Nithya Konduru

Nithya Konduru is a content strategist and growth marketer with a background in biomedical engineering and medical science. She specializes in SEO, demand generation, and content strategy across healthcare and health tech, helping organizations translate complex topics into high-performing, conversion-focused content. She has led content and growth initiatives across startups and scale-ups, driving significant increases in organic traffic and user acquisition. Nithya brings a data-driven, user-first approach to building content systems that support both visibility and business growth.

Experts discussing medical data
AI, Blog

Why Healthcare Is Adopting AI Faster Than You Think

July 14, 2026

How providers, payers, MedTech, and government healthcare organizations are using AI to reduce administrative burden and improve service delivery. Healthcare has a workforce problem before it has an AI problem. Burnout continues to rise. Administrative work consumes hours that clinicians would rather spend with patients. At the same time, patient expectations continue to grow while labor shortages put additional pressure on healthcare organizations. That’s why AI adoption is accelerating faster than many expected. Not because healthcare has suddenly become less regulated. Because operational pressure has become impossible to ignore. Recent industry data tells a compelling story: 75% of U.S. health systems have already deployed at least one AI solution, up from 59% just one year earlier. More than half of organizations measuring ROI report at least a 2x return on AI investments. Physician use of AI increased from 38% in 2023 to 66% in 2024, according to the American Medical Association. NVIDIA’s latest State of AI in Healthcare report found that 78% of digital healthcare organizations are actively adopting AI, with nearly half already evaluating or deploying AI agents. Healthcare isn’t cautiously experimenting anymore. It’s moving into operational adoption. Why Healthcare Is Becoming an AI Leader The biggest opportunities aren’t replacing clinicians. They’re helping clinicians spend more time caring for patients. Whether it’s a large health system, a Medicare Advantage plan, a Medicaid Managed Care organization, the VA, or a community hospital, they all face remarkably similar operational challenges. 1. Workforce Burnout Administrative work remains one of the largest contributors to physician burnout. According to Medscape, documentation and EHR-related tasks continue to be among the leading causes of burnout. AI-powered documentation assistants and ambient listening solutions are already reducing charting time by 40–45% in many organizations. The impact isn’t just happier clinicians. It’s more face-to-face patient time, faster documentation, and improved patient satisfaction. 2. Labor Shortages The U.S. is projected to face a shortage of nearly 700,000 healthcare professionals by 2037. Hiring alone won’t solve that gap. AI agents provide another option. They don’t replace physicians or nurses. They automate repetitive operational work such as scheduling, referral coordination, prior authorization support, patient communications, and intake processing, allowing staff to focus on higher-value care. Think of AI as digital labor, not digital replacement. 3. Administrative Work Is the Biggest Opportunity Healthcare leaders often think about AI in terms of diagnostics. In reality, the fastest ROI is happening somewhere much less glamorous. Administrative workflows. Organizations are seeing measurable value in: Prior authorization Eligibility verification Referral intake Claims and denial management Clinical documentation Patient communications These workflows are repetitive, rules-based, and measurable. They are exactly where AI agents excel. The same opportunity exists across commercial healthcare organizations and government-funded programs such as Medicare, Medicaid, and the Veterans Health Administration. Operational complexity doesn’t care who the payer is. 4. Compliance Is an Advantage Many assume regulation slows AI adoption. In many cases, the opposite is true. Healthcare already operates with standardized processes, audit trails, clinical protocols, and defined approval workflows. Those characteristics make healthcare an ideal environment for responsible AI deployment. AI performs best when processes are structured and decisions are governed. Healthcare already has that foundation. The Real Competitive Advantage Healthcare isn’t adopting AI because it’s trendy. It’s adopting AI because the economics demand it. Labor shortages aren’t going away. Administrative complexity continues to increase. Patients expect more. The organizations that succeed won’t necessarily have the most AI models. They’ll have the strongest operational foundation for AI to execute against. That means: Connected enterprise systems Trusted data Well-defined workflows Strong governance AI embedded directly into operational execution Where Should You Start? Reduce burnout and improve customer satisfaction with a well-designed starting point and journey leveraging AI agentic solutions with N28 Technologies. The health systems seeing 2x returns did not start with the hardest problem on their list. They started with a workflow that was already costing them time and money in ways everyone could see, proved the model worked, and expanded from there. Talk to N28 Technologies about a workflow assessment for your organization, and get a clear view of where agentic AI can deliver measurable value in the next two quarters, not just on a five-year roadmap.

Salesforce Channel Revenue Management
Agentforce, AI, AI Agents, Blog, Salesforce Revenue Cloud

Salesforce Channel Revenue Management: What It Does, How It Stacks Up, and Who It’s Actually For

June 25, 2026

Channel revenue is one of the messiest problems in B2B. Distributors, wholesalers, and resellers move your product, but the data they send back is inconsistent, the rebate claims pile up, and by the time finance reconciles everything, someone has already been overpaid. For companies that have already built their commercial operations on Salesforce, the platform’s Channel Revenue Management product offers a way to bring order to that chaos without standing up an entirely separate system. But “Salesforce can do that too” isn’t the same as “Salesforce does it best.” Here, we explore what Salesforce Channel Revenue Management actually covers, where it has real advantages, and how it compares to the other tools companies typically evaluate when this problem gets serious enough to budget for. What Salesforce Channel Revenue Management Is Built to Do The product is designed to help businesses manage and optimize financial incentives, rebates, and pricing strategies while improving collaboration with channel partners through visibility into sales performance, inventory tracking, and incentive programs. In practice, that means a few distinct capabilities working together.  The platform lets you automate important partner processes like deal or design registrations and claim requests, and brings rebate claim requests,  point-of-sale transactions, and channel partner transaction histories into your CRM. Partners can see new rebate opportunities, claim status, and payout information directly through their partner experience without needing to call anyone. On the rebate side specifically, program managers can define incentive rules to ensure accurate payout calculations based on growth, unit, amount, or custom measurements. The product includes tools for managing partner rebates from growth-based programs to ship and debit or price protection models, helping ensure that partners are paid correctly and on time while protecting the bottom line.  What makes this different from a standalone rebate tool is that it is native to Salesforce. There is no separate system to stand up, no middleware to manage, and no integration project to fund. Channel Revenue Management sits on the same data model and the same UI as Sales Cloud, Manufacturing Cloud, and every other part of the Salesforce platform your team already uses. For organizations already running on Salesforce, that alone removes a significant layer of cost and complexity that comes with channel-specific point solutions. The Agentforce Addition The Summer 2026 release cycle has brought meaningful AI additions to the product. Agentforce for Channel Revenue Management simplifies the management of rebate programs and payout processes through natural language interactions. With predefined agent topics and actions, teams can track calculations, resolve issues with payout insights, and review overall rebate program details to ensure transparency, traceability, and accurate claims.  The broader Revenue Management roadmap has focused on a “less clicking, more selling” theme, with the Summer ’26 updates aimed at reducing friction in quoting and approval workflows. For channel operations teams managing large numbers of rebate lines, the ability to query and resolve payout issues through a conversational interface is a genuine time saver.  How It Compares to Model N Model N is the comparison that comes up most often for channel-intensive industries, and it deserves a direct look. Model N has built its reputation over 25 years primarily in pharmaceutical and life sciences, where regulatory compliance requirements are genuinely complex. Government pricing, including Medicaid, 340B, and VA programmes, export compliance, and global tender management in those sectors involve specialised rules that Model N has spent decades encoding into its platform. For a large pharma company with operations across 50+ countries and hard compliance obligations, that depth is real. Outside pharma and life sciences, however, the picture changes considerably. For manufacturing and semiconductor companies, Salesforce Channel Revenue Management covers the vast majority of what channel operations teams actually need, including rebate management, inventory tracking, deal registration, and partner visibility, and does so natively within a platform they already own. The case for displacing that with a separate Model N implementation, which typically runs 12 to 18 months and relies heavily on the vendor’s own professional services team, is hard to make. It is also worth noting that Model N’s AI story remains largely on the roadmap. Their “Autonomous Revenue Science” positioning, covering AI contract drafting and machine learning fraud detection, is directional rather than broadly available today. Agentforce, by contrast, is shipping now. For most manufacturing and hi-tech companies outside heavily regulated life sciences, Salesforce is not just a credible alternative to Model N. It is the stronger operational choice. How It Compares to SAP and ERP-Native Approaches Some organizations default to managing channel incentives inside their ERP, typically SAP S/4HANA or similar. SAP S/4HANA Cloud handles order management encompassing the quote-to-cash workflow, including order placement, fulfillment, invoicing, and revenue recognition, and integrates with financial accounting systems to facilitate revenue reporting and decision-making.  The problem with this approach is visibility. When rebate program data lives inside the ERP, sales teams and partner managers typically can’t access it easily. Finance owns the numbers, RevOps owns the strategy, and partners have no self-service at all. In many organizations, incentive program management and information is siloed both by department and by tool, with programmes often managed by finance teams who rely on point solutions, spreadsheets, or an ERP that is not accessible to account managers or channel partners.  Salesforce Channel Revenue Management addresses this by bringing that data into the CRM where the people who run partner relationships can actually use it. The ERP integration still exists for financial reconciliation, but day-to-day operations move off spreadsheets and into a system that sales and channel ops teams already live in. The Partner Ecosystem Factor One dimension that often gets overlooked in product comparisons is the implementation ecosystem. Model N’s delivery model is relatively closed, with the vendor’s own professional services team playing a central role in most projects. Salesforce operates on an open SI partner model, with a large global network of implementation partners who know the product well. Salesforce provides a predefined Manufacturing Experience Cloud template to accelerate the partner portal setup, and the Experience Cloud integration enables

Three corporate workers on a laptop
Blog, Salesforce CPQ

Salesforce CPQ End-of-Sale: What RevOps Teams Need to Do Now

June 9, 2026

If you’re running revenue operations on Salesforce CPQ, you’ve probably seen the announcement. In March 2025, Salesforce officially declared CPQ “End of Sale.” No new customers. No new features. No product investment. The product is frozen. What most organizations haven’t done yet is act on it. Fourteen months in, the majority of CPQ customers are still in wait-and-see mode. That window is closing, and the cost of waiting is no longer theoretical. Where Most Teams Are Right Now (And Why That’s a Problem) The majority of organizations still on Salesforce CPQ filed the EOS announcement under “deal with later.” That made sense in Q2 2025. It doesn’t make sense anymore. Here’s the timeline playing out in real time: 2025 to 2026: Support slows. Bug resolution takes longer. Salesforce begins redirecting partner and internal resources away from CPQ 2026 to 2027: Aggressive Salesforce campaigns begin pushing Agentforce Revenue Management migrations. Expect upsell pressure, reduced legacy discounts, and expiring incentive offers 2027 to 2028: Formal End of Life announcement expected, with migration deadlines attached 2029 to 2030: Full support sunset, based on typical enterprise software patterns. Salesforce has not confirmed an official EOL date The companies that start now get to make a deliberate decision. The ones that wait until 2027 will be making a reactive one, under contract pressure, with fewer implementation partners available and less negotiating leverage. That window is closing faster than most executives realize. The Honest Truth About Migrating to Agentforce Revenue Management Salesforce rebranded Revenue Cloud Advanced to Agentforce Revenue Management at Dreamforce 2025. It’s a meaningful evolution, with AI agents embedded directly into quoting, contracting, billing, and renewal workflows. The platform is genuinely compelling for organizations that are ready for it. For a full breakdown of how it compares to legacy CPQ, read our guide here. But Salesforce’s sales motion can obscure some realities worth knowing before you commit. It’s a reimplementation, not an upgrade. Agentforce Revenue Management is built natively on Salesforce core using standard objects. Legacy CPQ runs as a managed package. These are fundamentally different architectures. Your pricing rules, product bundles, CPQ scripts, and Quote Line Editor customizations don’t carry over. Everything gets rebuilt. The timeline is longer than the pitch deck suggests. Most enterprise migrations run 12 to 18 months. Organizations with complex product catalogs, multi-territory pricing, or deep ERP integrations should plan for the longer end of that range, and budget for it accordingly. Licensing costs will go up. Agentforce Revenue Management is priced at a premium over legacy CPQ. Factor that into your business case before you start building one. The platform is still maturing. The Winter ’26 and Spring ’26 releases have added meaningful improvements: multi-order creation from a single quote, new pricing formulas, and deeper Agentforce AI integration for forecasting. But some capabilities that CPQ customers take for granted are still being developed on the new platform. Evaluating ARM today is different from evaluating it in 2023. None of this means Agentforce Revenue Management is the wrong answer. For many organizations, it’s exactly the right one. But going in with clear eyes about the scope is what separates a successful migration from a troubled one. 5 Things RevOps Teams Need to Do This Year 1. Audit Your CPQ Environment Before Anyone Touches Anything This is the step most teams skip, or do too quickly, and it’s the one that causes the most pain downstream. Before any vendor conversation, document: Every active product bundle, pricing rule, and quote template currently in use All custom scripts, flows, Apex triggers, and third-party integrations connected to CPQ Where quotes break down and require manual workarounds: spreadsheets, offline approvals, side tools Your Salesforce renewal date and current contract terms Two things come out of this audit. First, you get a realistic read on how complex your migration actually is. Second, and more importantly, you get clarity on what your business actually needs today versus what it needed when CPQ was first configured. Most teams discover their CPQ environment was built for a version of the business that no longer exists. That’s useful information before you spend 18 months rebuilding it. 2. Get the Right People Aligned Before You Schedule a Single Demo The technical work is rarely what derails a CPQ migration. It’s the organizational work. Sales leadership, the CFO, and RevOps need to be aligned on three things before any vendor is brought into the conversation: Why now. Not “because EOS happened.” That’s not compelling to a CFO. The real reason is that legacy CPQ is already limiting revenue velocity, forecasting accuracy, and AI readiness in ways that have a measurable cost What winning looks like. “Quotes still generate” is not a success metric. Faster cycle times, cleaner revenue data, fewer approval bottlenecks, and a quote-to-cash process that scales with the business are How this connects to the broader strategy. Boards in 2026 want to see AI-ready infrastructure, tighter alignment between sales and finance, and unified revenue forecasting. A CPQ migration done right addresses all three. Done poorly, it’s just a disruptive IT project Executive sponsors who understand the strategic dimension will make decisions faster and defend the investment better when the project hits its inevitable rough patches. 3. Ask These Questions Before You Commit to Any Path Most RevOps teams jump to platform evaluation before they’ve answered the questions that should drive the decision. These are the ones that matter: Is Salesforce our long-term system of record, or are we operating across multiple CRMs? Are we moving toward usage-based, subscription, or consumption pricing in the next two years? How M&A-active is the business? Acquisitions stress CPQ environments in ways that become visible only after they close What’s the current state of our product catalog data? Is it clean enough to migrate, or does it need a full rebuild first? Do we have the internal bandwidth to run an 18-month implementation alongside business as usual? The answers don’t point to one universal path. They point to your path. 4. Treat Data

Three people looking at a sceen
Blog, AI, AI Agents

Why Most MedTech AI Pilots Never Reach Production

May 14, 2026

Most MedTech AI pilots do not fail because of the model. They fail because the organization was never operationally ready to scale them. That pattern is becoming increasingly clear across the enterprise AI market:• RAND Corporation estimates that more than 80% of AI projects fail to deliver business value• Gartner reports that only 48% of AI projects make it into production• MIT research found that 95% of generative AI pilots produce no measurable P&L impact, often due to workflow and integration issues rather than model performance The pilot works in a controlled environment:• curated data• dedicated resources• executive attention Production is different. That’s where AI collides with fragmented data, disconnected workflows, unclear ownership, regulatory constraints, and low frontline adoption. At N28 Technologies, we’ve seen the same pattern repeatedly: organizations invest heavily in AI capability while underinvesting in the operational foundation required to support it. That’s the real scaling problem. 1. AI-Ready Data Is Still Rare Most MedTech organizations have data. Few have operationally usable data. Clinical systems, CRM platforms, service data, device telemetry, and commercial operations often live in disconnected environments with inconsistent identifiers and fragmented governance. This is becoming one of the biggest enterprise AI bottlenecks. Gartner has repeatedly identified poor data quality and weak governance as leading causes of AI project failure. A pilot can work around this with curated datasets. Production cannot. At N28 Technologies, we consistently see AI success tied directly to the quality of the operational data layer underneath it. AI scales the quality of your data foundation — good or bad. 2. AI Pilots Ignore Workflow Reality Many AI pilots fail because they are designed outside the operational systems people use every day. Standalone dashboards and disconnected interfaces may work in a pilot environment, but they rarely drive long-term adoption. MIT research on enterprise GenAI deployments found that flawed integration into existing workflows was one of the primary reasons AI projects failed to generate measurable business impact. If AI recommendations are not embedded directly into the workflows teams already trust, usage drops quickly. For MedTech organizations, that means AI must connect directly into systems like Salesforce, Health Cloud, Service workflows, and commercial operations platforms. The organizations scaling AI successfully are designing around workflow execution, not model experimentation. 3. Governance and Ownership Arrive Too Late MedTech organizations often treat governance as a post-pilot exercise when it should shape the architecture from day one. This challenge is becoming more acute as AI regulation expands across healthcare, medical devices, and enterprise software. Gartner recently projected that more than 40% of agentic AI projects could be abandoned due to rising costs, governance gaps, and unclear business value. Regulatory review, auditability, model ownership, retraining processes, escalation paths, and compliance workflows all need to be part of the deployment strategy early. Pilots usually have sponsors. Production systems require operational owners. Without clear ownership, AI systems slowly lose trust, degrade operationally, and eventually disappear from frontline workflows. 4. Success Metrics Focus on Accuracy Instead of Operations A 92% accurate model that nobody operationalizes is still a failed deployment. Too many AI pilots optimize for technical performance while ignoring operational outcomes. The organizations successfully scaling AI are measuring:• reduced cycle times• increased throughput• faster service resolution• improved workflow completion• stronger user adoption Not just model accuracy. This shift matters because enterprise AI is increasingly moving from “insight generation” toward workflow execution. AI only creates enterprise value when it changes operational execution. The Real Question Most MedTech organizations are asking: “Where should we use AI?”The better question is:“What operational foundation does AI need in order to scale?”Because scaling AI is not primarily a model problem.It is a workflow, data, governance, and execution problem.That is where successful deployments are won or lost. At N28 Technologies, we believe the future of enterprise AI is not standalone models or disconnected copilots.It is AI embedded directly into operational workflows where teams already execute work.That requires more than technology. It requires readiness.