Preparing for AI and Automation in 2026: IT Projects That Matter Most

Over the past year, there’s been no shortage of headlines offering advice on how organizations should prepare for AI-driven transformation. Analysts, vendors, and consultants alike are publishing frameworks, maturity models, and predictions for what 2026 will bring.

In a previous discussion on agentic AI, we explored how IT’s role evolves as systems become more autonomous.  Then following up within this article we will discuss those projects and how that responsibility becomes real.  Despite the variety of perspectives, most of these stories converge around a familiar set of themes:

• Infrastructure
• Responsibility
• Trust
• Humans in the loop
• Company data as the engine for AI

These are all critical considerations. Some, like trust and responsibility, demand deep governance, cultural change, and executive alignment. They take time, careful planning, and cross-functional ownership.

Others, however, are far more approachable—and far more actionable—right now.  From Morefield’s perspective as a managed service provider in Central Pennsylvania and technology advisor across multiple industries, 2026 readiness starts with (2) foundational project areas that organizations can—and should—prioritize today:

1. Infrastructure
2. Data

When you get this right, your organization will have the conditions for responsible, trustworthy, and scalable AI adoption. Ignore them, and even the most advanced AI initiatives will stall under their own weight.

Why AI Readiness Is an Infrastructure Conversation First

AI and automation are not “plug-and-play” workloads. They place very different demands on IT environments compared to traditional line of business applications.

As organizations look ahead in 2026, AI-driven systems will increasingly operate as distributed, always-on, multi-agent environments—not as single applications running in isolation.

That reality has major implications for infrastructure planning.

Advanced Networking Is No Longer Optional

AI agents need to communicate—constantly. They exchange signals, context, and results across systems in real time. That means networks must evolve well beyond basic connectivity.

Organizations should be planning for

• Ultra-low latency networking to support real-time decision-making
• High-throughput architectures capable of moving large data sets efficiently
• Energy-efficient designs that control operating costs as workloads scale
• Security embedded at every layer, not bolted on afterward

In practical terms, this often means refreshing core switching, modernizing WAN architectures, adopting software-defined networking, and rethinking how edge locations connect back to centralized resources.

For many SMB | Midmarket organizations, this is less about bleeding-edge technology and more about eliminating bottlenecks from legacy systems.  Systems that AI will quickly expose.

Flexible, Scalable Compute Is the New Baseline

AI workloads are bursty by nature. Demand spikes. Models retrain. Agents scale up and down dynamically.  Rigid, fixed-capacity infrastructure struggles in this environment.

As your team plans, compute strategies should prioritize:

• Hybrid architectures that blend on-prem, cloud, and edge resources
• Elastic scalability to align cost with actual usage
• Workload portability, avoiding lock-in that limit future options

This is where many organizations discover that yesterday’s “cloud-first” strategy isn’t enough. AI introduces workloads that may need to live close to users, machines, or data sources—while still integrating with cloud-based intelligence.

Multi-Nodal Architectures Reflect How AI Actually Works

One of the most overlooked infrastructure shifts is the move toward multi-nodal architectures.  In an AI-enabled domain, some agents will operate in the cloud.  Others run at the edge—inside facilities, warehouses, or branch offices.  And then humans monitor, intervene, and guide outcomes in real time.

This requires environments where workloads can operate in concert, not silos. Networking, identity, monitoring, and security must be consistent across every node.

Organizations that plan for this now will move faster later—without re-architecting under pressure.

Your Company Data Is the Real AI Differentiator

If infrastructure is the foundation, your company data is the fuel.

Agentic AI systems rely heavily on human-generated company data—documents, communications, operational records, transactions, and institutional knowledge. Unlike public internet data, this supply is finite and deeply contextual.

That reality introduces both opportunity and risk.

Identify and Prioritize High-Value Data Sources

Not all data is equally valuable to AI systems.  A critical planning exercise is identifying:

• Which data sets will drive the most meaningful AI outcomes
• Where that data currently lives
• How frequently it changes
• Who owns and governs it

This often reveals data sprawl, duplication, and inconsistent access controls—issues that must be addressed before AI agents are allowed to act on that information.

Manage Overlapping Data with Intentional Silos

AI does not eliminate the need for separation of duties. In fact, it reinforces it.

Where overlapping data sets exist, organizations will need to intentionally silo data to maintain operational boundaries between AI agents. This helps reduce unintended cross-influence between processes.  Improves explainability of outcomes and supports compliance and audit requirements.

Silos are not about isolation—they’re about control and clarity.

Plan for the Explosion of Synthetic Data

AI agents don’t just consume data. They will generate it.  Likely a lot of it.

Automated processes, simulations, predictions, and derived insights all create synthetic data that must be stored, secured, and governed.

Organizations preparing for production AI should be asking:

• Where will synthetic data live?
• How long is it retained?
• How is it distinguished from human-generated data?
• How is it used to retrain or influence future models?

Ignoring this creates risk. Planning for it creates leverage.

Adopt Platforms Designed for Both Human and Synthetic Data

Traditional data platforms weren’t designed for AI-scale complexity.  Forward-looking organizations are evaluating platforms optimized to handle large volumes of unstructured data.  Support AI-native analytics and workflows and still enforce security and governance consistently.

This is not a rip-and-replace conversation for most SMB | Midmarket organizations. It’s about evolution with intention.

What does this translate to on a roadmap?

For business leaders, this preparation ultimately takes shape as a small set of well-defined, multi-year initiatives rather than one monolithic “AI project.” In practice, that often includes a network modernization program to reduce latency and eliminate bottlenecks, a hybrid compute strategy refresh that aligns on-prem, cloud, and edge resources to support bursty AI workloads, and a data foundation initiative focused on identifying high-value data sets, tightening access controls, and reducing sprawl. Increasingly, forward-looking teams are also beginning synthetic data planning—defining where AI-generated data will live, how it’s governed, and how it influences future automation. These are familiar IT motions, but viewed through an AI readiness lens, they become strategic enablers that compound value over time rather than one-off infrastructure upgrades.

Infrastructure and Data: The Fastest Path to Real AI Value

Trust, responsibility, and human oversight will always matter. They require leadership, policy, and culture.  But infrastructure and data? Those are solvable—with planning, projects, roadmaps, and investment.

Organizations that prioritize these areas will be positioned to:

• Generate original insights from their own operations
• Automate complex workflows safely
• Solve problems that were previously out of reach
• Expand what’s possible without increasing risk

AI transformation doesn’t start with algorithms.
It starts with preparation.

As your technology partner, Morefield’s role is to help you make smart, practical decisions today—so AI becomes an advantage tomorrow, not an experiment that never delivers.

If 2026 is on your roadmap, now is the time to build the foundation.

Across organizations of all sizes, IT teams face the same challenge. Systems generate more data and alerts than people can act on fast enough. Traditional automation and analytics provide insights, but they still depend on human coordination to move work forward, slowing response and increasing risk.

Agentic AI addresses this gap by allowing AI systems to take limited, governed action after analysis. Rather than waiting for manual intervention, agents can trigger workflows, interact with systems and escalate decisions based on predefined rules. In practice, this autonomy is constrained and shaped by IT architecture and governance models.

As AI systems move closer to execution, IT plays a central role in determining how safely and effectively they operate. This article explains the role of IT in agentic AI, outlining how IT teams support agent-driven environments through infrastructure, security, governance, operations and change management.

How Agentic AI Changes the Operational Landscape

Agentic AI changes operations by allowing AI systems to initiate actions, not just generate insights. In a production environment, this execution is tightly constrained by architecture, data access and governance controls.

Most agentic AI deployments today operate with:

• Defined action scopes tied to specific systems or APIs.
• Event-driven execution triggered by monitored conditions.
• Enforced limits on permissions, spend and system writes.
• Required handoffs for decisions exceeding policy thresholds.
• Continuous logging of prompts, actions, tool calls and outcomes.

These systems rely on reliable data pipelines, identity controls for AI agents and enforced decision boundaries to function safely. Autonomy is incremental and conditional, not open-ended.

As execution moves closer to the production systems, the role of IT becomes operationally significant. IT teams help translate policies into technical controls. They manage integration points, so agent-initiated actions remain predictable and within the defined risk tolerance.

The 5 Functions of IT in an Agentic AI Framework

Agentic AI places new execution responsibilities inside operational systems, which means IT’s role extends beyond just support and integration. When AI agents can initiate actions and influence outcomes, IT becomes responsible for the technical conditions that enable autonomy to be controlled, observable, and reversible.

These responsibilities fall into five functions that shape how agentic AI operates in live environments. Together, they define how autonomy is applied as agentic systems move from initial deployment into day-to-day use.

1. Building the Foundation: Platforms and Infrastructure

Agentic AI depends on infrastructure designed for continuous execution, not batch-level analysis. IT teams must support computing resources alongside real-time data pipelines, event streaming, vector databases, configuration stores and low-latency API access.

Reliability matters because agents act automatically when certain conditions are met, rather than on a scheduled cycle. Weak data quality or brittle integrations can quickly become operational risks.

2. Implementing Proactive Security and Monitoring

Autonomous execution introduces new threat models that make monitoring a security function, not just an operational one. IT must account for factors such as agent identities, credential scoping, tool misuse and dependency integrity.

Controls typically include sandboxed execution, rate limits, environmental constraints, rollback plans and continuous security monitoring. Detailed logs of prompts, decisions, actions and intermediate states must be saved as evidence for audits and incident investigations.

3. Establishing Clear AI Governance Policies

In agentic terms, governance means defining exactly what an AI agent is allowed to do, under what conditions and with what level of human oversight. IT plays an important role in implementing this through technical controls, not just policy documents alone.

Many organizations formalize this through an internal AI agent registry that documents each agent’s purpose, scope, ownership, versions and environment for accountability during audits and reviews. These controls typically include:

• Defining autonomy levels and approval thresholds for agent actions.
• Enforcing data access rules, limits and privacy constraints.
• Implementing bias and fairness checks where agents influence decisions.
• Ensuring explainable decision logs for high-impact actions.
• Aligning controls with sector-specific regulations and internal risk policies.

4. Managing Day-to-Day AI Operations

Once launched, agentic systems require continuous operational oversight to maintain performance and behavioral stability. IT teams manage how agents move into production and evolve over time, while tracking behavioral drift, runtime health and cross-agent interactions.

This visibility helps identify early warning signs before minor issues become failures at an operational or regulatory level.

5. Leading a Smooth Change Management Process

Agentic AI changes the way decisions are made. IT supports this by clarifying updated Responsible, Accountable, Consulted, and Informed models, defining what agents can decide versus what requires human approval.

It also trains teams to recognize new failure modes, such as cascading automated actions or over-trust in AI outputs. This operational clarity reinforces the role of IT in agentic AI as systems move closer to execution.

Essential Tools for Your Agentic AI Toolkit

Supporting agentic AI in production requires tooling that goes beyond traditional monitoring or automation platforms. Because AI agents initiate actions, interact with multiple systems and operate with restricted autonomy, IT teams need tools that emphasize visibility, control and risk management, as well as performance.

An effective agentic AI toolkit includes:

Observability and Monitoring Platforms

These tools provide deep visibility into agent behavior by tracking action success and failure rates alongside latency metrics and reliability data. They monitor override frequency while flagging safety violations, whether a policy breach, unauthorized system write or an agent attempting to execute forbidden actions.

To satisfy audit evidence requirements and enable root-cause analysis, logs must capture the complete decision chain. Prompts flow into decisions, decisions generate intermediate states and those states trigger tool calls.

This comprehensive tracking ensures teams can respond to regulatory scrutiny with confidence, as every agent action has been documented and can be traced back throughout its entire execution path.

AI Safety and Governance Software

Governance platforms enforce policy-as-code, risk-tiering, approval workflows and human-in-the-loop thresholds. For example, an agent may execute routine actions automatically while blocking financial transfers or system changes above a defined threshold until explicit approval is granted.

Beyond this, these platforms allow for dynamic risk scoring that adjusts based on context. The same action might proceed automatically during business hours, but trigger validation after midnight.

These tools maintain fixed audit trails for compliance reporting and provide role-based access controls that determine which agents get access to specific systems. When violations occur, platforms can quarantine the agent, roll back actions and alert security teams before the damage can spread.

Data and API Integration Hubs

Integration layers such as centralized API gateways or service meshes manage secure access to operational systems through authenticated APIs, schema validation and rate controls. Rather than granting direct database access, these hubs create controlled channels where every request passes through security checks and transformation rules.

They handle protocol translation between legacy Simple Object Access Protocol systems and modern Representational State Transfer APIs. Rate limiting prevents runaway agents from overwhelming services.

Circuit breakers automatically disconnect misbehaving agents before failures compound. These hubs capture not just what data was accessed but how it was transformed and where it was sent.

Prepare Your IT Ecosystem for an Autonomous Future

Agentic AI doesn’t fail or succeed on algorithms alone. It depends on whether your systems, controls and operating models can support automation action without creating new exposure.

Infrastructure has to handle continuous execution, security must account for autonomous behavior, governance needs to live in code and operations must spot risk before it spreads. Together, these responsibilities define the role of IT in agentic AI as systems move from insight generation into real execution.

That’s where the right IT partner matters. Morefield helps you design, integrate and govern the systems that agentic AI relies on, from secure infrastructure and resilient integrations to compliance-ready monitoring and operational controls. Contact us to talk about building an environment where autonomy stays controlled and aligned with how your business operates.

Smartphones are no longer just communication tools—they are an extension of today’s employee. The same device that handles customer emails, approvals, and collaboration during the workday is often used after hours for personal banking, family communication, and everyday life. This overlap is now the norm across small businesses and mid-market organizations alike. 

As mobile devices become central to how work gets done, leaders must decide not just how smartphones are managed, but who should own them. In the article ahead, we have included a decision matrix that is designed to help businesses evaluate company-issued smartphones versus employee-owned devices in a clear, practical way—regardless of company size or current technology maturity—so mobile strategy aligns with productivity, risk tolerance, and organizational culture.

Company-Issued Smartphones: Control, Consistency, & Clarity

In a company-owned model, the organization purchases and issues smartphones to employees for work purposes. For larger organizations with 100+ devices an MDM platform may be adopted to help manage those devices.

Company-owned devices give the organization full authority to enforce security policies.  Employees generally accept stronger controls on a device they do not personally own. This clarity reduces ambiguity around monitoring, restrictions, and acceptable use.  With company-issued phones, IT can deploy consistent configurations, apps, and security settings across the organization.  

When an employee leaves, the organization can immediately lock or wipe the device, reclaim the asset, and reissue it. There is no dependency on employee cooperation.  For leadership, this reduces legal, security, and operational risk.

BYoD (Bring Your own Device): Flexibility with Guardrails

In a BYoD model, employees use their personal smartphones for work. The organization avoids purchasing and maintaining smartphones. This can be attractive for cost-conscious businesses or roles with light mobile usage.  Employees use devices they already know and prefer. This often improves adoption and satisfaction.  There is no procurement delay. New users can be enabled quickly with app-based enrollment.

Even with modern tools, organizations have limited visibility and authority over personal devices.  Employees may be uncomfortable with corporate management software on their personal phones—especially if policies are unclear.  When an employee leaves, the company must rely on app-level wipes or containerization rather than full device control. 

This is effective but not foolproof if policies are weak or inconsistently applied.  In regulated industries, or with Cybersecurity underwriters BYoD may not meet audit or data protection requirements without additional safeguards.

A Hybrid Approach: Common in the Real World

Many organizations land on a hybrid model.  An approach that balances cost, security, and employee experience—but requires clear segmentation and governance.  An example of a Hybrid model might include:

• Company-issued smartphones for executives, sales, and high-risk roles
• BYoD with app-level controls for lower-risk users

Mobile Device Strategy Decision Matrix

This decision matrix is designed for your leadership team when evaluating company-issued smartphones versus employee managed or BYOD (Bring Your Own Device).  Use it to align security, cost, compliance, and employee experience priorities before selecting tools or policies.

Executive-Level Comparison

Strategic Fit by Business Priority

Leadership Decision Checklist

Before selecting a model, executive teams should be able to answer yes to the questions below.  If any of these are unclear, a company-issued approach will typically reduce ambiguity and risk.

• We understand what data is accessed on mobile devices
• We know our compliance and insurance obligations
• We have defined acceptable use and privacy expectations
• We can clearly communicate policies to employees
• We have ownership alignment between IT, HR, and leadership

Will I need Mobile Device Management (MDM) for Company issued Phones?

Company-issued smartphones and Bring Your Own Device (BYOD) programs introduce very different risks, costs, and management realities.  Once your organization has decided on who will own the device.  You will want to consider whether Mobile Device Management aligns strategically with your decision.

In most cases, MDM best aligns with those organizations who elect to provide company issued devices.  MDM will help with policies, platforms that can prevent policy missteps, employee frustration, and security gaps.  Check out Adopting MDM for Mobile Smartphones to learn more about a smart technology evaluation for Mobile Device Management (MDM).

Making the best choice for your Business

Choosing between company-issued smartphones and employee-owned devices is not a technology decision alone—it’s a business decision that touches productivity, risk, culture, and growth. The right answer looks different for every organization, and it often evolves as the business scales. 

Morefield’s role is to help leaders step back, assess how mobile devices are used in their environment, and evaluate options through a practical, business-first lens. If you’re weighing your mobile strategy or want an objective perspective on what model best supports your Central Pennsylvania business goals, we invite you to start a conversation. 

A short, consultative discussion can bring clarity, reduce uncertainty, and help ensure your mobile approach supports where your business is headed—not just where it is today.

Smartphones have quietly become one of the most critical pieces of business infrastructure. They hold customer emails, executive conversations, access to cloud apps, and in many cases, sensitive company data. Yet in many organizations, mobile devices are still managed informally — or not managed at all.

If you are a leader considering Mobile Device Management (MDM) for company-issued smartphones, the decision can feel complex. MDM promises better security and control, but it also introduces cost, policy decisions, and potential friction with employees.

This article looks at MDM technology. Breaking down what MDM is, where it delivers value, where it can create challenges, and how to decide if it is right for your organization.

What Is Mobile Device Management (MDM)?

At its core, Mobile Device Management is usually a cloud-delivered approach to configure, secure, monitor, and support smartphones used for work. An MDM platform allows a company to manage devices remotely across operating systems like iOS and Android.

Typical MDM capabilities include:

• Enrolling new devices into a managed environment
• Enforcing security settings such as passcodes and encryption
• Controlling access to email, apps, and company data
• Deploying and updating business applications
• Locking or wiping devices that are lost, stolen, or retired
• Reporting on device compliance and usage

MDM is not just a security tool. It is an operational framework for managing mobile devices at scale.

Why Leaders Are Paying More Attention – The Business Benefits of Adopting MDM

The need for MDM has grown as the workplace has changed. Today your employees work from anywhere. Phones are used for authentication, approvals, collaboration, and customer engagement. At the same time, cyber threats have increased, and regulators and cyber insurance underwriters expect stronger data protection controls.

For many, the question is no longer whether mobile devices represent risk — but whether that risk is being actively managed.

Stronger Security and Risk Reduction

Smartphones are frequently lost, stolen, or replaced. Without MDM, each lost phone can become a potential data breach.  MDM reduces risk by enabling:

• Mandatory passcodes and biometric security
• Device-level encryption
• The ability to remotely lock or wipe a device
• Restrictions on unapproved apps or cloud backups

From a leadership perspective, this translates into lower breach risk, better audit readiness, and fewer late-night crisis calls.

Better Control Over Corporate Data

Company-issued phones often blur the line between personal and professional use. MDM allows organizations to define clear boundaries.  This level of control is especially valuable for regulated industries or companies handling customer data, financial records, or intellectual property.  You can:

• Limit which apps can access corporate email and files
• Prevent data from being copied into personal apps
• Ensure company data is removed when an employee leaves

Simplified Onboarding | Offboarding

Without MDM, setting up a new phone can be time-consuming and inconsistent. With MDM, devices can be preconfigured before they ever reach the user.  For growing organizations, this operational efficiency quickly adds up.  Benefits include:

• Faster employee onboarding
• Consistent device configurations
• Reduced reliance on manual IT setup
• Cleaner, faster offboarding when roles change

Improved Visibility and Accountability

MDM provides insight into what devices exist, who is using them, and whether they meet security standards.  Instead of guessing, leadership gains data-backed clarity.  This visibility helps leaders understand true mobile costs, identify unused or non-compliant devices and make informed decisions about refresh cycles.

Support for Compliance and Governance

Many compliance frameworks and cyber insurance providers expect controls around mobile access to corporate systems. MDM helps demonstrate due diligence by enforcing consistent security policies and generating compliance reports.

For executives, this supports stronger governance without relying on informal processes.

The Potential Downsides of MDM to Consider

While MDM delivers real value, it is not without trade-offs. Understanding the challenges upfront leads to better outcomes.

Cost and Resource Investment

MDM is not just a software license. It includes platform licensing fees, project for initial configuration followed by ongoing management activities and end user support.  For smaller organizations (less than 100 company issued smartphones), these costs may feel significant if mobile risk is currently low. Leaders must weigh cost against risk exposure and growth plans.

User Experience and Adoption Concerns

Employees can be sensitive about how much control the company has over their device — even when the device is company-issued.  Common concerns include a fear of personal monitoring, restrictions that feel overly limiting and frustration with blocked apps or settings.  Clear communication is essential. MDM policies should be designed to protect the business without unnecessarily disrupting employee productivity.

Policy Complexity

MDM forces organizations to make decisions and adopt policy.  Topics that they may have avoided:

• Which apps are allowed?
• What data is considered sensitive?
• How strict should security controls be?

A poorly designed policy can create friction, confusion and slow down work. Whereas strong policies balance security with practicality.  Work with your vendor or a consultative partner like Morefield who can provide guidance on best practices around policy adoption.  

Platform and Device Limitations

Different operating systems support different controls. Apple and Android take different approaches to privacy, updates, and device restrictions.  This means leaders should expect some variation in policy enforcement by device type and ongoing adjustments within the MDM as operating systems evolve.  MDM is not a “set it and forget it.” It requires governance.

Company-Owned vs. BYOD: A Critical Distinction

This article focuses on company-issued smartphones, which are generally easier to manage. With company-owned devices, organizations have more authority to enforce controls.

However, some businesses operate mixed environments with both company-owned and employee-owned devices. In those cases, leaders may consider lighter approaches such as Mobile Application Management (MAM), which focuses on securing apps rather than the entire device.

Clarifying device ownership strategy is a prerequisite to successful MDM adoption.  Read more about the decision of Company-owned vs Employee BYoD under Morefield’s resources page.

Questions to Ask Before Adopting and When MDM Makes the Most Sense

MDM is most successful when it supports clear business objectives, not just technical controls.  Before moving forward, leadership teams should align on a few strategic questions:

• What data is accessed on company smartphones today?
• What would the business impact be if that data were exposed?
• How many devices are currently deployed, and how fast is that number growing?
• Do we have the internal resources to manage MDM effectively?
• How will policies be communicated to employees?

MDM tends to deliver the strongest ROI when

• Smartphones are mission-critical to daily operations
• The organization is growing or geographically distributed
• Security, compliance, or customer trust is a priority
• Leadership wants predictable, scalable device management

In these environments, MDM becomes an enabler of productivity and resilience — not just a security expense or burden to employees.

Whether your organization adopts MDM. It’s a Business Decision, Not Just an IT One

Adopting Mobile Device Management is ultimately a business decision. It touches security, employee experience, operational efficiency, and risk management.

For organizations, the question is not whether MDM is perfect — but whether your current approach to managing mobile devices is sustainable.

When implemented thoughtfully, MDM replaces uncertainty with visibility, and risk with control. Like any strategic investment, success depends on clear goals, smart policies, and ongoing alignment between IT and the business.

If your Central Pennsylvania organization is considering an adoption of MDM and you would like an independent partner to help evaluate this technology decision, contact Morefield.  We are proud to support our Central Pennsylvania neighbors, enabling their success through smart technology decisions. 

Every business is carrying some level of technical debt—the accumulated impact of aging hardware, outdated software, unsupported systems, and quick fixes that were never fully resolved. A little technical debt is normal. But too much and it becomes a silent tax on your productivity, your security, and your ability to innovate.

As the end of year approaches, Morefield sales activity has shown an unmistakable pattern: organizations are electing to confront years of deferred maintenance. From Windows 11 migrations to network refreshes, server replacements to SIP trunk conversions—modernization has become a predominant buying behavior across SMB | Mid-Market clients.

This isn’t a coincidence. It’s a reflection of a broader truth:

Companies who treat IT modernization as strategic investments will outperform those who treat it as a cost burden to avoid.

And the Central Pennsylvania market is validating this shift.

What it Means: Technical Debt Is No Longer Invisible

These recurring themes across the pipeline at End of Year aren’t random. They’re symptoms of an underlying challenge:

Organizations are hitting a wall because older systems will no longer support operational needs.

When Morefield talks to our client business leaders, directors, administrators the same themes surfaces repeatedly:

• Downtime risk is too high
• Replacement parts are too hard to find
• Vendors are ending support
• Performance bottlenecks are slowing down teams
• Security exposure is becoming unacceptable
• Strategic initiatives are stalled

Technical debt doesn’t show up on the CFO’s balance sheet, but it absolutely shows up in:

• Lost productivity
• Higher support costs
• Increased cybersecurity risk
• Lower employee satisfaction
• Inability to adopt modern tools

When you work with what you have every day, it is difficult to realize how much an old platform will hold you back until you begin to replace those systems.

Why Deferred Modernization Becomes More Expensive Over Time

It’s tempting to squeeze another year out of aging servers or postpone a switch refresh. But the longer upgrades are delayed, the more expensive they become—both directly and indirectly.

Support Costs Increase. Most older systems will require T&M work, premium warranty renewals, or manual remediation because vendor support has lapsed.

Security Vulnerabilities Multiply.  Outdated operating systems, unpatched firmware, and unsupported applications become prime targets for Cyber adversaries. Insurance carriers are now asking detailed questions that many legacy environments can’t satisfy.

Integration Breaks.  Modern tools—especially cloud and AI-driven platforms—expect modern APIs, secure protocols, and updated infrastructure. Legacy equipment becomes an inhibitor.

Performance Bottlenecks Slow Teams Down.  Every minute waiting on a slow login, a frozen application, or a rebooting server is paid for somewhere—usually in lost productivity and your employee’s frustration.

Emergency Upgrades Cost Significantly More.  A planned server refresh is a budget item.  A failed server is a crisis.  When Morefield’s service desk is asked to respond to an emergency replacement—more often those disrupting incidents would have been avoided with proactive modernization.

A Simple Philosophy: “Take care of the legacy today so you’re ready for tomorrow.”

Businesses that modernize now will have a clean runway for:

• AI integration
• Automation projects
• Zero-trust security
• Hybrid work optimization
• Scalability
• Cost predictability

Businesses that continue deferring upgrades will face increasing friction, higher risk, and a widening competitive gap.  Adapted from our many client discussions Morefield has compiled a checklist to provide a practical way to identify systems that need your attention now, so the business is ready for what comes next.

Use this list to simplify planning, prioritize investments, and create a roadmap that reduces risk while strengthening resilience.

The End of Year Technical Debt Checklist

What Every SMB | Mid-Market Organization Must Modernize in the year ahead to Stay Secure, Productive, and AI-Ready

SECTION 1: CORE INFRASTRUCTURE READINESS

Servers & Storage

☐ Are any servers more than 5 years old?

☐ Are you running unsupported versions of Windows Server or Hyper-V?

☐ Do you have a plan for server warranty expirations in the next 12–18 months?

☐ Is your SAN or storage array nearing capacity or end-of-life?

☐ Do you have redundancy to protect against downtime?

Endpoints & Operating Systems

☐ Are there still Windows 10 devices in your environment?

☐ Do you have a plan for Microsoft’s 2025 end-of-support deadline?

☐ Are older PCs slowing down productivity or failing under modern workloads?

☐ Are you using device management (Intune | Entra) for security and updates?

Cloud Strategy & Migration

☐ Are critical apps still tied to legacy on-prem hardware?

☐ Is your network prepared for cloud-first workloads?

☐ Are you using scalable storage, compute, and automation where appropriate?

Why it matters:  Aging infrastructure is a top contributor to downtime, failed backups, and cybersecurity gaps. In our engagements, server refreshes, SAN expansions, and warranty renewals appear more than any other hardware topic.  We’ve seen dozens of Windows 11 upgrade requests this quarter alone.  Unsupported devices become security liabilities—and block adoption of modern AI tools like Copilot.  There is strong demand for a shift toward hybrid and cloud-first architectures migrating premise workloads to the cloud, enhancing disaster recovery and backup solutions.

SECTION 2: CYBERSECURITY POSTURE

Threat Detection, Zero Trust Network Access (ZTNA) & Identity

☐ Is multi-factor authentication (MFA) deployed everywhere?

☐ Are privileged accounts tightly controlled and monitored?

☐ Are you using Entra ID and conditional access policies?

☐ Do you have 24/7 threat monitoring?

☐ Do you have MDR or XDR in place?

☐ Are you able to detect and respond to breaches in minutes—not days?

Firewalls & Perimeter

☐ Are firewalls current, supported, and properly configured?

☐ Do you have IPS, content filtering, and zero-trust controls enabled?

☐ Are remote users protected with secure access?

Security Assessment Services & Confirmation of Compliance

☐ Do you conduct regular internal/external vulnerability scans?

☐ Have you completed a cybersecurity assessment in the last 12 months?

☐ Are you preparing for NIST, CMMC, HIPAA, PCI, or insurance compliance?

Why it matters:  Morefield clients are requesting vulnerability assessments, MDR deployments, and Microsoft security alignments.  Identity is a top attack surface and as the focus, Central Pennsylvania Businesses are shifting from basic tools to fully managed detection & response.  We’re seeing growing demand for Meraki firewall projects, Barracuda licensing, and network segmentation.  The requests for penetration tests, vulnerability assessments, and cyber reviews tell us Central Pennsylvania business leaders want a clear picture of their risk.

SECTION 3: COMMUNICATION & COLLABORATION SYSTEMS

Phone System | UCaaS

☐ Is your phone system still on-premises?

☐ Are you experiencing outages, feature gaps, or support issues?

☐ Are you considering cloud voice (RingCentral, Teams, etc.)?

☐ Are paging and collaboration tools integrated into your ecosystem?

Microsoft 365 & Collaboration

☐ Is your organization using Teams voice, shared workspaces, or cloud storage?

☐ Is your Microsoft tenant aligned with best practices for security and governance?

☐ Do you have a roadmap for Copilot or AI-driven productivity tools?

Why it matters:  Cloud communications remain one of the fastest-moving modernization priorities.  Morefield is engaged in multiple UCaaS migrations, SIP Trunking conversions and integrations of 3rd party platforms.  Collaboration systems require modern identity, licensing, and security foundations—areas where many organizations fall behind due to technical debt.

SECTION 4: PHYSICAL SECURITY SYSTEMS

Access Control & Cameras

☐ Are your cameras cloud-managed and remotely accessible?

☐ Is your DVR or NVR past end-of-life?

☐ Do you have visibility across all locations?

☐ Is your access control system outdated or badge-based without MFA integration?

Why it matters:  This year we have seen a greater volume of Verkada, Hanwha, access control installations, and perimeter security upgrades all that indicate physical security modernization is accelerating as part of a unified security strategy.

SECTION 5: AI & AUTOMATION READINESS

AI Foundations Checklist

☐ Are your endpoints modern enough to support AI-enhanced workflows?

☐ Is your data environment organized and protected for use in automation?

☐ Do you have modern identity systems (Entra ID)?

☐ Are you using cloud collaboration tools that integrate with AI?

☐ Are systems patched, supported, and compliant?

Why it matters:  Here’s the truth our clients are starting to appreciate:  You cannot embrace AI if technical debt is holding your environment together by duct tape.  Modernization is the first step toward AI maturity.

Technical debt has a way of compounding quietly.

But modernization unlocks:

• Better security
• Higher productivity
• Lower operating costs
• Stronger reliability
• The foundation required for AI and automation

This article and checklist will help you understand where you stand today—so you can plan with confidence for tomorrow.

If you’d like a guided review of your environment, or help building a modernization roadmap tailored to your business, contact Morefield.  Our team is ready to support you.