CIOs are dealing with a lot of change, from the rapid rise of AI and cloud modernization, to cybersecurity and workforce transformation. Many are sharing what they’re learning as they tackle these challenges, including what’s working, what isn’t, and how they’re implementing new technologies in their organizations. Here, a select group of tech leaders reveal the CIOs they follow and why.

Suma Nallapati, chief AI and information officer, City and County of Denver

I follow Suma Nallapati primarily through public technology and AI leadership discussions. She regularly discusses AI adoption, responsible AI, digital transformation, technology strategy, AI governance, human-centered technology, and using AI to improve public services. I find her perspective particularly valuable because she connects AI strategy with practical organizational and citizen outcomes. She also emphasizes responsible and human-centered AI rather than viewing AI simply as a technology implementation exercise. One initiative I found particularly interesting was Denver’s use of AI through its multilingual Sunny chatbot and other city services. Her current role includes scaling AI across areas such as resident engagement, internal automation, permitting, and emergency response while maintaining governance, transparency, and public trust. – Sachin Gupta, senior IT manager, Arrow Electronics

Jennifer Hartsock, chief information and digital officer, Cargill

I follow Jennifer Hartsock through the CIO community, as well as through her thought leadership, interviews, and LinkedIn. I especially enjoy hearing her perspective in longer-form conversations where she talks candidly about the realities of leading technology and transformation in a large, complex organization. Hartsock talks about many of the issues CIOs are navigating right now, including AI, digital transformation, technology operating models, talent, and leadership. A consistent theme in her perspective is connecting tech decisions back to the business, including how tech teams work with the business, and how you create greater ownership closer to the outcomes you’re trying to achieve. – Kathy Kay, EVP and CIO, Principal Financial Group

Fletcher Previn, SVP and CIO, Cisco

Fletcher Previn’s profile shows how a modern technology leader can discuss change without reducing it to a narrative about vendors. He explores AI, culture, talent, and productivity, with an emphasis on conditions that determine whether transformation lasts. The insight worth returning to is his focus on adoption as a leadership responsibility. New technology fails when teams lack learning time, managers lack clarity, and incentives reward behavior that no longer serves the business. CIOs can apply that lens to modernization, security initiatives, and data governance. It encourages an honest definition of progress based on decisions and outcomes rather than launch dates or dashboards. – Sherif Koussa, CEO, Software Secured

Kellie Romack, CDIO, ServiceNow

What makes Kellie Romack’s perspective valuable is she talks about AI from inside a large enterprise IT organization, not the outside. Her posts regularly cover enterprise AI, AI governance, autonomous IT, workforce transformation, cybersecurity, digital transformation, and how the CIO role is changing.

The point that stayed with me was her argument that CIO organizations now have to redesign jobs, teams, and workflows around AI while building new human skills at the same time. That’s a useful distinction. It frames successful AI adoption as an organizational and leadership problem as much as a technology one. Her seat at ServiceNow also gives her a view of what happens after the experiments stop. ServiceNow has reported that its internal AI use cases grew from 15 to more than 200. That makes her worth reading about for any CIO trying to understand what scaling enterprise AI actually looks like. – Tapabrata Biswas, project manager, InfluxIQ Tech Pvt. Ltd.

Mike Blandina, CIO, Snowflake

I follow Mike Blandina for his practical perspective on enterprise AI adoption and the operational realities behind digital transformation. His commentary consistently connects emerging technology with the governance, workforce skills, and measurable outcomes needed to make it useful. One insight that particularly resonates with me comes from this CIO.com article.

He makes the case for building an editing culture rather than a writing culture. As AI increasingly handles first drafts of code, reports, and other work, he argues that the critical human skill becomes evaluating outputs, identifying mistakes, and knowing when to challenge the system. I also appreciate his emphasis on data lineage, access permissions, and measuring actual productivity improvements instead of AI tool logins. Those are the less visible decisions that determine whether enterprise AI adoption delivers lasting value. – Parth Patel, director of operations, Shaligram

Deepa Soni, EVP and CIO, New York Life Insurance Company

Deepa Soni’s insights are particularly valuable because she oversees AI in one of the most critical and regulated sectors of financial services, a Fortune 100 life insurance company where AI decisions have real consequences for policyholders and regulators.

Soni frequently shares in-depth thoughts on what responsible AI adoption truly looks like within a large financial institution. She discusses the delicate balance between the speed of innovation and the readiness of governance, as well as how tech leaders can foster cultures that promote both agility and accountability. What sets her apart is that her approach to AI strategy is rooted in operational realities rather than just vendor hype, which is a rare and precious perspective for CIOs facing similar challenges. – Viren Meghani, technical architect and independent researcher in AI governance

Sears Merritt, CIO and head of enterprise technology and experience, MassMutual

I follow Sears Merritt on LinkedIn and through his interviews. He talks about what enterprise AI costs to run and how to keep the option to change your mind. Merritt also discusses why AI vendor contracts should be capped at 12 months to maintain flexibility, how success metrics are defined before AI pilots begin to determine whether they should be scaled, and the data platform he spent three years building before the AI wave arrived. This is useful to other CIOs because he’s one of the few CIOs at a regulated insurer who talks about AI in the language of procurement and measurement rather than vision. – Egiziago Cioffi, IT/enterprise architect and CEO, SynSphere Italia

Ganesh Ariyur, CIO and head of enterprise technology, Gainwell Technologies

I follow Ganesh Ariyur for his insightful views on AI, enterprise architecture, transformation, tech strategy, governance, and ways CIOs can link tech investments to quantifiable business value. What I find particularly helpful is his content extends beyond just talking about new technologies. He continually positions technology choices in relation to business results, operational frameworks, financial discipline, and implementation. This is particularly important for CIOs facing pressure to showcase ROI from AI and modernization efforts.

An example is his work, The 2026 CIO Operating Model: Architecture, AI, and Enterprise Value. His main point is that tech strategy must be viewed more like capital strategy, with CIOs assessing issues like if cloud expenditures enhance unit economics, and if architecture facilitates quicker decision-making. That viewpoint is important as it transitions the dialogue from which technology to implement, to what ways technology should transform the enterprise’s operations and value creation. – Ketan Patel, digital marketing manager, WeDoWebApps

Jim Swanson, EVP and CIO, Johnson & Johnson

I’m interested in how Jim Swanson thinks about the relationship between technology and the business, particularly where innovation carries considerable responsibility. For example, he recently discussed how AI agents are changing the way people interact with enterprise software. What caught my attention was his emphasis on the complexity that comes with that change — the integration, oversight, and accountability required to make it work. Those are questions that deserve as much attention as the technology itself. – Bill Huber, partner, ISG

Kimberly Basile, CIO, Kyndryl

I follow Kim Basile along with her bylined pieces on Kyndryl’s newsroom and interviews in the business press. She discusses enterprise AI adoption, workforce readiness, AI governance, platform modernization, and security leadership, informed by her earlier role as SVP and CISO at Leidos. Basile speaks from inside a transformation few CIOs have experienced, having helped guide Kyndryl’s technology separation from IBM and consolidate its application portfolio. She treats governance and people readiness as part of AI adoption from the start, and she’s candid about what hasn’t worked. In a recent feature on agentic AI, for instance, she said Kyndryl initially focused on counting agents, and the number didn’t tell the true story. Her team pivoted to measure business process flows. She also identified automating a process that hadn’t been properly validated as one of the company’s biggest mistakes. Activity metrics are easy to report, so organizations default to them. Her lesson pushes CIOs to validate a process before automating it.

She matters beyond the technology because women remain underrepresented in IT leadership, and she uses her visibility to lift others. When she was named to Women We Admire’s Top Women CIOs of 2026, her second year on the list, she used her post to congratulate the other honorees by name and highlight how these leaders help employees embrace technology. That generosity matters in a field where women CIOs are still too few. As the co-founder of Girl Power Talk, I pay attention to leaders who open doors for the next generation of women in tech. Basile does that consistently. – Sameer Somal, founder and CEO, Blue Ocean Global Technology

When an AI workload is not performing as expected, the obvious place to look is the GPU. Do we have enough compute capacity? Would adding more GPUs solve the problem?

Not necessarily.

The GPUs may be perfectly capable of handling the workload, but if the network is not able to move data quickly and consistently, adding more compute could result in more expense without addressing the real bottleneck.

This is something I think we risk losing sight of in the rush to invest in AI. GPUs cannot do their job alone. They rely on huge amounts of data moving quickly between compute, storage and applications, which means the network has a big part to play in how well that investment actually performs.

For CIOs, this means looking beyond how much compute they have and asking whether the network and wider infrastructure can keep pace.

AI is changing what the network needs to do

Enterprise networks have always had to move data reliably between systems. The difference with AI is the volume, speed and pattern of that traffic. Large AI workloads are distributed across multiple GPUs because the processing requirements are simply too great for one processor to handle efficiently. Those GPUs need to communicate constantly, exchanging data and synchronizing their work. Training is particularly demanding because GPUs may need to exchange parameters and gradients throughout the process, creating large volumes of traffic. As the number of GPUs increases, so does the importance of the connections between them.

This changes the relationship between compute and the network. Adding more GPUs increases processing capacity, but it can also increase the amount of data that needs to move between systems. If the network can’t keep pace, it can become the bottleneck that prevents that additional compute from delivering the expected performance.

Organizations do not need thousands of GPUs before this starts to matter. Even relatively modest enterprise deployments can involve large datasets moving between storage and compute, multiple accelerators working together and applications accessing models in real time.

The important point is that AI readiness cannot be measured simply by counting GPUs. You can continue adding compute, but there comes a point where the network determines how effectively that compute can be used. If data cannot reach the GPUs quickly enough, or communication between them is slowed by congestion or latency, expensive resources can spend time waiting rather than processing.

At that point, the organization doesn’t have a compute shortage — it has an infrastructure problem.

More bandwidth does not solve every problem

The obvious response might be to add more network capacity. While bandwidth certainly matters, increasing it does not remove every potential constraint. AI workloads can be particularly sensitive to latency, congestion, packet loss and inconsistent performance. Cisco notes that AI pipelines create different traffic patterns across training and inference, with training requiring both high bandwidth and low latency as GPUs continuously exchange information. The infrastructure therefore needs to manage not just capacity, but how traffic moves through the environment.

That distinction matters.

Imagine hundreds of GPUs working together on the same task. Their value comes partly from their ability to operate in parallel, so if one part of the network slows communication between them, the effect can extend beyond that individual connection. GPUs may be left waiting for other parts of the workload to catch up. The fastest component in the environment is not much use if it is constantly waiting for the slowest.

Storage adds another dimension. AI depends on data, which means moving large datasets from storage systems into compute environments and, depending on the workload, moving information back again. A network that performs well for GPU-to-GPU communication could still encounter problems elsewhere in that journey.

AI infrastructure does not usually exist in isolation, either. Enterprises are introducing GPU clusters into data centers that already support conventional applications, storage platforms, cloud connectivity and other business-critical systems. AI traffic therefore becomes another part of an increasingly complex infrastructure estate.

This is where network design starts to have a direct bearing on the economics of AI.

If an organization has invested heavily in accelerated compute, it wants those resources doing useful work for as much of the time as possible. A network bottleneck that leaves GPUs underutilized is not simply an IT performance issue — it means the business is getting less value from some of its most expensive infrastructure.

You cannot fix what you cannot see

Another challenge is knowing where the problem actually is. Infrastructure teams can see whether devices are available, track bandwidth utilization and receive alerts when something fails. That remains important, but increasingly complex AI environments demand a broader view.

A switch can be working. A server can be healthy. The GPUs can be available. Storage can be online. Yet the workload can still be performing below expectations. That’s because understanding individual components does not necessarily tell you how well the overall system is working. This is where the distinction between monitoring and observability becomes useful.

Traditional monitoring generally tells teams whether infrastructure components are operating within expected parameters. Observability goes further, bringing together information from across the environment to help teams understand why a system is behaving in a particular way. IBM describes network observability as providing real-time visibility into the performance, behavior and health of the network by drawing on data from across the infrastructure, rather than focusing solely on predefined metrics and reactive troubleshooting. For AI environments, that wider view is increasingly important.

If a training job slows down, teams need to be able to establish whether the cause is compute, storage, congestion on a network path, packet loss, a degraded link or something else entirely. Looking at each device separately can make that difficult, particularly when the issue is not a complete failure but a gradual deterioration in performance. Those are often the harder problems to find. A component that fails completely usually generates an alert, but a connection that still works yet is performing badly can be much less obvious. The infrastructure is technically available, but the workload is taking longer, GPUs are being used less efficiently and costs are creeping up. End-to-end visibility gives infrastructure teams a better chance of seeing those relationships.

AI readiness is an infrastructure question

As AI moves deeper into business operations, the consequences of infrastructure performance will become harder to ignore. A bottleneck during an experiment might delay a development team, but the same problem affecting a production AI service could have consequences for employees, customers or other systems that depend on it.

This means the network needs to be considered as part of the AI investment from the outset, rather than something to revisit when performance problems appear.

That does not necessarily mean replacing the existing network or building an entirely separate environment for AI. In many cases, the infrastructure already in place may be perfectly capable of supporting the workloads being introduced. The important thing is knowing where its limits are. That requires an understanding of what those workloads actually need, where genuine constraints exist and whether IT teams have enough visibility across compute, networking and storage to spot problems as they emerge.

Ultimately, it is not how much compute an organization has that matters, but how well it can use it. If CIOs are investing heavily in GPUs, they need to make sure the infrastructure around them is not holding them back.

It’s 2 a.m. The change window is open. The plan was reviewed and the change review board signed off. And still, everyone on the call is holding their breath because nobody actually knows what will happen next. That feeling isn’t paranoia. It’s the accurate read of an industry that has spent decades changing production networks with no way to prove the outcome first.

Every other engineering discipline solved this long ago. Aerospace models a system before it flies. Pharma models a molecule before it enters a trial. Software gets a compiler and a test suite before code ships. Networking never got the equivalent, so it built rituals around the gap instead: change windows, war rooms, and the quiet understanding that the only real test environment is production itself. Not because engineers weren’t careful, but because the discipline never had the tooling to prove itself before shipping.

Now the pressure to close that gap is compounding. Leaders want AI managing the network the way it’s starting to manage everything else. But I hear the same hesitation in nearly every conversation I have with network and security leaders, and it’s rational. Automating a network you don’t fully understand doesn’t create efficiency; it accelerates risk. When neither your team nor the AI agents acting on their behalf have a deterministic answer for what a change will do to the production network, you’re not automating your network; you’re automating risk at machine speed.

Here’s the part I want IT leaders to hear as encouragement, not caution. This gap is closable, and it doesn’t require waiting for AI to get smarter. It requires a different standard of proof. A change shouldn’t be graded on who reviewed it or how confident the room felt. It should be modeled against the full production network, spanning every vendor and every protocol layer, and graded as a deterministic pass or fail, the same way a software build either builds and passes the test suite or it doesn’t. That’s not a bigger dashboard. Visibility tells you what already happened. Proof tells you what will happen next, before it does. That distinction is the whole game, and it’s why the fix has to be architectural, not more data flowing into the same review process.

Once that standard exists, the rest of the autonomous networking conversation gets a lot less scary. AI agents can propose changes, get a real answer, and iterate until the answer is yes, with a human deciding when to pull the trigger. Engineers get their time back for architecture instead of manual validation and firefighting. The organizations that get to autonomous networking first will be the ones that built this foundation before handing the keys to AI.

IDC recently published a Spotlight: Navigating the Shift to Autonomous Networking that delves into the barriers to autonomous networking and its recommendations on how to successfully embrace the technology. I encourage you to read it.

Chris Barnard, vice president of Enterprise Infrastructure at IDC, recently joined me for a conversation around how to successfully prepare for autonomous networking. You can watch it here.

For such a rapid innovation cycle, AI has been given unprecedented levels of responsibility. According to the Stanford AI Index 2026, 88% of organizations used AI in 2025, with 70% using generative AI in at least one business function. Most analysts agree that this technology, particularly when it comes to generative and agentic AI, is yet to reach full maturity, yet it’s already making itself indispensable to most enterprises. Employees expect LLM-based copilots to respond as readily as any other business application, customers are increasingly exposed to AI as part of their user experience, and emerging agentic models need to communicate continuously with applications and infrastructure as they crunch data and carry out tasks. Any hint of delay within those interactions has the potential to disrupt productivity, sow mistrust in the technology, and limit any return on investment (ROI).

Most businesses now inhabit a multi-cloud environment that spans countries and continents. When an AI request depends on information stored in one cloud environment, processing capacity hosted in another, and an application delivered somewhere else entirely, latency becomes the deciding factor. Each network hop, particularly through public Internet pathways, increases response time. Repeat these delays across hundreds, thousands, or even millions of individual requests – from both humans and AI agents – and the whole organization becomes artificially hampered. Sometimes connectivity becomes so hampered that IT teams must step in and spend valuable time rectifying it.

A recent Censuswide survey commissioned by DE-CIX found that IT teams in the US and UK spend an average of 11.5 hours every week resolving cloud connectivity issues – that’s more than a full working day each week spent on network troubleshooting. This is the “hidden productivity tax” that organizations are now paying, and it may be why the ROI for AI initiatives feels lukewarm at best, and impossible to measure at worst. McKinsey’s 2025 global AI survey found that only 39% of respondents could attribute any enterprise-level EBIT impact to AI, and most of those reported a contribution below 5%. That’s why even the best models can only get you so far – compute power may command the budget, but it’s the network that determines value.

A tax on productivity

Consider what happens when an employee asks an AI assistant to carry out a simple task like summarizing an internal document or doing some number crunching. The application may need to verify the employee’s identity, locate the relevant information, send it to a model operating in another cloud environment, and return the response through the user-facing application. An agent completing a more involved task could make this journey repeatedly as it gathers information and interacts with other systems. If one connection is slow or unreliable, the delay follows the request all the way back to the user, and if that connection fails, a whole team may be drafted in to isolate a problem that appeared inside the application but originated somewhere along the network path.

Engineers who could be improving AI services or supporting new deployments are instead tracing routes, investigating intermittent failures, and working around infrastructure that behaves unpredictably. What makes this particularly striking is the contradiction that was revealed as part of DE-CIX’s survey. While enterprises concede that more than a full working day per week is spent on network troubleshooting, as noted above, nearly all (96%) say their networks are “ready” to support future cloud and AI projects. This doesn’t prove that enterprise networks are fundamentally unprepared, but it does reveal a disconnect between strategic confidence and everyday operational experience. When more than a working day can disappear into connectivity troubleshooting each week, some of the expected productivity benefit from AI is already being spent before the technology has had an opportunity to deliver it.

AI performance and network performance are inseparable

Users don’t experience a model and its supporting infrastructure as individual sets of requests – they just experience “fast” or “slow” workloads. A cutting-edge AI model will still feel ineffective if the information it needs travels along congested or unpredictable public transit routes, so any real-time interaction with AI will become an exercise in frustration with small delays that compound over time, slowing teams down.  As AI services become more distributed, network performance is becoming inseparable from application performance, but it’s rarely given the same attention as model accuracy, compute capacity, or the cost of running AI inference.

IT leaders are beginning to take note, however, despite being a little overconfident about their network readiness. Alongside latency between cloud environments, security vulnerabilities including DDoS attacks, and downtime or reliability concerns are triggering a change in approach towards network connectivity. That’s why many are now turning to private, direct connectivity to cloud providers through an Internet or Cloud Exchange or via other means of directly connecting to the cloud providers’ private connectivity solutions, bypassing the pitfalls of the public Internet and IP transit.

Direct connectivity offers a greater degree of sovereignty, visibility, and control over data pathways that can reduce latency and enhance security. Public Internet connectivity will remain essential, of course, but business-critical AI traffic should not follow routes that can change unexpectedly or introduce additional network hops. It isn’t a universal cure for poor AI performance, but it can remove some of the uncertainty from an operating model that increasingly depends on information moving quickly and reliably between distributed environments.

Turning network performance into a business metric

The next 12 months could be a turning point for businesses looking to prove AI’s worth. CIOs simply need to account for the full cost of delivering an AI service, including the hours spent diagnosing connectivity problems and the value lost when applications respond inconsistently. In other words, they need to bring network teams into AI planning from the outset, considering their network infrastructure with the same rigor they apply to selecting AI models and projects. Private interconnection can provide more direct and predictable routes between environments, while effective observability can reveal how traffic moves, where delays accumulate, and how reliably the entire workflow performs. Without that control and visibility, network friction will remain a “hidden tax on productivity” that could stall AI deployments and weaken the case for future investment.

Across all verticals and regions, contact centers are under pressure to modernize and improve customer experience (CX). With today’s consumers being digital-centric and mobile-first, CX expectations are higher than ever, and IT and contact center leaders are struggling to keep pace, especially when it comes to adopting new technology.

Voice AI is the latest technology wave for improving CX, and while most contact center leaders want to adopt it, they need a fuller understanding of all that is needed for voice and AI to work together effectively. This is especially true for enterprise-scale contact centers that must support a global customer base.

While AI can easily scale in these environments, most voice traffic must still traverse the PSTN. Customer usage of digital channels keeps growing – including voice-capable apps like WhatsApp and Viber, but voice remains the primary channel for contact center interactions.

Both modes of voice are important, but telephony-based voice is central for CX, and as contact centers adopt AI, the integration of voice with AI will be essential. This article will review the benefits of this integration, the key capabilities needed for Voice AI and why I think it’s so important for contact centers to own their voice when adopting AI.

What is Voice AI and how it impacts CX

Following in the footsteps of ChatGPT and Agentic AI, Voice AI is the latest AI wave. While Voice AI is currently at the early adoption stage, I see it becoming foundational to modern CX, just like these predecessors quickly became.

Being so new, some explanation is in order. Voice AI refers to AI applications or platforms that are voice-based, meaning that voice can be used for both inputs and outputs when human customers make inquiries with a contact center.

Customers can use several modes to make these inquiries, but the “input” usually starts with voice, which the Voice AI platform is able to understand – and now can do so at a much higher level than with legacy voice technologies. For the “output”, Voice AI can respond to the customer using speech (or text) in a conversational manner that can often take the inquiry through to completion.

In more familiar terms, Voice AI applications are known as digital assistants, automated voice agents, or intelligent chatbots. Customers have been using legacy versions of this for years – think of the traditional IVR – but with today’s AI, the voice experience feels more natural. Furthermore, these applications can understand voice at a much deeper level, by integrating more extensively with back-office systems, leading to better CX outcomes, both with human agents and for self-service.

Once contact center leaders are comfortable with the Voice AI concept, they need to think about it more practically – how it impacts CX. This is what drives business value, and to frame that, here are some leading examples:

• Being automated, Voice AI can manage customer inquiries 24/7 – this is especially important for global brands with centralized contact center operations.
• Voice AI platforms, by nature, can scale quickly and at nominal cost – making them an ideal solution to handle both expected growth in  call volumes, and unexpected spikes in traffic.
• Improving self-service automation is a core driver for any form of AI, especially Voice AI. This will lead to higher containment rates where self-service options handle more inquiries, and higher deflection rates, where more routine inquiries are routed away from human agents to AI agents. Going a step further, these capabilities will improve the agent experience, which leads to another layer of operational benefits.
• Elevating omnichannel capabilities – today’s customers expect a seamless experience across all channels, and with voice being the preferred channel for contacting a company, a clean handoff to other modes and human agents is critical.
• Better outcomes drive better CX – voice is a more expensive channel to support than text, but when properly supported, Voice AI will deliver better CX than text. It can be validated with metrics such as CSAT scores, loyalty, retention, referrals, purchasing trends, etc.

Key considerations for voice infrastructure

With the above in mind, I think the converse outcomes speak even more loudly when either voice or AI – and especially together as Voice AI – do not perform as intended. Not only would this negate the above benefits, but the negative impact will be even more consequential. As noted, most contact centers urgently want to modernize with cloud and AI – precisely to avoid these negative consequences.

While this article focuses on AI-ready contact centers, the broader intention is to build a case for Voice AI that will help legacy-based contact centers advance their plans for getting on this much-needed path. No other channel has a greater impact on CX, and the Voice AI opportunity adds a new dimension of business value with voice, along with leveraging AI for automation and customer engagement wherever they are, and whatever language they speak.

Technology aside, my underlying message for all contact centers is to own your voice. The best way to own your voice is by having the right infrastructure in place. To varying degrees, contact centers have long relied on purpose-built voice infrastructure, since so much of their voice traffic is still PSTN-based. However, as contact centers adopt AI, it’s easy to overlook the complexities of integrating telephony-based voice into AI applications.

Voice infrastructure is technical by nature, and there are some key elements that IT and contact center leaders must keep in mind when deploying AI in tandem with voice. In AVOXI’s 2025 State of International Voice Report, 77% of enterprises cited difficulty consistently achieving high-quality voice, highlighting an existing challenge that becomes even more important as contact centers integrate voice with AI. This is especially relevant for contact centers serving a global customer base, and at minimum, voice infrastructure must include the following:

• A global network to support telephony, and for large-scale contact centers, this means managing voice traffic seamlessly across dozens of local and international carriers.
• Telephone numbers, both for all local markets and toll-free numbers – and ensuring the numbers are in working order and connect properly to the contact centers.
• Quality Assurance to support SLAs, and real-time network analytics to monitor voice performance, especially for uptime, reliability and quality.
• Orchestration capabilities to route calls from AI agents to human agents – accurately, securely, and without losing context – all in real time.
• Integration of telephony-based voice traffic with both AI and CCaaS platforms – at scale and in accordance with local compliance and privacy requirements.

There are other elements to voice infrastructure as well, and the key idea here is to explain why these are fundamental capabilities; not just to support the most important channel your customers rely on, but for voice to be effectively integrated with the AI platforms and applications contact centers are now rushing to deploy. The visual below illustrates how the main infrastructure pieces must interwork to support Voice AI.

Jon Arnold

More than anything else, voice infrastructure is about minimizing latency, given the real-time nature of voice communication. When customers speak to human agents, some incremental latency can be tolerated by both parties, but when the customer is relying on an AI agent to understand them, that same degree of latency – or worse – will lead to a multitude of problems, both during and after the interaction.

The AI part of Voice AI isn’t advanced enough to respond accurately when there are gaps in the conversation, echo, choppy audio, garbled speech, etc. That’s the scenario contact center leaders need to avoid – not just for poor quality bot-to-human communication, but for inaccurate speech-to-text outputs like transcriptions and call summaries. This will be the result when overlooking voice infrastructure, and it will completely defeat the purpose of using AI.

Again, the best way to do that is to put just as much consideration into the voice infrastructure piece as with AI platforms. By nature, neither AI nor CCaaS platforms can natively address the voice capabilities outlined above, hence the importance of owning your voice.

Vendor considerations for integrating voice with AI

Without a proper voice infrastructure, Voice AI will simply not be effective. No matter how “intelligent” the AI platform, these capabilities are not native or comprehensive enough to manage telephony-based voice, especially at scale and on a global basis.

The key point to recognize is that both AI and CCaaS platforms will be at their best when they receive voice streams that have already passed all the hurdles required to make them AI-ready. Considering all the things that can go wrong with this traffic if not managed properly, and the computational resources needed for this, bolting voice on to one of these platforms is risky at best.

IT and contact center technology leaders could choose to leave the job of monitoring voice performance up to an AI or CCaaS vendor. However, many contact centers rely on multiple vendors, which is challenging when adopting new technologies. There are various issues to consider:

• How will call flows be orchestrated in real time?
• Can you trust voice to be managed by one of these vendors when their competitors are also part of your deployment?
• What would happen to your voice infrastructure (and corresponding customer service phone numbers) if you switched out from the vendor you’ve entrusted for this?

Making good decisions here really comes down to what’s most important. When adopting AI, IT and contact center technology leaders must balance two priorities, neither of which should be compromised: providing the highest possible quality voice experience with telephony, and providing the most impactful forms of automation with AI – especially for self-service.

Even though telephony-based voice isn’t the only mode for voice, it’s the most important one for CX. PSTN calling will be with us for years to come, and when architecting your voice stack, telephony must be a core consideration. App-based voice options have their virtues, but for today’s AI deployment decisions for CX, telephony will have a far greater impact.

This brings us to the familiar best-of-breed versus all-in-one trade-off that contact center leaders must consider with Voice AI. In short, telephony-based voice is best handled by voice infrastructure vendors, just as AI applications or CCaaS applications are best handled by those platforms. Not only will you get the best AI outcomes with best-of-breed for voice infrastructure, but the contact center will have much more flexibility in a constantly changing environment:

• Avoiding vendor lock-in, especially for capabilities that are not core competencies or focus areas.
• Ability to support multiple AI platforms, especially for customer bases with specific languages or dialects, or verticals with complex terminology – this is where voice quality is paramount.
• Enabling contact centers to enter new geographies that are only supported by certain carriers for telephony, or have specific requirements for compliance, privacy, data sovereignty, etc.
• Premises-based contact centers ready for cloud migration can keep their voice infrastructure in place, leaving them open to choose the best-fit AI and CCaaS partners.

To make the right decisions about voice infrastructure, contact center leaders must consider what each of the following options can offer:

• Carrier partners – especially regional or global telcos such as BT, Orange and Tata. While they can provide the core elements to support telephony at scale, you will be locked into their network footprint and because they are primarily in the network business, software integration with AI platforms is not their forte.
• CCaaS providers – the likes of NiCE, Genesys, Five9, etc., provide the foundational technology for CX, but voice is just one of many modes they must support. Also, most are cloud-native and lack the critical voice pedigree necessary to directly support telephony in particular.
• API/messaging-based platforms – Twilio being a prime example – do have extensive global reach with voice networks, but mostly for outbound traffic. They are not focused on broader CX like CCaaS providers; and more importantly, their voice offerings are not out-of-the-box the way a best-of-breed partner would be.
• AI platforms – such as PolyAI, Cresta and ElevenLabs – are rapidly expanding, and these will be your primary partners for Voice AI. These vendors are highly specialized, and their value comes from being purpose-built for conversational AI. However, when the voice component is about telephony, this is where integrations with voice infrastructure partners are so critical.
• Voice infrastructure – leading vendors such as AVOXI – offer purpose-built solutions for both PSTN and cloud-based voice. These are the best-of-breed providers best suited for global contact centers deploying Voice AI, as well as hybrid contact centers that must support all modes of voice.

Conclusion: Own your voice

The core theme here has been the importance of owning your voice, and how doing so requires an understanding of the role voice infrastructure plays with Voice AI. As contact center leaders look to add Voice AI to their expanding portfolio of AI capabilities, my intention is to make clear how considerations for supporting voice infrastructure are just as important as those for conversational AI.

While complementary, the requirements for each are quite different, and both are best served by best-of-breed partners. As AI platforms become CX differentiators, and as long as voice remains the most important communications channel for CX, voice infrastructure is fundamental to the success of Voice AI. This article makes the case for best-of-breed, not just for getting the best outcomes with Voice AI, but to provide the most flexibility for contact centers as their deployments continue evolving.

A likely US administration ban on Chinese optical transceivers for AI data centers may have an unintended consequence: IT will rush to buy as many of the components as possible before restrictions kick in.

The US Federal Communications Commission (FCC) “is working on the measure to bar imports of new Chinese optical transceivers” and officials hope to publish and implement it this year, Reuters reported on Tuesday.  

The report, citing four sources familiar with the matter, said that the official rationale is “to prevent Chinese firms from stealing data, installing malware or disrupting service at US data centers.” The sources did, however, stress that such a ban could still be modified or shelved.

A valid concern

Analysts and consultants agree that the concern, albeit hypothetical at the moment, is valid. 

If implemented, such a ban would have a severe impact on data center (DC) strategies for both enterprises and hyperscalers. Although higher costs for replacement products would be all but certain, the greater concern is the lack of availability of non-Chinese transceivers and other components, regardless of price. 

A potentially even more worrying element of a ban is the need for far more sophisticated supply chain visibility. That is because many of those non-Chinese component suppliers actually use some Chinese components in their products, which means that the exact wording of any potential FCC restrictions will be critical. It will define how closely enterprises will need to examine their suppliers’ supply chains.

Aman Mahapatra, chief strategy officer for Tribeca Softtech, a New York City-based technology consulting firm, said that he thinks that an FCC ban is quite likely, because it “has run this exact playbook four times in eighteen months, against drones, routers, robots, and the July 28 inverter and robotics restrictions. The mechanism is tested, the machinery is warm.”

If the ban is enacted, said geopolitical analyst Irina Tsukerman, “CIOs will need to reassess vendor diversification, and other factors such as replacement compatibility, lifecycle planning and inventory management, given that many organizations have historically treated optical components as interchangeable commodities.”

“Enterprises will also need much greater visibility into firmware development, manufacturing origin, as well as subcontractors, and software update processes, because future procurement decisions are increasingly likely to examine the entire supply chain rather than simply the company selling the finished product,” she added. This will make future procurement more complex.

IT pain will vary

Tsukerman said that, although prices would certainly spike, the pain felt will vary based on the nature and size of each affected business She noted that while hyperscale operators can negotiate directly with manufacturers, secure long-term supply, and qualify multiple vendors for critical components due to their purchasing power, enterprises, regional data center operators, and colocation providers generally lack that leverage. Rather, they often depend on distributors supplying lower-cost Chinese products, making them considerably more vulnerable to price increases and delivery delays.

Mahapatra added that the preliminary indications suggest any such ban would have a “new models only” framing that would protect the installed base while restricting the next generation of products, which, he said, would be a compromise “generous enough to mute the hyperscaler objection.”

But, he said, “the enterprise CIO running a colocation expansion or private AI cluster is about to discover they are competing with Microsoft and Meta for the same constrained supply and losing.”

He recommended that enterprises lock down forward optics supply for anything they plan to build through 2028 before the restriction publishes, because, he pointed out, “announced-but-not-effective bans consume non-Chinese capacity through panic buying, and buyers who move after publication pay in schedule rather than dollars.”

However, such a move depends on how serious IT considers the cybersecurity risks from the Chinese components. Tsukerman argued that data leakage and malware fears need to be taken seriously, because modern optical transceivers often contain firmware, onboard memory, and management interfaces, and may also offer capabilities that can influence how traffic is monitored and managed throughout the data center.

In that case, she noted, “the risk would extend beyond espionage to include compromised firmware updates, manipulation of diagnostic information, disruption of maintenance support, delayed replacement shipment, or in the worst case scenario, interference with critical infrastructure during periods of heightened political tension.”

However, Mahapatra sees the risk quite differently.

“A transceiver is a comparatively dumb device converting electrical signals to optical and back, not a router running a network operating system with deep packet visibility,” he said. “The near-term espionage risk from currently shipping Chinese optics is thin, and CISOs who reallocate budget toward this threat over their software supply chain and identity attack surfaces are responding to headlines rather than risk.”

The suppliers involved

Consultant Brian Levine, executive director of FormerGov, labeled the potential US administration move as “one of the more consequential supply-chain moves the FCC has contemplated, because optical transceivers are the workhorse components that move data across fiber inside every AI data center, and Chinese vendors dominate that market.”

He noted that Chinese vendors Innolight and Eoptolink alone reportedly account for the majority of the 800-gig modules going into Nvidia’s AI clusters, so a ban “wouldn’t be a minor substitution,” and non-Chinese alternatives such as Coherent and Lumentum in the US don’t yet have sufficient capacity to fill the gap.

Nader Henein, a Gartner VP analyst, agreed, adding that since the nature of the AI data center supply chain is both complex and fragile, a small change can deliver a disproportionate industry impact.

“If you remove one provider from the equation, it’s not as if the others have capacity to fulfil the increase in demand, so it’s not simply a question of added cost, it’s a question of placing a ceiling on capacity and growth,” he said.

Tsukerman said that her list of the companies most likely to benefit from such an FCC ban would include Coherent, Lumentum, Applied Optoelectronics and Cisco’s Acacia business, while Broadcom and Marvell, as well as  Japanese and Taiwanese manufacturers, also provide important optical and connectivity technologies that support advanced networking infrastructure.

Other components in the crosshairs

She pointed out that there is also a strong probability that a transceiver ban would quickly be followed by attacks on other components. 

Networking switches, SmartNICs, data processing units, baseboard management controllers, storage controllers, intelligent power distribution units, cooling management controllers, optical transport systems, and embedded management processors “all perform functions that could influence the operation of an entire facility if compromised,” she said. “None of these products simply passes data or delivers electricity. They manage, monitor, or control critical infrastructure, making them increasingly attractive targets for supply-chain attacks.”

Her list of likely future US targets for restrictions also includes top-of-rack switches, spine switches, and rack management systems,.

Flavio Villanustre, CISO for the LexisNexis Risk Solutions Group, echoed Henein’s fears about industry impact.

“I think that the appropriate response to these types of risks needs to be more nuanced than just a blanket ban,” he said. “Since 15%-20% of all world’s semiconductors are manufactured in China, and that number rises to 80% or 85% if you include Taiwan, blocking Chinese imports for these components could hamper the entire datacenter industry.”

Although there have been rumors of insecure or trojanized hardware components sourced from China in the past, given that many large American and multinational technology vendors manufacture their parts there, ”a sledgehammer approach could spike prices for these types of systems, jeopardizing development of new technologies,” he noted. “A far more reasonable approach would be to require appropriate testing and quality controls to ensure that those risks are appropriately mitigated.”

Would likely harm the US

In addition, independent technology analyst Carmi Levy said that he is skeptical about whether an FCC ban would ultimately be a good move for the US.

“It’s fair to ask whether this will truly make American technology infrastructure more secure, or whether it’s little more than a performative stunt designed to score geopolitical points,” Levy said, pointing out that Canada didn’t end up any safer because of the Huawei and ZTE ban, and “no one should fool themselves into believing a Chinese data center ban in the US would be any different. It would only add further constraints to a supply chain that’s already close to collapsing under its own weight [and it] will likely harm American interests more than anyone else’s.”

But he also concluded that such a move would likely fail, given the current global state of data center technologies. 

“Chinese suppliers and components have been so ingrained in the global technology supply chain for so long that no ban of any form could hope to have any tangible impact on so-called national security,” Levy said. “To claim otherwise exposes the true motivations of this misdirected policy strategy.”

This article originally appeared on NetworkWorld.