TechnologyMatch answers the top questions IT leaders have

TL;DR

• Align AI projects with business goals, set realistic expectations, and maintain transparency.
• Build secure, quality data infrastructure—break down silos and ensure compliance.
• Prioritize cybersecurity to support safe AI and business continuity.
• Upskill employees continuously and foster a culture of learning and adaptability.
• Use MSPs for efficiency, but monitor performance and focus on building robust internal teams long-term.

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Based on our survey, about 25% of IT leaders are planning to invest most of their resources into AI in the next 12 months, over 50% of whom are final decision makers or part of the decision-making team. About 15% of other IT leaders want to invest heavily in upgrading their devices to support emerging technologies like AI.

Everything happening with AI, and it being in the nascent stages, brings forward certain difficult questions that IT leaders have trouble finding answers to. Constant pressure from the C-suite to prove value and a visible lack of ROI from AI puts a lot of pressure that’s unjustified. What’s more, budget constraints, lack of skills, and an ill-prepared tech stack put even more pressure on an already burdened department.

We identified the top 5 questions that IT leaders have today about emerging trends and technologies, and answered them backed by data and proven insights from industry pioneers.

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How can you translate AI pilot projects into production-ready solutions that demonstrate realistic ROI while ensuring transparency?

While AI's rise has been prominent, IT leaders have remained understandably cautious in their experimentation. According to a survey by BMC, although 94% of respondents say AI is part of their IT strategy, only 17% have progressed beyond the experimental phase, and a mere 5% have achieved mature implementation with tangible results.

Most IT leaders aren’t being able to communicate the value of AI beyond the time-saving aspect, which doesn’t mean much to CFO or even CEOs, because they’re more worried about ROI in terms of profitability. This is another issue — the C-suite is looking at IT managers to recenter the organizational compass with AI initiatives while they have their plates overflowing with budget constraints, evolving leadership landscapes, incapable legacy systems, and a shortage of skills.

IT leaders are not being able to make the C-suite understand that recognizing any direct impact of AI will take time. It’s not something that happens overnight; AI is more nascent than we think, and rushing it will not make it any better.

The true challenge here isn’t AI implementation itself but a strong misalignment between unrealistic expectations from the C-suite and the inability of IT leaders to communicate realistic expectations.

Work with the entire organization to eliminate silos and ensure everyone is on the same page about AI initiatives and outcomes

Set realistic expectations with AI

• Talk to your internal team and understand realistically what AI outcomes are possible.
• Conduct risk assessment and potential downsides to business continuity with AI.
• Understand the restrictions of current systems and friction points in terms of AI implementation.
• Create a realistic outline of AI initiatives and realistic outcomes for a specific timeline.
• Communicate risks, challenges, limitations, and realistic outcomes to the C-suite.

Create a transparent AI strategy

• Work with internal departments to understand their priorities and how realistically can AI help.
• Do the groundwork of things necessary to prepare for AI implementation.
• An in-depth review and research of the value and outcome that can derived with each AI initiative.
• Create realistic goals in alignment with implementation timelines.
• Ensure transparency throughout the process, especially with the C-suite and decision makers.

‍Work with stakeholders to achieve projected outcomes

• Start work on executing strategic initiatives based on defined timelines.
• Have scheduled meetings to discuss progress, difficulties, roadblocks, and projections.
• Work with tech partners and external stakeholders to help execute AI plans and align them with your vision.
• Work with internal stakeholders: CFO for finances and budget, CIO for aligning tech with other departments, CISO for security & compliance, CDAO for data, CTO for tech production, CHRO for employee adaptability, and CEO for overall adoption and change management.
• Ensure goals are met, prepare for advancements for next phase, and discuss challenges if goals aren’t met.

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How can I build and maintain a data infrastructure that supports both secure implementation and business outcomes?

70% of GenAI pilots fail to move into production due to inadequate data, lack of governance, high risk, or inability to justify the business value (source).

Most organizations today can only effectively utilize about 20% of their data through traditional analytics. The remaining 80% sits locked away in siloed systems, legacy applications, and unstructured formats—essentially invisible to decision-makers but filled with potential business intelligence.

This "dark data" problem has become more urgent as AI adoption accelerates. While many executives push for AI implementation, the reality is that AI systems are only as good as the data they can access—and most enterprise data remains inaccessible without specialized solutions.

The security implications are equally significant. Traditional approaches to AI often require sending data to external environments, creating what Gartner calls the "AI security paradox"—organizations must expose their data to extract its value, potentially creating new vectors for data leakage or compliance violations.

Today’s data infrastructures must serve multiple purposes simultaneously

• Technical requirements: Ensuring data is available, secure, and performant.
• Business needs: Making data accessible, meaningful, and actionable in structured formats.
• Governance demands: Maintaining compliance and certification standards to enable reliable Gen AI pilots into production.
• Data quality: Ensuring data is of high quality to enable lower AI hallucinations and high-quality output.

IT leaders are facing the “low quality, high cost” problem with AI, which makes reliability and, therefore, productivity, a grave concern. An ineffective data strategy forces AI to become a liability sooner rather than later, damaging brand image and forcing negative ROI. There’s no AI without data, and figuring out a good data strategy is crucial before scaling AI initiatives.

Create a strong data strategy to enable the implementation of clean, secure, and compliant AI solutions

Understand data silos and fix them

• Identify “dark data” hidden in unstructured and unusable formats.
• Work with other departments to identify critical data necessary to enable better AI answers.
• Separately identify sensitive data and ensure secure channels for better implementation.
• Establish data quality standards and enrich all data to meet those standards.

Create a clean data pipeline

• Convert all relevant data into business-ready documents or other formats.
• Educate your organization on the important of creating usable data catalog and including that as a part of their day-to-day.
• Make the data highly available for a continuous pipeline and schedule reviews for quality assurance.
• Extensively train AI on these data formats while ensuring security & compliance benchmarks.

Work with teams to ensure high AI reliability

• Extensive QA tests with AI solutions to ensure reliability of the data.
• Enable all departments to use AI in their work and report back discrepancies.
• Define timelines for this phase of the project and communicate updates to all internal stakeholders.
• Reiterate and improve the data strategy to minimize hallucinations and increase reliability.

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How to implement a cybersecurity plan that enables better AI implementation and protects data from outages & breaches?

The past decade has seen some of the worst ransomware attacks and security breaches, strengthening the need for better security protocols. The unpredictability of these breaches weighs heavy on IT leaders, and the best they can do is invest in better tools and train company staff in safety measures. The need for better cybersecurity becomes even more important with AI because any external entity can have access to all company data if breached.

Some of the biggest challenges with cybersecurity are dealing with end-of-support legacy systems, staff ignorance or unawareness, and shadow IT. According to Gartner’s report, 75% of the employees will acquire, modify, or create technology outside of IT’s visibility—up 41% from 2022. This poses a larger threat to IT leaders than almost anything else, especially when you scale AI implementation, potentially exposing sensitive information almost entirely.

IT managers seem to be aware of the need for a better security infrastructure—based on our survey, cybersecurity turned out to be the third most important investment this year after AI and devices, with about 12% of them saying it’s a strong priority.

Think of cybersecurity to account for important factors that can protect data and provide immediate backup restoration

• Threat and risk assessment: Account for vulnerabilities in your current IT infrastructure, potential threats, and business continuity risks.
• Data protection: Deploy strong infrastructures with modern principles like Zero Trust to improve access control and minimize the risk of data loss.
• Staff awareness: Conduct employee awareness programs to educate staff on potential risks of shadow IT and how they can help avoid security breaches.
• Disaster recovery: Create strong infrastructures to support multi-region failover and strong data backups for better business continuity and minimal RTO/RPO.

Identify risks and threat to data

• Assess the current situation of your organization and uncover potential vulnerabilities.
• Locate sensitive data and understand the impact of it being compromised.
• Identify roadblocks and challenges such as legacy systems, shadow IT, etc.
• Communicate the importance of better cybersecurity to the C-suite and potential losses.

Minimize exposure to external entities

• Explore what are the vulnerabilities of the organization in terms of external exposure.
• Minimize the use of third-party tools and also assess the need for external vendors.
• If possible, move operations in-house, especially in terms of AI implementation.
• Prioritize internal solutions and discontinue relations with external partners when you have them.

Improve the cybersecurity infrastructure

• Build a strong cybersecurity team to help deploy stronger security measures faster.
• Move from legacy systems towards more hybrid or cloud solutions that are easier to scale and maintain.
• Deploy progressive measures like access management, microsegmentation, and data loss prevention to isolate attacks and protect data.
• Deploy disaster recovery and incident response strategies to minimize downtime and improve recovery.

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How can I upskill employees and provide training on emerging technologies for better expertise and implementation?

The struggle to derive significant value from AI stems not just from its unpredictability but also from various ineptitudes that IT teams face, often due to flawed innovation strategies. In the next couple of years (as 2026 ends), over 90% of organizations are predicted to experience an IT skills crisis, with not enough talent to fill essential positions. This begs the question: Is an overworked and underprepared department ready for AI implementations?

About 81% of CIOs cite AI skills gaps as having a moderate to severe negative impact on their ability to meet 2025 objectives. As an IT manager, a critical part of enabling better AI implementation is to build a good team, either by hiring more experienced engineers or upskilling your employees to be prepared. But hiring, again, is a temporary solution as upskilling is the only way to keep your employees updated on modern technology.

Another option is to outsource technology and only focus on the business outcomes, but that comes with a cost. The cost of vulnerabilities, negligence, and accountability. Hiring MSPs or working with external vendors might be a good idea temporarily, but your ultimate goal should be to bring operations in-house as quickly as possible.

You also need to enable better HR operations to improve hiring, onboarding, and employee relations with the company. Although this might not be entirely your domain, but enabling the right technologies certainly is. AI being an important part of it.

Identify skill gaps and talent requirements

• Identify the skills you need to enable implementation of modern technologies like AI.
• Assess the current situation of your workforce and how much of a gap needs to be filled to meet business goals.
• Conduct a talent assessment to understand what new talent you need to on the team.
• Create a outline and timeline for building a strong team by upskilling and hiring new talent.

Hiring strategy and workforce planning

• Speak to your team and understand what they think will help them upskill and in what field.
• Create a transparent strategy for your workforce with a detailed timeline for upskilling, acquiring certifications, and applying those skills.
• Work with HR to improve the hiring pipeline. Personally be involved with shortlisting and interviewing potential new hires.
• Work with sourcing partners or referrals to acquire the best possible talent in the market according to your budget.

Build a pipeline of constant upskilling

• Upskilling isn’t a one time thing. Create a strategy that requires employees to upskill every 3 months as a part of their KPI.
• Conduct internal webinars or working sessions to discuss new technologies and what skills are needed to implement them.
• Delegate tasks based on skills and expertise. Give your team the freedom to implement & test new ideas.
• Leverage incentive-based upskilling programs to encourage your staff.

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How can I work with external vendors and outsourced partners to ensure I meet business outcomes?

There are certain advantages of outsourcing IT early on and hiring an MSP or an outsourced partner to handle certain parts of your infrastructure:

• They’re cheaper and faster than hiring full-time employees, training, and upskilling them.
• Direct accountability through defined SLAs, KPIs, and deliverables.
• Independent of the company, so there’s no need for direct management.
• Better expertise and compliance certifications, ideal for quicker implementation and scale.
• Modern tech stack.
• Specific implementations in remote locations where it’s expensive or doesn’t make sense to have hired personnel.

That being said, an MSP can never replace your internal team. You’re another client for them, and their priorities are shared. Which means they will not always have your company’s best interest at heart and only deliver what’s expected of them. Therefore, having relations with external partners is a good idea, but it doesn’t trump building an internal team in the long run.

How to work with MSPs and external partners

Deciding to look for an MSP and initial steps

• Define primary reasons why you would want an MSP.
• Do you have multiple locations, do you need setting up infra, do you not have budget for internal implementation, is there skill gap or lack of expertise in your internal team, do you want to delegate certain task outside, etc.?
• Prioritize your use cases based on criticality i.e. building a strong infrastructure is more critical than customer support.
• Start looking for affordable and effective MSPs. Prioritize referrals and social proof.

How to hire an MSP + vetting process

• Connect with a partner at TechnologyMatch and explain your needs.
• Clarify what your priorities, SLAs, and service requirements are.
• Get connected to the right technology partner who can help you with your needs.
• Ask important questions about accountability, deliverables, turnaround time, expertise, certifications, incident response, etc.

When to move on from an MSP

• Constantly review contract SLAs and agreements.
• Understand support parameters like ticket resolutions, response rate, resolution rate, and end user feedback.
• How are they handling critical services and are their services impactful on the business?
• If they fail to meet required standards, evaluate other MSPs or tech partners.

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FAQ

1. How can you translate AI pilot projects into production-ready solutions that demonstrate realistic ROI while ensuring transparency?

Move beyond time-saving claims and set realistic, business-aligned expectations with the C-suite. Work cross-functionally, conduct risk and system assessments, outline achievable AI goals, and maintain transparent communication about challenges and timelines. Focus on measurable outcomes and ensure organization-wide understanding.

2. How can I build and maintain a data infrastructure that supports both secure implementation and business outcomes?

Develop a strong data strategy: break down silos, surface “dark data,” improve data quality, and ensure security and compliance. Build clean, accessible, business-ready data pipelines and regularly review data health. Train AI models on vetted data and refine your approach based on feedback and QA.

3. How to implement a cybersecurity plan that enables better AI implementation and protects data from outages & breaches?

Assess vulnerabilities, minimize external exposure, and move toward internal, secure solutions. Invest in Zero Trust, microsegmentation, and disaster recovery. Educate staff to reduce shadow IT, update legacy systems, and maintain a skilled security team. Communicate the importance of cybersecurity to leadership.

4. How can I upskill employees and provide training on emerging technologies for better expertise and implementation?

Identify current skills gaps, create a roadmap for hiring and upskilling, and work with HR for continual talent acquisition. Make upskilling ongoing (e.g., quarterly), use internal webinars, delegate tasks for hands-on learning, and offer incentives. Focus on building a self-sustaining, adaptable team.

5. How can I work with external vendors and outsourced partners to ensure I meet business outcomes?

Clearly define your needs and priorities before engaging MSPs. Vet partners for expertise, accountability, and fit. Set SLAs, review performance regularly, and know when to switch if standards aren’t met. Remember, MSPs are a complement, not a replacement, for strong internal teams.