AI Evolution blog explores a wide range of topics tailored for operational leaders and innovators, including:

AI Market Analysis: Periodic reviews of market trends and the evolving AI landscape.
AI Research Insights: Relevant topics around traditional machine learning, models, LLMs, GenAI, agents, algorithms, deep learning, neural networks, agentic software and much more.
AI Product Innovation and Strategy: Relevant topics around product development, economics, and go-to-market (GTM) strategies.
AI Software Engineering Lifecycle: Best practices for developing, testing, and managing AI-enabled and AI-native products.
Adopting AI Across Enterprise: Strategies to boost operational efficiency through AI adoption.
And much more, offering relevant insights to keep you ahead in the AI-driven world.

State of AI Innovation in HealthcareTech

For years, healthcare, education, and government have been seen as the next big frontiers in the software revolution (Article from Marc Andreessen in 2011 : https://a16z.com/why-software-is-eating-the-world/). However, if you've spent time developing software in these sectors, it’s evident that there’s still a long road ahead. Several factors contribute to this slow progress, including regulatory hurdles, dominance by a few major players, legacy technology & eco-systems, ownership structures (with most healthcare tech firms being private), and the reality that much of the tech talent is drawn toward cutting-edge technology development.

Lately, there has been a lot of discussion about AI stepping in to address these challenges. If you're a seasoned leader in healthcare technology, you might be cautiously optimistic—hoping for a significant disruption but remaining skeptical. From an outsider’s perspective, AI appears to be the long-awaited solution, but the scale of fundamental change required raises an even bigger question: How?

Understanding the Healthcare Tech Ecosystem

Before diving into AI’s role, let’s examine the current landscape of healthcare technology (excluding big pharma, biotech, and life sciences):

Electronic Health Records (EHR/EMR) Providers – Epic dominates the market, alongside Oracle (Cerner), Athenahealth, and numerous niche players offering specialized EHRs for specific medical fields. Despite efforts to standardize interoperability (FHIR/HL7), patients often have no idea where their health data is stored. Patient portals exist but often feel outdated, lacking the usability and customer-friendliness expected in modern software solutions.
Revenue Cycle Management (RCM) Providers – A range of companies assist hospitals and healthcare providers in getting paid, navigating multiple layers of financial complexity—from clearinghouses to manual claims submission.
Prescription Management – Various vendors handle drug data and facilitate prescription processing between EHRs and patients. This ecosystem includes both large pharmacy chains like CVS and software solutions that manage prescription transactions.
Payer Systems – Major insurance companies operate independently, maintaining extensive patient data from paid claims.

Since many of these companies are privately held, dealing with legacy technology, their motivations for innovation often revolve around financial gains and incremental improvement. However, there has been a notable influx of venture capital investment in revenue cycle management (#2 above), signaling potential shifts in the landscape.

AI’s Growing Role in Healthcare Tech

So, where does AI stands currently in this area? Here are some early trends we’ve observed:

AI-Powered Scribes for Physicians – Numerous startups and tech giants like AWS HealthScribe are integrating AI-driven documentation solutions within EHRs to alleviate administrative burdens for doctors.
Automation in Revenue Cycle Management – Some investment is focused on streamlining and automating financial processes within healthcare billing and claims management.
AI Initiatives by Payers – Insurance companies are investing in AI, primarily to optimize claims processing and improve profit margins.
Research Groups - Bunch of research groups funded by some big tech or hospital systems(ex: MassGeneral) or drug companies to develop specific models. Often lack mass commercialization angles beyond the purpose around research funding.

The primary bottleneck: Data, Data, and More Data

Despite all advancements, obtaining quality access to healthcare data to advance AI remains a significant challenge. As mentioned earlier, this is largely due to:

A lack of motivation within the ecosystem to share data.
The tendency to use regulations like HIPAA as an excuse rather than investing in truly secure, HIPAA-compliant solutions.
A shortage of technology talent addressing these challenges holistically, as top talent is naturally drawn to a few specific verticals.

The Patient Perspective: Still a Struggle

Amidst all this innovation, patients continue to face rising premiums, shrinking coverage, and increasing complexity in navigating healthcare—whether scheduling visits, disputing bills, or understanding the quality of care received. Access to personal health records remains a challenge, with little education or ease of retrieval. While HIPAA was designed to protect patient data, it inadvertently contributes to restricting patient access rather than facilitating it.

The Need for Disruption

The healthcare sector is ripe for transformation, and AI has the potential to drive meaningful change. We invite you to explore real-world applications of AI, leveraging our experience in developing and implementing AI strategies to support your organization’s journey in this evolving landscape.

Disclaimer: This blog is intended to reflect author’s personal opinions around general healthcare landscape and not intended for any one segment within healtcare.

Business, AI Research, Go-to-MarketBlackPepper LabsFebruary 5, 2025

One of the early use cases of Generative AI has been around, how to create, manage and operate Agents for supporting existing customers. As you might have noticed, Salesforce has been promoting AI agents in their platforms, so do other providers like Servicenow, Zendesk. Naturally it is the extension of their business model to protect existing business from over AI optimization, while making sure a their version of AI narrative is promoted. However there are plenty of AI-First agent products have been coming to market in 2024. We have seen good adoption of these agents in enterprise context.

As these technologies mature, a crucial conversation has emerged around leveraging AI for end-to-end customer support and success. This includes new customer onboarding to providing product support(including tech support in most cases).

So, if you are a Chief Customer Officer or Leader in this organization, naturally you have to be able to create Ai strategy to adopt agentic software capabilities. In most organizations, these capabilities are provided by IT function. However it is crucial to be able to understand and create your own strategy around AI for your department beyond toolings. Following are crucial points that keeps coming up in conversations between Exec Leadership and Chief Customer Success office.

"How can we leverage AI Agents to best support to customers to make them successful?"

"What's the fastest path to optimize existing customer support systems and data?"

"How can we increase key KPIs such as NPS and CSAT while optimizing customer success/suport investments?"

"How do we maximize our existing talent's efficiency?"

"Which current Gen AI/Agent tools can deliver immediate productivity gains?"

While these questions are critical, the answers vary significantly based on organizational role and AI expertise. This has created three distinct stakeholder groups:

1. Investors, particularly in PE and VC spaces, seeking efficiency gains in customer success and support budgets beyond offshoring.

2. Organizational leaders driving acceleration and digital transformation initiatives need to show ROI backed by key metrics including efficiency gain metrics

3. Product Support, Success and onboarding leaders tasked with adoption, and implementation

For those in the third category, the responsibility is significant. Success requires crafting both short and long-term strategies through careful discovery, stakeholder education, and adaptive road mapping - all while navigating this rapidly evolving technological landscape collaborating with technology function.

The challenge isn't just about adopting new tools - it's about understanding their true potential and limitations in your specific customer success, product and operational context. As we move forward, the winners won't be those who simply embrace AI, but those who strategically integrate it into their customer lifecycle managment processes while maintaining focus on business outcomes.

We invite you to learn from our real-world experience in developing and executing AI strategies, and discover how we can support your organization's AI journey.

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