Lenovo 北美中小企業務總經理 Matt Meyers 專訪

Matt Meyers is General Manager, North America SMB at Lenovo, where he works with small and midsized businesses navigating AI adoption, cybersecurity, and the operational demands of modern IT. He has spent more than a decade at Lenovo, with experience spanning global accounts, services and software, digital workplace solutions, and commercial leadership. That progression has shaped his focus on helping smaller organizations access capable, secure technology that fits their budgets and lean IT teams. His perspective centers on practical business outcomes: identifying useful AI applications, matching devices and services to employee needs, and measuring the value of technology investments before expanding them.

Lenovo is a global technology company whose portfolio spans personal computing, infrastructure, software, solutions, and services. Its business offerings include AI-enabled PCs, digital workplace solutions, ThinkShield security, and TruScale subscription-based services. For small and midsized businesses, these capabilities bring together devices, support, security, and lifecycle management to help simplify technology operations. As AI becomes part of everyday business applications and workflows, Lenovo is developing solutions across devices, edge, and cloud environments, giving organizations options for how they deploy and manage their technology.

You’ve spent more than a decade at Lenovo, moving through global accounts, services and software, digital workplace solutions, commercial leadership, and now North America SMB. How has that progression changed the way you think about the technology challenges facing small and midsized businesses compared with large enterprises?

Throughout my career at Lenovo, I’ve found that the fundamental technology challenges are remarkably consistent regardless of company size. Every organization is trying to improve productivity, strengthen security, control costs and determine how to leverage new innovations such as AI to create competitive advantage.

Where SMBs differ is in resources. Large enterprises often have dedicated teams focused on security, infrastructure, procurement, AI and digital transformation. In many SMBs, those responsibilities may fall to a handful of people, or even a single individual. That leaves very little room for complexity or missteps.

What that has taught me is that SMBs don’t need scaled-down versions of enterprise technology. They need solutions designed around the realities of how they operate. Technology must be secure, easy to deploy, easy to manage and capable of delivering value quickly. When we get that right, technology becomes a growth enabler rather than another operational burden competing for time and attention.

AI adoption can feel overwhelming for an SMB with a small IT team and limited budget. What are the first two or three things a business should have in place before it starts investing seriously in AI?

The biggest mistake I see organizations make is starting with the technology instead of the business problem.

The first step is identifying where AI can create measurable impact. That might be reducing administrative work, improving customer responsiveness, helping employees access information faster, or streamlining decision-making. The strongest AI initiatives are tied to a specific business outcome.

Second, understand your data. Organizations need a clear understanding of what information they have, where it resides, who has access to it and what information should never be entered into public AI environments. You don’t need perfect governance before getting started, but you do need a foundation.

Third, define success before making the investment. If the goal is productivity, establish a baseline. If the goal is cost reduction, understand today’s costs. A practical approach is to start with focused use cases, prove value and then scale based on results rather than assumptions.

From the SMB customers you work with, where are you seeing AI generate tangible value today, and which use cases are still being overhyped relative to the results businesses are actually achieving?

The most immediate value is in everyday productivity.

We are seeing organizations use AI to summarize information, create and refine content, analyze documents, surface insights and reduce repetitive administrative work. None of those tasks individually may seem transformative, but collectively they create meaningful efficiency gains across an organization.

Where expectations are sometimes ahead of reality is around complete automation. There’s still a perception that AI can fully replace complex roles or business processes with minimal oversight. For most SMBs today, that’s not where the greatest value is being realized.

The strongest results come from using AI to augment employees, remove friction from workflows and accelerate decision-making. The best starting point remains repeatable, high-frequency tasks where the impact is measurable and adoption can happen quickly.

Many companies are being encouraged to upgrade to AI PCs. For an SMB owner, what workloads genuinely benefit from running AI locally on the device, and when does a conventional PC combined with cloud-based AI remain sufficient?

There isn’t a universal answer because not every employee works the same way.

AI PCs deliver the most value for users who regularly rely on AI-enabled applications, real-time transcription and translation, advanced collaboration tools, content creation workflows or workloads involving sensitive information. Local AI can improve responsiveness, reduce reliance on connectivity and enable certain AI functions to run directly on the device using dedicated processing resources. For an organization that wants certain information to remain on the device, local AI may be useful, subject to its security policies and the application in use.

At the same time, not every employee requires an AI PC today. Employees primarily using traditional productivity applications and occasional cloud-based AI services may continue to see strong performance from conventional systems.

The conversation shouldn’t be about refreshing devices simply because AI PCs are available. It should be about matching the right technology to the employee, the workload and the future needs of the business. The most effective technology strategies align investments with outcomes rather than trends.

Lenovo has been expanding its AI PC strategy while also offering Device-as-a-Service models through TruScale. How can a smaller company determine whether buying and managing its own hardware or moving toward a subscription-based model makes more financial and operational sense?

I encourage SMB leaders to evaluate both the financial impact and the operational reality of managing technology.

Traditional ownership can make sense when device requirements are predictable, the organization has capital available for an upfront investment and it has the resources to manage deployment, support, lifecycle management and refresh planning internally.

A Device-as-a-Service model can be attractive when the goal is predictable monthly costs, flexibility and reducing operational burden. Solutions such as Lenovo TruScale DaaS combine devices and lifecycle services into a subscription model, helping organizations simplify deployment, support, management and retirement of devices.

Ultimately, the question isn’t just how a company wants to pay for technology. The question is where it wants its people spending their time. For many SMBs, the real value comes from allowing internal teams to focus on strategic priorities that drive growth rather than managing device lifecycles and day-to-day administration.

Cybersecurity is becoming more difficult as employees gain access to generative AI tools and potentially expose sensitive company data. What new security risks should SMBs be paying particular attention to as AI becomes embedded in everyday workflows?

One of the most immediate concerns is data exposure.

Employees can unintentionally share proprietary information, customer data, financial information or intellectual property through AI tools without fully understanding how that information is stored, processed or used. Organizations need clear policies around approved AI tools and data handling practices.

At the same time, AI is increasing the sophistication of cyber threats. Phishing campaigns, impersonation attempts and social engineering attacks are becoming more convincing and more difficult to identify.

Despite these new challenges, the fundamentals remain critically important. Strong identity management, multifactor authentication, endpoint protection, regular patching, backup strategies and employee education continue to be some of the most effective defenses available. AI changes the threat landscape, but it doesn’t change the importance of strong cybersecurity discipline.

Lenovo has positioned ThinkShield solutions around organizations with limited internal IT resources. How much of cybersecurity can realistically be automated or outsourced today, and which security responsibilities should an SMB never completely hand over to a third party?

A significant portion of day-to-day security operations can be automated or supported by trusted partners today.

Capabilities such as endpoint monitoring, patch management, threat detection and incident response can increasingly be delivered through automation and managed services. For SMBs, that can provide access to enterprise-grade capabilities without requiring enterprise-sized teams.

ThinkShield is one example of how technology can support automation and help organizations manage security complexity. However, accountability cannot be outsourced.

Every organization still needs to understand its most critical data, determine who should have access to it, establish policies, define acceptable risk and create a culture where security is everyone’s responsibility. Technology and service providers can help manage complexity, but leadership must maintain ownership of risk management, governance and business decisions.

The strongest security strategies are built on partnership, not delegation.

Integrated services can reduce the burden on lean IT teams, but they can also increase reliance on a small number of technology providers. How should SMBs balance simplicity and managed services against concerns such as flexibility, interoperability, and vendor lock-in?

For most SMBs, simplicity has real strategic value.

Every additional vendor, platform and management layer introduces complexity. That complexity consumes time, increases operational overhead and can distract limited resources from higher-value priorities.

That said, simplicity should never come at the expense of flexibility. Organizations should look for solutions that integrate well with their existing environments, support open ecosystems and allow them to evolve as business requirements change.

I always encourage leaders to ask potential technology partners about interoperability, portability and flexibility early in the process. The best technology partnerships create options and reduce complexity. They shouldn’t make future change more difficult or expensive.

One challenge with AI is determining return on investment before committing significant resources. What metrics should SMB leaders use to decide whether an AI deployment is actually improving productivity or reducing costs rather than simply adding another technology layer?

The most effective measurement approach is also the simplest: tie outcomes back to the original business objective.

If the goal is improving productivity, measure time saved. If the goal is improving customer experience, measure response times, customer satisfaction and resolution rates. If the objective is cost reduction, compare the process before and after implementation.

I would also pay close attention to adoption. A solution may be technically impressive, but if employees aren’t using it consistently or are spending significant time correcting outputs, that’s meaningful feedback about the value being created.

For SMBs, ROI doesn’t need to be overly complex. Establish a baseline, run a focused pilot, measure a handful of meaningful metrics and scale based on evidence. If you can’t clearly articulate what improved, it’s worth reevaluating the use case before expanding the investment.

Looking ahead over the next three to five years, how do you expect AI to change the technology stack of a typical small or midsized business, and what capabilities that are considered enterprise-grade today do you expect to become commonplace among much smaller companies?

I believe AI will increasingly become embedded across the technologies businesses already use rather than existing as a separate category of tools.

We’ll see intelligence integrated across devices, collaboration platforms, business applications, operating systems and security solutions, with workloads moving between device, edge and cloud environments based on performance, cost and security requirements.

For SMBs, that’s incredibly significant because capabilities that once required substantial budgets and large IT teams will become far more accessible. Advanced security monitoring, intelligent automation, sophisticated analytics, proactive device management and highly personalized employee support will be within reach for more SMBs.

At its core, AI is helping democratize access to technology. A small business shouldn’t need the budget, resources or staffing levels of a Fortune 500 company to compete effectively. Over the next several years, I expect the gap between what is possible for large enterprises and what is possible for SMBs to continue narrowing.

The real differentiator won’t be access to AI. It will be how effectively organizations apply it to empower employees, serve customers and drive business outcomes.

Thank you for the great interview, readers who wish to explore practical AI adoption and secure, manageable IT for small and midsized businesses should visit Lenovo.