In this Technology Reseller News podcast, Todd Fisher of CTM returns for a hands-on look at how the platform connects marketing attribution with contact center activity—and how AI can help businesses understand what actually happens after a prospect picks up the phone.

CTM brings together inbound and outbound calling, web chat, web forms and integrations with platforms including Salesforce, HubSpot, Clio and Google Ads. The goal is to give marketing and sales teams a shared view of customer activity rather than leaving those functions in separate silos.

Fisher demonstrates how CTM can analyze a completed call once the transcript is available. Businesses can create AI-driven workflows that ask specific questions about each conversation, including whether the caller was a qualified lead, what product or service they wanted, their sentiment, the outcome of the call and other details important to the business.

“You can ask any number of questions about the outcome of a phone call,” Fisher says.

Those insights can then be analyzed across hundreds or thousands of conversations.

An insurance company, for example, could determine whether calls originated from a specific advertising campaign, why prospects called, what price points generated interest and whether customers were responding to the brand or another factor.

Healthcare organizations can examine insurance coverage, treatments being sought or other characteristics of incoming inquiries. Automotive businesses can automatically identify the year, make and model discussed during calls.

For marketers, that creates a much clearer connection between advertising spend and actual customer intent. For sales and contact center leaders, the same data can provide visibility into agent performance and why qualified prospects did—or did not—convert.

Fisher says this level of intelligence is no longer limited to large enterprises.

For MSPs, CTM represents an opportunity to bring sophisticated call tracking, attribution and AI analysis to smaller businesses that may not have dedicated marketing analytics or contact center teams.

The platform includes prebuilt AI prompts for functions such as lead scoring, call summaries, referral sources, outcomes and agent performance, helping partners and customers get started without building everything from scratch.

CTM can also provide the underlying phone platform, allowing organizations to manage agents, inbound and outbound calling, campaign tracking and integrations from the same environment.

“The use case of the business drives how we set it up,” Fisher says.

Visit CTM.com to learn more.

In this Technology Reseller News podcast, Tim Mandell of Leaseweb launches an ongoing infrastructure series focused on how MSPs can build successful AI practices—and why AI infrastructure can no longer be treated as a one-size-fits-all proposition.

Leaseweb is a 29-year-old, private and sovereign-by-design infrastructure-as-a-service provider. The company works with MSPs by supplying the underlying infrastructure while allowing the service provider to own the customer relationship and deliver the higher-value solution.

Mandell says one of the biggest mistakes organizations make is starting with the hardware.

The AI conversation is often dominated by GPUs, including the latest high-performance systems, but the most powerful hardware is not automatically the right infrastructure for every workload.

“You have to understand what problem you’re trying to solve before assuming the technology is going to solve it,” Mandell says.

AI training, inference and production workloads can have very different requirements. For many MSP customers, inference is particularly important because performance depends heavily on latency, data location, storage and connectivity.

Mandell points to an MSP that was losing customers because infrastructure had been placed in the wrong geographic location, creating latency problems that degraded the customer experience.

GPUs may get most of the attention, but Mandell says storage can sometimes become the larger and more complicated infrastructure challenge. Organizations can underestimate both the amount of data required and how quickly that data must be retrieved.

The same problem applies to sizing. MSPs can overengineer an environment and spend unnecessarily, or build something that lacks the flexibility required as customer demand grows.

“There really is no such thing as one AI infrastructure,” Mandell says. “Different applications require different compute, bandwidth, storage and placement.”

For MSPs, that creates an opportunity to move away from simply selling infrastructure and toward becoming strategic advisers.

Leading with a particular server, GPU or technology stack turns infrastructure into a commodity where price becomes the primary differentiator. Instead, Mandell recommends beginning with the customer’s business objective, asking where the organization is headed over the next several years and then designing the infrastructure around those outcomes.

“The infrastructure is going to be what it needs to be based on the business outcome,” Mandell says. “But if you lead with infrastructure, that becomes the foundation of the relationship.”

His takeaway for MSPs is straightforward: ask more questions, understand the workload and build value around the customer journey rather than the technology itself.

Visit Leaseweb.com to learn more.

In this Technology Reseller News podcast, Ben Prescott, Head of AI Solutions at Trace3, discusses why successful AI adoption depends on getting both the “first mile” and the “last mile” right.

Trace3 is a technology and consulting organization that helps enterprises adopt new technologies and transform business operations. Prescott works within the company’s digital consulting group, focusing specifically on AI strategy and implementation.

The “first mile,” Prescott explains, is about definition: understanding what the organization is actually trying to accomplish with AI, where the opportunities are, and which business problems are worth solving.

The “last mile” is about adoption. Once an organization has selected or built an AI solution, it still has to change workflows, educate users, drive adoption and demonstrate that the investment is creating value.

“The first mile is about where and when AI can provide value,” Prescott says. “The last mile is about influencing change and actually squeezing the juice out of the investment.”

Between those two points sits the technology itself—software, infrastructure, cloud services and increasingly AI-focused hardware. Prescott says infrastructure matters, but it should be connected to a clearly defined business objective.

That discipline is becoming increasingly important as enterprises struggle with AI sprawl. Employees may already be experimenting with unsanctioned tools, while organizations are simultaneously evaluating multiple platforms, infrastructure investments and governance policies.

Trace3 recommends that organizations identify core AI platforms, establish acceptable-use policies and create a structured process for evaluating employee and business demand.

Prescott says the market has also shifted from experimentation toward measurable value. Organizations that rushed into AI investments are now asking harder questions about duplication, cost and return.

“We’re getting to the stage where value matters more than it did over the last couple of years,” he says.

The approach applies across commercial enterprises, government, defense and other sectors. While security and regulatory requirements may vary, Prescott says the underlying discipline remains the same: define the problem, choose the right technology, drive adoption and measure the outcome.

His advice to organizations beginning or expanding their AI programs is to resist the pressure to move too quickly.

“Take a more methodical approach,” Prescott says. “Build the process, develop the muscle memory, and then you can move faster.”

Visit Trace3.com to learn more.

In this Technology Reseller News podcast, Vanessa Jensen, Senior Account Executive at Shure, discusses how the company is expanding its IntelliMix collaboration portfolio for Microsoft Teams Rooms and Zoom Rooms.

Shure’s IntelliMix Room Kits are designed for spaces ranging from small rooms to larger, more complex deployments. The company has also introduced the IntelliMix Bar Pro, an all-in-one solution certified for medium to large Microsoft Teams Rooms.

Jensen says customers increasingly want a consistent meeting experience across different room sizes, locations and collaboration platforms.

“They need solutions that are easy to deploy, easy to standardize on and easy to manage at scale,” Jensen says.

The IntelliMix Bar Pro combines Shure audio, intelligent video, built-in compute and enterprise management in a single system. It includes AI-assisted audio processing, stereo loudspeakers and a four-camera system with intelligent framing to help ensure that participants are clearly heard and accurately seen.

That quality is becoming more important as AI increasingly uses meeting audio and video to generate transcripts, summaries, action items and follow-up workflows.

“If someone is not heard clearly or represented accurately, the AI may be working from incomplete information,” Jensen says.

Shure is also bringing features such as AI denoising and de-reverberation into its collaboration products to improve audio in challenging environments. The goal is for the technology to fade into the background so meeting participants can focus on the conversation rather than the equipment.

The portfolio can scale from simple meeting rooms to larger environments using ceiling array microphones, camera tracking and other advanced capabilities. Shure Cloud also enables IT teams to remotely monitor and manage rooms and receive alerts when attention is required.

For Zoom environments, Shure offers IntelliMix Room Kits for small through extra-large Zoom Rooms, combining Shure audio and control with video technology from Huddly. More customized systems are also available for complex spaces.

Jensen says the products are designed for straightforward deployment and everyday use, with many configurations operating as plug-and-play systems once the appropriate Teams or Zoom licensing is in place.

MSPs and channel partners can also work with Shure to add its collaboration solutions to their portfolios.

Visit Shure.com to learn more.

By Doug Green

“A renewal should not just be a look in the rear-view mirror.”

In Part Two of the Technology Reseller News SmarTrak series, I talk with Jay Pierce of SmarTrak.ai about how Cisco partners can turn renewal and lifecycle data into longer-term customer strategies.

Pierce describes the approach as a “windshield view” — looking ahead at what a customer may need over the next three to five years instead of simply reviewing what they bought last time. That means considering hardware, software, services, Cisco buying programs and the customer’s broader business plans as part of the renewal conversation.

The discussion also looks at how AI can help partners identify transformation opportunities, while recognizing that raw data alone is not enough. Pierce points to data protection, Cisco program knowledge and context as important factors in turning information into useful partner intelligence.

For Cisco partners, the opportunity is to move beyond a transaction-based renewal process and use lifecycle information to build a more complete account strategy — one that expands the customer conversation and helps partners plan for what comes next.

This is Part Two of the SmarTrak series: Turning Cisco Renewals into Five-Year Plans.

Next in the series: Clean Data Before Artificial Intelligence.

“You can access these devices at the BIOS level, regardless of the state of the system.”

In this Technology Reseller News podcast, Dawn Siskowski, Technical Product Manager at Legrand’s Raritan division, discusses the newly launched Dominion KX III G2, the company’s next-generation enterprise KVM-over-IP switch.

Raritan has been developing KVM and intelligent power solutions for more than 40 years. Siskowski says the new Dominion KX III G2 builds on that history while addressing the requirements of modern, distributed IT environments.

The platform provides secure BIOS-level remote access to servers and other devices, allowing IT teams to manage systems even when operating systems have crashed or machines are turned off.

“You don’t have to have someone onsite,” Siskowski says. “You can access the hardware at the lowest possible level.”

The Dominion KX III G2 scales from smaller environments to large enterprise deployments and can support up to 96 servers from a single switch. Raritan has also improved video performance, delivering up to 60 frames per second for use cases ranging from traditional IT administration to broadcast, AV and other environments where image quality is critical.

The platform is device agnostic and designed to operate over existing network infrastructure without requiring a separate management network.

For service providers and enterprise IT teams, remote access can also reduce recovery times during major outages. Siskowski points to incidents where technicians have been forced to travel to individual locations simply to restore or patch inaccessible systems.

“With KVM in place, you can do that work remotely,” she says. “You save travel costs and improve response time.”

As organizations operate leaner IT teams and increasingly manage infrastructure across multiple locations, Siskowski says secure remote management becomes even more important.

Visit Raritan.com to learn more about the Dominion KX III G2 and Raritan’s KVM portfolio.

“If you can’t see something, you can’t secure it.”

AI security discussions often focus on models, applications and data. But Chris Hosking, GTM Advisor at Stealthium, says organizations may be overlooking a critical layer: the accelerated computing infrastructure where AI actually runs.

In this Telecom Reseller podcast, Hosking discusses Stealthium’s partnership with Tenstorrent and a broader emerging challenge for enterprises, cloud providers and operators — securing GPUs and other AI accelerators that traditional security tools often cannot fully observe.

Stealthium describes itself as a runtime observability and security company for AI infrastructure. Its goal is to give organizations visibility directly into accelerated compute environments, below the point where many conventional security tools stop.

“There’s an architectural gap here when it comes to controls,” Hosking says. Existing security instrumentation may provide extensive visibility at the endpoint, CPU and driver layers, but organizations increasingly need to understand what is happening inside the accelerator runtime itself.

That becomes especially important as GPUs move from specialized computing resources to critical enterprise infrastructure.

Hosking describes GPUs as environments that increasingly contain an organization’s “crown jewels” — including sensitive model weights, proprietary AI workloads and potentially valuable customer or corporate data.

And GPUs are not simply black boxes performing calculations. They are programmable compute environments with memory, network access and the potential for persistence. That means they also create new attack surfaces.

Stealthium’s recently announced partnership with Tenstorrent is designed to address that challenge by bringing runtime observability and security controls deeper into Tenstorrent’s AI infrastructure.

Hosking points to Tenstorrent’s open, full-stack architecture as particularly important because it enables security and observability to be incorporated into the infrastructure rather than added later.

The discussion also explores the security risks associated with shared GPU infrastructure.

Hosking cites vulnerabilities such as Januscape as an example of how weaknesses involving virtualization and isolation can become particularly serious when multiple customers share physical accelerated-compute infrastructure. Customers need more than an architecture diagram saying workloads are isolated, he argues — they increasingly need evidence that the isolation is actually working.

That leads to several basic questions AI infrastructure operators should be able to answer:

• What actually ran on the infrastructure?
• Was my workload isolated?
• Am I receiving all the compute capacity I paid for?
• Is another workload consuming resources?
• Is something behaving abnormally?
• Can I prove that the infrastructure is operating securely?

Runtime visibility can therefore become more than a cybersecurity tool. It can also help organizations identify resource hijacking, inefficient GPU utilization and expensive compute capacity being consumed without operators realizing it.

The issue takes on additional importance as enterprises pursue private and sovereign AI strategies.

Organizations may own or control their infrastructure, but Hosking argues that sovereignty ultimately requires the ability to verify what is happening inside that infrastructure.

Over the next six to twelve months, he expects security attention to move increasingly toward AI hardware and accelerated computing. As GPU adoption grows, vulnerabilities and attacks targeting these environments are likely to receive far greater scrutiny.

For enterprises, service providers and cloud operators, the takeaway is straightforward: AI infrastructure is becoming too important — and too privileged — to remain a security blind spot.

“If you can’t see something, you can’t secure it,” Hosking says.

Learn more at Stealthium.io.

“It’s really touched so much of what we do and how we think about our practice and how we communicate. It’s been amazing for us.”

In this follow-up to our earlier conversation with Weave CTO Abhi Sharma, we move from the technology strategy to the experience of an actual healthcare practice. Dr. Jeffrey Ellis, co-founder of Belaray Dermatology on Long Island, joins us to discuss how Weave has changed patient communication, front-office workflow and the way his practice is beginning to use AI.

Ellis says the practice had reached a point where staff were juggling multiple systems at once. At one stage, employees had “seven different windows open” as they tried to manage calls, scheduling and other patient communications. At the same time, phones were constantly ringing, front-desk employees were serving patients in person while answering calls, and multiple callers could be waiting on hold. “It just wasn’t good for anyone, for my staff or for patients,” Ellis says.

Weave helped consolidate those communications and move more routine interaction to text. Patients can text the office 24/7, with staff monitoring messages seven days a week. The platform is also used for appointment reminders, visit-specific instructions and post-visit outreach. Ellis says that shift has reduced pressure on the front desk while making it easier for patients to communicate with the practice.

The impact can go beyond convenience. Ellis gives the example of a patient coming in for a full-body skin check while wearing nail polish. Because skin cancer can sometimes occur beneath fingernails or toenails, the practice wants patients to arrive prepared so those areas can be examined. Better reminders and pre-visit communication can therefore support a more complete exam and, ultimately, better patient care.

AI is adding another layer. Ellis explains that an AI tool can review a very large number of conversations and surface the few that may need his attention. “That AI tool might listen to a thousand conversations and pull out three of them for me to review. I can review three conversations. I can’t go through all of them.” For a smaller independent practice, that can provide a level of quality oversight that would otherwise be difficult to achieve.

The larger takeaway is that AI and better communications do not have to make healthcare less personal. In Ellis’s experience, they can reduce administrative friction, give staff more time to focus on patients and help an independent practice deliver a more consistent patient experience.

“Training as an event is not effective. It’s just not effective. It doesn’t work,” Thiel says, pointing out that leaders often leave a conference or training session energized, only to fall back into old habits once they return to the pressures of daily work.

Thiel argues that leadership happens “one conversation at a time.” Managers may need to address an employee performance problem, mediate a conflict, discuss a promotion, coach a talented employee or manage their own emotional reaction—all while dealing with customers, deadlines and other pressures.

The challenge is especially significant for growing technology companies, where strong technical employees are often promoted into management roles and suddenly expected to have advanced skills in coaching, feedback, conflict resolution, recognition and communication.

Thiel discusses The Vault, TTS Company’s leadership development community designed to move beyond one-time training. Available on desktop and as an app, The Vault provides short weekly leadership challenges, practical resources and ongoing coaching designed to help managers develop new habits and find guidance when they actually need it.

Rather than waiting for the next conference or searching through an old training binder, a manager preparing for an interview, difficult conversation or coaching session can access relevant resources immediately.

The conversation also explores TTS Company’s broader fractional HR model. The company works with growing businesses that need ongoing HR expertise but may not require a full internal HR department, helping with employee issues, manager coaching, HR best practices, acquisitions and the added complexity that comes with growth.

Learn more about TTS Company and The Vault at thettscompany.com.

Nearly 85% of healthcare organizations experience network or security disruptions that can affect patient care and privacy, while two-thirds of healthcare IT teams say they are strained or constantly firefighting, according to new research released by Nile.

The findings are part of Nile’s new report, “The State of Networking, Security & AI in Healthcare,” based on responses from more than 300 IT leaders, C-level executives, and network and security practitioners worldwide.

In a Technology Reseller News podcast, I spoke with Shashi Kiran, CMO of Nile, about what the findings reveal about healthcare networking and why the problem goes beyond cybersecurity tools to the underlying architecture.

Healthcare networks now connect hospitals, outpatient facilities, imaging centers, remote locations and large numbers of medical and IoT devices. Many of those devices cannot simply be patched or equipped with traditional security agents, yet they may operate alongside critical systems such as electronic health records.

That complexity has consequences. According to Nile, 38% of healthcare organizations experience network or security disruptions at least weekly. Cybersecurity threats and ransomware ranked as the top network challenge at 27%, ahead of staffing shortages and lack of expertise at 23%.

Kiran argues that adding more staff alone will not solve the problem. About 66% of healthcare IT teams described themselves as strained or firefighting, while only 18% said they were comfortably managing network operations.

“Most often people think that it’s throw more resources at the problem,” Kiran says. “More often than not, that’s not the case.”

Zero Trust and the Containment Gap

Only 38% of organizations surveyed report partial or full Zero Trust implementation, while 26% are aware of Zero Trust but have no concrete plans to implement it.

The research also found that only 20% of respondents are confident their networks can contain a cyber incident across medical, IoT, clinician and guest devices. Sixty-one percent are not confident or only slightly confident.

Kiran says the challenge is creating a consistent security posture across environments that may include conventional IT, operational technology and medical devices managed by different teams.

“Zero Trust is not a protocol or a feature,” he says. “It’s really a holistic posture that you need to have on an end-to-end basis.”

He also points to the complexity of legacy infrastructure, describing some environments as a “Frankenstein architecture” assembled over time from different systems, vendors and operational processes.

That complexity can make troubleshooting more difficult and itself become a security problem.

AI and NaaS

The report also shows strong interest in new operating models. Sixty-four percent of healthcare organizations surveyed are open to adopting Network-as-a-Service to simplify operations and strengthen security, while 20% of more mature organizations now view AI-driven automation as essential to running their networks.

Nile argues that combining Zero Trust, automation and networking into the infrastructure itself can help healthcare IT organizations move from constant firefighting toward more proactive operations.

For Kiran, that shift ultimately comes back to healthcare’s core mission.

“In healthcare, the network is the lifeblood of patient care — when it falters, both patient care and patient privacy are put at risk,” he said in announcing the research.

The takeaway is that networking, security and patient care are increasingly interconnected. As healthcare organizations enter their next infrastructure refresh cycle, Nile believes the opportunity is not simply to replace old equipment, but to rethink an architecture that has become too complex to operate and secure effectively.

The full “The State of Networking, Security & AI in Healthcare” report is available from Nile.