Introduction to the Era of Buying Network

Your B2B buyer’s network is driving organizations to reimagine their messaging. Gone are the days when targeting a single decision-maker with a one-size-fits-all message would suffice. Even the days of building messaging for the buying group alone are now numbered. Welcome to the era of the buying network — a complex web of external influencers, customers, partners, providers (that’s us!), and even buyer AI such as ChatGPT, all of which are engaging with our buyers in the buying process. This new era is characterized by:

• Buyers who have grown up using technology and are accustomed to self-service interactions and options.
• Increasingly large internal buying groups that contribute to buying complexity.
• Growing reliance on external influencers for third-party insights and validation.
• Unparalleled adoption and dependence on generative AI for support throughout the entire purchasing process.

Connected Messaging Provides The Link Between Audiences

As marketers, we must rethink our approach to building and deploying messaging. We’re dealing with participants that demand a lot of insight from us, and we can’t just shout into the void hoping that our message sticks. Instead, messaging in the era of the buying network requires a more thoughtful approach, one that prioritizes building connected messaging that engages not only our buyers but all of the stakeholders and AI tools that they are increasingly turning to in order to help them buy better. This ensures that our message is not only heard but resonates everywhere.

Navigate The New Frontier By Building Messaging That Resonates Across Audiences

But how do we achieve this? It’s not just about crafting a great message; it’s about understanding the dynamics of the buying network and how each participant interacts with and influences the buying process. Here are five pointers to get you started:

1. Know your audience (all of them). Dive deep into identifying and mapping your buyer’s buying network. Who are they? What motivates them? How do they prefer to receive information? The better you understand each participant, the more tailored and effective your messaging will be.
2. Consistency is key. Your message needs to be consistent across all touchpoints and channels. This doesn’t mean being repetitive or boring; it means ensuring that the core message is clear, whether it’s being communicated through an email, a blog post, or even an AI chatbot.
3. Leverage technology. Your buyers are using it, and so should you! Technology, and especially AI, is your ally in the quest for connected messaging. Use analytics to gain insights into how messages are received and shared within the buying network. While nascent, AI may eventually help personalize the message at scale, ensuring relevance for every member of the network.
4. Foster collaboration. Encourage and facilitate dialogue within the buying network. When influencers, customers, and partners talk to each other, they reinforce your message and add their unique perspectives, making the narrative around your product or service even more compelling.
5. Be human. Last but certainly not least, remember that at the heart of every B2B transaction, there are people. Your messaging should not only be clear and concise but also authentic. Buyers are looking for trust and relationships, so show empathy, understand their frustrations and challenges, and offer solutions that resonate on a human level.

Leveraging The Buying Network To Deliver Your Message

The rise of the buying network represents both a challenge but, more importantly, an opportunity for B2B marketers. It’s a call to elevate our game by being more thoughtful, strategic, and connected in our messaging. By doing so, we can engage more deeply with our audiences, build lasting relationships, and ultimately drive higher buyer satisfaction.

Conclusion

Remember, in the end, it’s not about shouting louder than everyone else; it’s about understanding and speaking directly to the needs and wants of our buyers by engaging with the buying network in a language that resonates with every member of it. This approach will lead to more effective messaging, stronger relationships, and ultimately, greater success in the B2B marketplace.

FAQs

• Q: What is the buying network?
  A: The buying network refers to a complex web of external influencers, customers, partners, providers, and even buyer AI that engage with buyers in the buying process.
• Q: Why is connected messaging important?
  A: Connected messaging is crucial because it ensures that the message is not only heard but resonates everywhere, engaging not only buyers but all stakeholders and AI tools.
• Q: How can I leverage technology for connected messaging?
  A: You can leverage technology by using analytics to gain insights into how messages are received and shared within the buying network and by utilizing AI to personalize the message at scale.
• Q: What are the key elements of effective messaging in the era of the buying network?
  A: The key elements include knowing your audience, consistency, leveraging technology, fostering collaboration, and being human.
• Q: What is the ultimate goal of mastering B2B messaging in the AI era?
  A: The ultimate goal is to drive higher buyer satisfaction by engaging more deeply with audiences and building lasting relationships.

Introduction to AI in Indian Healthcare

A quiet revolution is underway in the heart of a radiology lab at Apollo Hospitals in Chennai, India. Artificial intelligence is scanning high-resolution images, flagging anomalies, reducing the time for diagnosis, and improving accuracy. But what makes this advancement so powerful isn’t just the algorithm behind it. It’s the collaboration between a hospital, a tech company, and a university that makes AI innovation sustainable, scalable, and relevant to India’s complex healthcare landscape.

Across India, a new model of digital health transformation is emerging, one where partnerships are as crucial as platforms. For a country grappling with massive disparities in healthcare access and delivery, this shift couldn’t be more timely. These are the observations and conclusions from my peer, Dr. Priyanka Shrivastava, who is a Professor of Marketing & Analytics at Hult International Business School and an Executive Fellow at The Digital Economist.

The Double Burden of AI Innovation and Inequity

India’s healthcare system faces deep challenges: a rapidly growing population, stark urban-rural divides, a chronic shortage of medical professionals, and overstretched public infrastructure. While the proliferation of health-tech startups has brought promise, much of the innovation remains confined to urban pockets or pilot projects.

AI detects disease, streamlines diagnosis, and personalizes treatment. Tools like AI-powered nutrition coaches (HealthifyMe’s Ria) and automated diagnostic assistants (such as those used by Aindra or Columbia Asia Hospital) are transforming the delivery of healthcare.

Yet, these tools often encounter barriers due to a lack of interoperability, fragmented data systems, regulatory uncertainty, and resistance from overworked staff who fear that AI might be more of a disruption than an aid.

Technology alone cannot fix healthcare. But technology plus collaboration just might.

Why Collaboration Is the Real Innovation Before AI

In a recent study, Dr. Shrivastava and her colleagues surveyed 300 healthcare professionals across 50 institutions and held in-depth interviews with doctors, technologists, and policymakers. The results were striking: institutions with strong cross-sector collaborations consistently showed higher and more sustained AI adoption.

Three core insights emerged:

1. Shared Resources Bridge Structural Gaps

Urban hospitals often have access to advanced technology and data, whereas rural clinics often lack even basic diagnostic capabilities. But when these entities partner via telemedicine links, shared platforms, or co-funding arrangements, AI can extend its reach. For example, Apollo’s AI systems, when linked with satellite clinics, enable faster referrals and better triage in underserved regions.

2. Knowledge Exchange Builds Trust

Resistance to AI isn’t irrational—it often stems from a lack of understanding. The study found that joint workshops, where doctors and engineers co-learned and co-created, built buy-in from healthcare workers. When staff are trained with the tools and understand how they were developed, they are far more likely to embrace them.

3. Collaborative Culture Drives Continuity

AI isn’t plug-and-play. It requires regular updates, feedback loops, and cultural alignment. Institutions that formalized collaboration through MOUs, shared R&D labs, or co-published studies were more likely to sustain AI programs over the long term.

Case Study: Apollo Hospitals’ Triple-Helix Success With AI and Collaboration

Apollo’s AI-driven radiology initiative in Chennai is a textbook example. Faced with long diagnosis times and overburdened radiologists, the hospital sought a solution. Instead of simply buying an off-the-shelf AI tool, Apollo co-developed one with a university, providing algorithm expertise, and a startup delivering the technical infrastructure.

Doctors and developers worked side by side. The result? Diagnosis time dropped by 30%, and accuracy improved by 15%. Radiologists weren’t replaced—they were enhanced, with AI acting as a second pair of eyes. Continuous training and feedback ensured the system evolved with practice.

This wasn’t a one-off deployment. It was an ecosystem. And that made all the difference.

Policy in Action: eSanjeevani and the Public Sector Push

While Apollo represents a private success, the public sector isn’t far behind. India’s eSanjeevani platform, which added AI-supported teleconsultation features during the pandemic, saw a 40% increase in rural usage. This shows that with the right support and scale, AI can democratize access to care.

The National Digital Health Mission is another promising initiative. If executed well—with strong data privacy frameworks and open APIs—it can offer a common layer for innovation. Startups can plug into public records; government hospitals can access AI-enabled diagnostics; researchers can draw insights from anonymized data.

But for this to happen, policymakers must prioritize collaboration frameworks just as much as digital infrastructure.

What Policymakers and Leaders Must Do to Collaborate with AI

As India enters a defining decade for health innovation, here are four actionable takeaways from the research:

1. Create Incentives for Public-Private Partnerships

Tax breaks, innovation grants, and pilot funding for joint ventures in AI health can catalyze adoption. Startups gain credibility and scale; public hospitals get access to frontier tech.

2. Invest in Capacity Building

Set up AI literacy programs for frontline health workers. Encourage interdisciplinary training so doctors, nurses, and tech teams speak a common language.

3. Standardize Data Sharing Protocols

A national framework on health data interoperability is overdue. Without this, AI solutions cannot scale beyond one institution. Build trust through consent-driven, encrypted data-sharing norms.

4. Measure What Matters

Mandate impact audits for all health AI deployments—measuring not just tech efficiency, but patient outcomes, staff satisfaction, and system-level equity.

The Bigger Picture: AI as an Asset for Collaboration

The most inspiring part of this story? AI in Indian healthcare isn’t being driven solely by top-down mandates or Silicon Valley imports. It’s being shaped organically by Indian doctors, engineers, policy thinkers, and entrepreneurs who are joining forces.

This pluralistic model with many voices but one mission could well become a template for emerging economies around the world. In a landscape where access to a doctor can mean the difference between life and death, AI’s potential is undeniable. But its success will depend on something far more human: our ability to collaborate. The most transformative technology for health care is not an algorithm. It is the alignment of purpose, people, vision, and AI through collaboration.

Conclusion

The integration of AI in Indian healthcare is not just about technology; it’s about collaboration and partnership. By understanding the importance of shared resources, knowledge exchange, and collaborative culture, India can successfully implement AI in its healthcare system, leading to better patient outcomes and more efficient healthcare services. Policymakers and leaders have a crucial role to play in creating an environment that fosters collaboration and supports the development of AI in healthcare.

FAQs

• Q: What are the main challenges faced by India’s healthcare system?
  A: India’s healthcare system faces challenges such as a rapidly growing population, urban-rural divides, a shortage of medical professionals, and overstretched public infrastructure.
• Q: How can AI improve healthcare in India?
  A: AI can detect disease, streamline diagnosis, and personalize treatment, thereby improving patient outcomes and healthcare efficiency.
• Q: What is the importance of collaboration in AI adoption in healthcare?
  A: Collaboration between hospitals, tech companies, and universities is crucial for sustainable, scalable, and relevant AI innovation in healthcare.
• Q: What are the key takeaways for policymakers and leaders to collaborate with AI in healthcare?
  A: Policymakers and leaders must create incentives for public-private partnerships, invest in capacity building, standardize data sharing protocols, and measure what matters in terms of patient outcomes and system-level equity.

Introduction to AI and Mobile Networks

During congressional hearing in the House of Representatives’ Energy & Commerce Committee Subcommittee of Communication and Technology, Ronnie Vasishta, Senior VP of telecom at Nvidia said that mobile networks will be called upon to support a new kind of traffic—AI traffic. This AI traffic includes the delivery of AI services to the edge, or inferencing at the edge. Such growth in AI data could reverse the general trend towards lower growth in traffic on mobile networks.

The Rise of AI Traffic

Many AI-enabled applications will require mobile connectivity including autonomous vehicles, smart glasses, generative AI services and many other applications. He said that the transmission of this massive increase in data needs to be resilient, fit for purpose, and secure. Supporting this creation of data from AI will require large amount of memory, particularly very high bandwidth memory, such as HBM. This will result in great demand for memory that supports AI applications.

Micron’s HBM4 Memory

Micron announced that it is now shipping HBM4 memory to key customers, these are for early qualification efforts. The Micron HBM4 provides up to 2.0TB/s bandwidth and 24GB capacity per 12-high die stack. The company says that their HBM4 uses its 1-beta DRAM node, advanced through silicon via technologies, and has a highly capable built-in self-test.

HBM Memory and AI Applications

HBM memory consisting of stacks of DRAM die with massively parallel interconnects to provide high bandwidth are combined GPU’s such as those from Nvidia. This memory close to the processor allows training and inference of various AI models. The current generation of HBM memory used in current GPUs use HBM3e memory. At the 2025 March GTC in San Jose, Jensen Huang said that Micron HBM memory was being used in some of their GPU platforms.

HBM Memory Manufacturers

The manufacturers of HBM memories are SK Hynix, Samsung and Micron with SK Hynix and Samsung providing the majority of supply and with Micron coming in third. SK hynix was the first to announce HBM memory in 2013, which was adopted as an industry standard by JEDEC that same year. Samsung followed in 2016 and in 2020 Micron said that it would create its own HBM memory. All of these companies expect to be shipping HBM4 memories in volume by sometime in 2026.

Emerging Memory Technologies

Numen, a company involved in magnetic random access memory applications, recently talked about how traditional memories used in AI applications, such as DRAM and SRAM have limitations in power, bandwidth and storage density. They said that processing performance has skyrocketed by 60,000X over the past 20 years but DRAM bandwidth has improved only 100X, creating a “memory wall.”

AI Memory Engine

The company says that its AI Memory Engine is a highly configurable memory subsystem IP that enables significant improvements in power efficiency, performance, intelligence, and endurance. This is not only for Numem’s MRAM-based architecture, but also third-party MRAMs, RRAM, PCRAM, and Flash Memory.

Future of Memory Technologies

Numem said that it has developed next-generation MRAM supporting die densities up to 1GB which can deliver SRAM-class performance with up to 2.5X higher memory density in embedded applications and 100X lower standby power consumption. The company says that its solutions are foundry-ready and production-capable today.

Projections for Emerging Memories

Coughlin Associates and Objective Analysis in their Deep Look at New Memories report predict that AI and other memory-intensive applications, including the use of AI inference in embedded devices such as smart watches, hearing aids and other applications are already using MRAM, RRAM and other emerging memory technologies will decrease the costs and increase production of these memories.

Conclusion

AI will generate increased demand for memory to support training and inference. It will also increase the demand for data over mobile networks. This will drive demand for HBM memory but also increase demand for new emerging memory technologies.

FAQs

Q: What is AI traffic?
A: AI traffic refers to the delivery of AI services to the edge, or inferencing at the edge, over mobile networks.
Q: What is HBM memory?
A: HBM (High-Bandwidth Memory) is a type of memory that provides high bandwidth and is used in applications such as AI and machine learning.
Q: Who are the manufacturers of HBM memory?
A: The manufacturers of HBM memories are SK Hynix, Samsung, and Micron.
Q: What are emerging memory technologies?
A: Emerging memory technologies include MRAM, RRAM, PCRAM, and Flash Memory, which offer improvements in power efficiency, performance, and storage density compared to traditional memories.
Q: What is the projected market size for emerging memories?
A: The projected market size for emerging memories is $100B, with NOR and SRAM expected to be replaced by new memories within the next decade.