Artificial intelligence is no longer a futuristic concept — it’s here, transforming how businesses operate, compete, and grow. From automating IT workflows to enhancing cybersecurity and accelerating decision-making, AI is embedded across the enterprise.
But as promising as AI may be, adoption isn’t simple. Enterprises are realizing that moving too fast without a strategy can be just as risky as moving too slowly. The stakes are high: budgets, compliance, and customer trust are all on the line.
AI in the Enterprise: What’s Changing, What’s Next – A Guide for Business Leaders
Jump right to the solutions. Download our comprehensive guide to learn:
- The most important AI trends shaping enterprise IT today.
- Recommendations for responsible adoption and governance.
- Insights from leaders at UDT, BayMark Health Services, and TD SYNNEX.
Overcoming the 5 Biggest Barriers to Enterprise AI Success
At UDT’s recent AI in Action panel, leaders from UDT, BayMark Health Services, and TD SYNNEX shared candid insights into the roadblocks enterprises face. Now, we’ve captured those lessons in a resource designed for business leaders who want to act responsibly and strategically.
1. Navigating Governance Gaps
AI is advancing faster than policies can keep up. Without clear governance frameworks, enterprises risk exposing sensitive data, violating compliance requirements, or eroding stakeholder trust.
As Danny Rodriguez, Chief AI Officer and CTO at UDT, explained, “AI governance isn’t optional. It’s how you build trust with your customers and employees. Without that trust, the technology can’t achieve its potential.”
Yet, many organizations jump into adoption without extending their IT governance frameworks to cover AI. The result: innovation without oversight, which often leads to problems that outweigh the benefits.
2. Balancing Data Quality and Silos
AI systems are only as good as the data they process. Clean, unified data enables forecasting, anomaly detection, and evidence-based decisions. But many enterprises struggle with siloed, inconsistent, or ungoverned data.
Caridad Brioso, Data Architect at UDT, stressed the importance of getting the data right: “Without clean, organized data, AI just accelerates bad decisions. The technology itself is powerful, but if the inputs are flawed, the outcomes are flawed — only faster.”
For leaders, the challenge isn’t just investing in AI tools — it’s building the data foundation that makes those tools valuable.
3. Evaluating Security Risks
AI brings both promise and peril to cybersecurity. On one side, it enables faster detection and response. On the other, adversaries are using AI themselves, creating a new arms race.
Roy Escalona, AVP of IT at BayMark Health Services, cautioned against careless adoption: “If you’re feeding sensitive data into public AI, you’re essentially giving it away. That’s what keeps me up at night — not what AI can do for us, but what happens when we use it carelessly.”
Enterprises that don’t pair AI adoption with robust governance risk exposing their most valuable information to unintended audiences.
4. Determining Workforce Readiness
AI doesn’t replace jobs — it reshapes them. But enterprises often underestimate the cultural and educational shifts required for successful adoption. Employees need new skills in governance, data literacy, and even prompt engineering.
Michael Zealy, VMware Field Solutions Architect at TD SYNNEX, highlighted the human side of AI: “The organizations that win will be the ones that create safe spaces for AI learning. You can’t just dictate adoption — you need to let people explore, fail, and grow.”
Without training and psychological safety, employees may resist AI adoption or fail to realize its potential.
5. Calculating ROI
Many enterprises struggle to connect AI adoption to measurable business outcomes. Too often, leaders invest in tools without defining clear use cases or success metrics. The result: experiments that drain budgets without delivering value.
David Mabry, VP of Product Software Engineering at UDT, offered a cautionary reminder: “The winners won’t be the fastest adopters, rather they’ll be the ones who monetize AI responsibly. It’s not about racing ahead — it’s about scaling with discipline.”
The challenge isn’t whether AI can deliver ROI — it’s how to align AI initiatives with strategic business goals.
Why These Challenges Matter to Business Leaders
For executives, AI is no longer just a technology decision. It’s a business decision that affects:
- Expenses: Whether AI reduces costs through automation or creates waste through poorly planned adoption.
- Compliance: Whether data is safeguarded through governance or exposed through risky practices.
- Talent: Whether employees are empowered to innovate with AI or left behind by change.
- Scalability: Whether AI initiatives fuel growth or overwhelm IT capacity.
Ignoring these challenges isn’t an option. But neither is standing still. Business leaders must navigate a middle path, adopting AI responsibly while positioning their organizations to capture its long-term value.
From Risk to ROI: Building Trust in Enterprise AI
AI adoption isn’t easy. The challenges are real — from governance gaps and security risks to workforce readiness and ROI uncertainty. But with the right strategies, enterprises can overcome these obstacles and unlock AI’s true potential.
As Danny Rodriguez reminded the audience during the panel, “we view AI as a tool to enhance, not replace, the human experience. The real challenge is how to adopt it responsibly, in a way that balances governance with innovation.”
The organizations that thrive will be those that face these challenges head-on, invest in responsible frameworks, and build trust along the way.
Ready to take the next step? Take the next step: download the guide to see how partnering with an expert like UDT for your AI rollout can unlock new value for your organization. Ready to optimize your AI strategy? Schedule your personalized consultation today.
