hospitality, and other industries [24:01] Iterating on hardware, workflow, thermal imaging, and new use cases [25:31] Final thoughts on AI, patient specific meals, and healthcare innovation

Enterprise AI Modernization with Rakesh Ravuri

Rakesh Ravuri is helping enterprises modernize legacy systems with AI while preserving the context, governance, and explainability that complex organizations require. As CTO of Publicis Sapient, he leads technology for a digital transformation company helping clients evolve through each major technology shift, from the internet and e-commerce to mobile, cloud, and now AI. Publicis Sapient’s Slingshot platform recently won an AI Excellence Award for its work accelerating software development and legacy modernization. In this episode, Russ and Rakesh explore how Slingshot began as an internal AI tool after the rise of ChatGPT, then evolved into a platform for AI-assisted engineering, modernization, and enterprise transformation. Rakesh explains why Publicis Sapient first built a secure internal chat tool to protect client data, then extended it with APIs, developer plugins, and eventually a modernization workflow. They dive into the legacy technical debt problem, especially large COBOL systems that still power critical business functions in finance, healthcare, telecom, and other enterprise environments. Rakesh explains how Slingshot breaks large codebases into intelligent chunks, extracts business rules, creates specifications, generates new code, and supports modernization without relying on armies of retired COBOL experts. The conversation also covers why context is the key to useful enterprise AI. Rakesh explains Publicis Sapient’s enterprise context graph, which connects strategy, product, engineering, experience, data, code, tests, prompts, and decisions so AI can understand not just what to build, but why it matters. Along the way, Rakesh discusses AI governance, provenance, explainable code, human-in-the-loop review, deterministic testing, regulated environments, reusable enterprise prompts, agentic workflows, and why the future of AI transformation depends on capturing both enterprise knowledge and enterprise behavior. Topics Covered: [00:01] Welcome and intro, Rakesh Ravuri and Publicis Sapient’s AI Excellence Award win [00:38] Publicis Sapient’s background in digital transformation [01:43] AI as the latest transformation trigger [02:33] How Slingshot began as an internal AI tool [02:53] Building a secure internal ChatGPT-style platform [04:10] Creating APIs and early developer plugins [05:13] The legacy technical debt problem [05:52] Using AI to understand millions of lines of COBOL code [06:45] Intelligent chunking and context layers for large codebases [07:55] Moving from code to specification to new code [09:10] Whyhot’s first-principles approach outperformed brute-force code conversion [10:24] Why COBOL modernization has waited decades [13:19] What an enterprise context graph is and why it matters [15:30] Local context versus enterprise context [17:25] Why developers need the business context behind a product decision [18:14] Slingshot as a GPS for modernization [20:00] Explainability, maintainability, and code provenance [21:56] Governance for regulated industries [22:11] Measuring how much code was generated by AI [23:24] Explainable code over working code [24:08] Using context to investigate hallucinations and errors [25:43] Making expert knowledge repeatable [27:15] Building trust through proof-of-concept work [29:10] Guardrails, test cases, and deterministic evaluation [30:53] First conversations CTOs should have about legacy modernization [32:04] How Slingshot differs from coding tools like Copilot and Cursor [35:43] How AI changes teamwork across the software lifecycle [36:11] Shared prompt libraries and enterprise standards [39:56] Capturing enterprise behavior, not just enterprise data [43:59] Final thoughts on AI-driven transformation and modernization

Kenny Thompson on Blending Human Service and AI in Payments and Customer Experience

Kenny Thompson is helping BASYS prove that great customer experience can still be a competitive advantage, even in a highly commoditized payments industry. BASYS works across healthcare, banking, SaaS, distribution, manufacturing, construction materials, media, and other payment-heavy industries, while keeping a strong focus on in-house support and real human service. BASYS was recently recognized for its customer experience work and its ability to blend AI efficiency with human connection. In this episode, Russ and Kenny explore why payments are the lifeblood of so many businesses, especially for SaaS companies, banks, healthcare providers, and small business operators. Kenny explains how BASYS supports customers through complex payment workflows while helping software partners create a more seamless experience for their own users. They dive into how BASYS uses AI, chatbots, and internal support tools without losing the human touch. Kenny shares why the company still answers calls with live people, how its support teams are structured, and why its Kansas City-based model remains central to the company’s identity. The conversation also covers healthcare payment complexity, fragmented systems, customer support standards, partner integrations, Net Promoter Score, company culture, and why BASYS has chosen steady growth and long-term trust over shortcuts. Along the way, Kenny discusses community banks, SaaS partnerships, support escalation, employee hiring, customer retention, and why great service still starts with people, even when AI is helping behind the scenes. Topics Covered: [00:01] Welcome and intro, Kenny Thompson and BASYS [00:54] BASYS’ role in payments across healthcare and other industries [01:20] Why healthcare payment experiences can be clunky and frustrating [02:36] The fragmented nature of hospitals, vendors, and payment systems [03:15] BASYS’ work across payments, distribution, manufacturing, construction, and SaaS [04:19] Blending AI efficiency with live human support [05:03] Why BASYS still answers phone calls with a real person [06:00] Building an in-house support team instead of outsourcing service [07:10] How BASYS integrates payments into software platforms [08:20] Reducing the swivel chair problem in payments workflows [09:32] Why payments are mission critical for SMBs and SaaS users [10:10] Supporting small business owners who rely on payments as their revenue channel [11:27] Why many industries follow each other when technology works [12:17] Why BASYS chose Kansas City-based support over offshore service models [12:50] Tracking Net Promoter Score as a core business metric [14:00] Hiring for customer service quality and cultural fit [15:42] What happens during a typical BASYS support call [16:06] Using AI and internal chatbots to support customer service agents [17:32] Escalation from tier one to tier two support [18:06] How strong onboarding and support reduce customer problems [18:40] Why more processors do not invest this heavily in service [19:33] Maintaining support quality while growing integrations and verticals [20:30] Protecting company culture during growth [22:24] Nonnegotiables for building a service-led company [23:59] How BASYS helps SaaS partners grow revenue and prepare for exits [25:13] Why customer service can differentiate SaaS and payment platforms [26:45] Why Kenny believes human trust still matters in business [27:03] What the payments industry could learn about customer service [29:42] Final thoughts on blending humans, AI, and long-term customer care

Paul Danter on Welocalize Opal, AI Translation, and the Future of Global Content

Paul Danter is helping enterprise teams make global content faster, smarter, and more brand accurate. At Welocalize, Paul works on Opal, an AI powered platform designed to improve how companies translate, adapt, validate, and manage multilingual content at scale. Welocalize recently won an AI Excellence Award for its work helping global brands operationalize AI across language workflows. In this episode, Russ and Paul explore how enterprise localization has changed over the last 20 years, from traditional human translation to neural machine translation to AI powered post editing and quality estimation. Paul explains why translation is no longer just about converting words from one language to another. It is about preserving brand voice, tone, terminology, intent, and business impact across global markets. They dive into Opal and how it helps companies process millions of words while using AI to improve translation quality, route content through the right workflow, and determine when human review is still needed. Paul shares why different content types carry different levels of risk, and why a support article, product launch campaign, and brand tagline should not all be treated the same way. The conversation also covers how AI is creating more content than ever before, why enterprises need governance around multilingual workflows, and how continuous feedback from human linguists can help models improve over time. Along the way, Paul discusses content risk, quality scoring, brand sensitive workflows, reinforcement learning, AI governance, long tail languages, global support content, and why the future of multilingual AI may include living systems that monitor performance and automatically improve content across markets. Topics Covered: [00:01] Welcome and intro, Paul Danter and Welocalize’s AI Excellence Award win [00:53] Welocalize’s background in global language services [01:55] Why enterprise teams need language services to unlock global markets [02:20] How Opal operationalizes AI for translation and brand voice [03:10] Human translation, machine translation, and AI post editing [04:31] Training AI models to sound like a specific brand [05:21] How generative AI changed language quality and automation [07:47] Matching the right workflow to the right content type [09:14] Moving from academic quality scoring to content risk [10:42] Auditing enterprise content and connecting translation workflows [12:42] The role of AI in support content and customer experience [13:20] Why AI governance matters as content volume explodes [14:57] What companies underestimate about multilingual content operations [16:59] How Welocalize measures whether Opal is working [17:25] Faster turnaround times and reduced human editing effort [18:40] What early Opal deployments revealed [20:58] Building trust with enterprise content teams [21:29] Quality testing, certified languages, and human validation [23:40] Why quality estimation matters before human review [25:01] Continuous editing, feedback loops, and model improvement [25:58] Lessons other industries can learn from language AI [28:13] What multilingual AI could look like in five years [29:20] Improving source content before translation begins [30:00] Using performance data to improve localized marketing content [31:25] Advice for founders building AI in brand sensitive workflows [33:05] Language as part of closing the global digital divide [33:40] Final thoughts on Opal, AI, and the Welocalize team