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Gartner predicts agentic AI will facilitate 15% of daily autonomous decisions by 2028. Vendors and analysts alike tout how the autonomous AI will transform workplaces by automating tasks, optimizing workflows and supporting more intelligent decision-making.
But before businesses invest in these systems, they have to build scalable and secure digital infrastructure. One element is zero trust architecture, which helps secure AI systems with granular access controls, enhanced visibility and continuous security monitoring. This approach significantly reduces the threat surface that attackers can exploit. Other areas organizations should get in place include:
- Cloud-native architectures for scalability and flexibility.
- AI governance frameworks to ensure responsible AI use.
- Real-time data processing pipelines to manage exponential data growth.
The future of work relies not just on AI itself, but on setting up this infrastructure for AI to operate effectively. This also includes seamless integration with existing systems, predictive analytics capabilities and the ability to adapt to dynamic environments. By focusing on all of these critical components, organizations can create a robust digital foundation to support agentic AI.
Scalability, Flexibility, Security: The Design Principles of Modern IT Architecture
Automation will play a key role in creating a scalable IT architecture, IBM Automation's Bill Lobig told Reworked. Without it, companies won't be able to operate and scale at the necessary pace, he said.
Automation tools like resource management and observability can proactively provide full visibility into the entire tech stack to mitigate outages, troubleshoot issues and balance the costs and resources that come with these transformations.
AI initiatives can quickly become unwieldy with cost and/or mismanaged resources. "AI agents have immense power to orchestrate IT actions, but without AI-powered automation, businesses will experience roadblocks as they try to scale,” he said.
In practice, this means scalability, flexibility and security are no longer optional — they are fundamental design principles and practical needs, Mithilesh Ramaswamy, a senior engineer at Microsoft working with AI tools, said. “Modularity is your mantra. Build in microservices, APIs and automation. This 'Lego-brick' approach lets you scale fast and adapt to whatever the future throws at you," he said.
A modular, API-driven design philosophy creates IT architectures that are both scalable and adaptable, so they can evolve alongside emerging technologies, Ramaswamy added. This symbiosis between cloud computing and AI is the bedrock of modern digital infrastructure. According to Ramaswamy, "Cloud is the fuel, AI is the engine. Cloud delivers the scale and reach; AI injects the intelligence."
Cloud environments provide the necessary computational power and accessibility, while AI algorithms drive automation, personalization and real-time decision-making, fundamentally transforming workplace efficiency, he continued.
However, the introduction of generative AI into the workplace aggravated a problem that's existed since the first enterprise content management systems.
The continuation of remote and hybrid work models makes balancing accessibility with airtight security even more important, Ramaswamy said. The only solution is a zero-trust security framework. "We need to build new systems with Zero Trust as the foundation. Layered security, strong identity and smart remote access — accessibility without compromising airtight security," he said.
When security is embedded at every level — identity management, endpoint security, network controls — organizations can mitigate threats while enabling seamless remote collaboration. However, Ramaswamy highlights two major roadblocks: legacy baggage and the talent gap.
Outdated systems often slow down innovation, and the demand for skilled engineers proficient in AI, cloud and security far exceeds supply, he said. Businesses must prioritize upskilling, reskilling and strategic hiring to bridge this gap.
The Challenges of Upgrading Digital Infrastructure
Dato co-founder and CEO Eli Goodman agreed, saying that digital infrastructure upgrades are never straightforward. For larger organizations, the biggest challenge is often resistance to change. Employees accustomed to legacy systems may push back, making communication, training and demonstrating real-world benefits crucial for smooth transitions.
Smaller companies face budget constraints, making it difficult to modernize everything at once. Legacy system integration also presents a common hurdle, as new technologies must be carefully incorporated to avoid inefficiencies, Goodman continued.
Despite these challenges, emerging AI technologies are transforming workplace efficiency, particularly in how companies manage knowledge transfer. Goodman highlighted the benefits of AI-powered meeting summary tools like Chorus AI and Read AI, which automatically capture key takeaways and make information easily accessible for employees.
New employees benefit from these shifts in particular, as they can then absorb knowledge at their own pace, whether through transcripts, video recordings or AI-curated summaries tailored to their learning preferences.
However, with these advancements comes new imperatives, Goodman added. AI makes it easy to capture and share information, but without proper governance, sensitive data can end up in the wrong hands. Companies must be intentional and provide an infrastructure that offers access controls and ensures automation serves its purpose without exposing critical conversations.
For Goodman, the message is clear: building a future-ready digital workplace requires a balance of innovation, security and adaptability. Organizations that embrace AI-powered infrastructure thoughtfully will gain an edge, not just in efficiency, but in resilience.
Where Do Legacy Systems Fit in Transformation?
The issue of legacy systems is complex. While they're often viewed as the source of security risks, they also bring important additions to any updated architecture, Jens Koch, head of product management at Retarus, added.
“While it's true that reliance on older systems can pose cybersecurity risks, such as outdated protocols or unsupported software, they also embody years of fine-tuned workflows, compliance adherence, and business-critical operations. The challenge lies not in dismissing legacy systems as outdated but in evaluating their contribution to a larger IT strategy," Koch said.
Start by understanding what these systems offer and where the dependencies lie. Each system needs to be treated as an individual use case, rather than making sweeping measures or strategies.
“A thorough audit can uncover which legacy systems are essential to operations and which can be replaced with technology that better addresses a company's pain points,” he said.
Hybrid approaches can bridge the gap, allowing enterprises to retain the reliability of existing systems while layering modern, secure functionalities on top.
Shifting away from reliance on legacy systems is less about replacement and more about transformation. By approaching modernization as a strategic opportunity, not an obligation, organizations can future-proof their operations while leveraging the reliability and expertise embedded in their legacy infrastructure, Koch argued.
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Read more about how to prepare your business for agentic AI:
- Rethink your HR Strategy to Include AI Agents — As AI agents make their way into our workforce, HR is called to adapt its workforce strategy.
- How Agentic AI Will Transform the Workplace: An Insider View — Employees of some of the major tech vendors in the agentic AI space discuss opportunities and challenges associated with the technology.
- The AI Agent Explosion: Unexpected Challenges Just Over the Horizon — The coming wave of AI agents raises both potential and challenges in the technical, business and human domains.