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Secure SD-WAN
A hybrid wide-area network solution with built-in security for faster connectivity and better performance of cloud-enabled applications.
ARTICLE
The CIO’s Guide to AI in IT
Florin Soltan
Florin is a Cyber Security Product Manager at Acronym, where he leads the Cyber security portfolio and manages hardware resale technologies. His portfolio includes a comprehensive suite of managed cyber security services and solutions—ranging from threat detection and response to secure access and endpoint protection. Florin helps organizations protect against digital threats by developing and implementing robust security strategies that safeguard sensitive data and infrastructure while ensuring business continuity and regulatory compliance.
AI in IT is no longer a future consideration; it’s a present-day imperative. It’s time for Canadian CIOs to start planning, scaling, and taking action to ramp up for AI in IT. From infrastructure, to applications, to security, to the cloud, there is much to do in preparation for AI as the new reality impacts Canada’s business conditions.
A Canadian Survey, in the second quarter of 2025, reported 12.2% of businesses using AI to produce goods or deliver services over the last 12 months, up from 6.1% in the same period a year earlier.
Key Takeaways
- AI Has Shifted from Experiment to Infrastructure: AI is no longer a bolt-on capability or isolated pilot. It is becoming foundational to IT architecture, influencing cloud design, edge computing, automation, and application development. CIOs must redesign environments to support real-time, data-intensive, AI-driven operations rather than relying on legacy, centralized systems.
- Traditional IT Architectures Can’t Support Modern AI Demands: High-volume data streams, distributed environments, and intelligent automation require low-latency infrastructure, modern data pipelines, observability, and zero-trust security. Cloud-native and multi-cloud strategies are quickly becoming table stakes for scalable AI adoption.
- AI Maturity Determines Investment Priorities: Organizations typically move through four stages—Awareness, Operational, Systemic, and Transformational. Knowing your maturity level helps prioritize infrastructure upgrades, governance models, talent acquisition, and risk management strategies. Many Canadian businesses remain in early adoption stages, signaling both a readiness gap and a competitive opportunity.
- Data Quality, Governance, and Talent Are Make-or-Break Factors: AI success depends less on models and more on clean, observable data pipelines, structured workflows, human oversight, and defined IT roles. Over-automation without governance can erode trust and create operational blind spots. Sustainable AI requires structured change management and ongoing skills development.
- The Future Is AI-Native and Autonomous (AIOps): By 2026, many large enterprises are expected to adopt Artificial Intelligence for IT Operations (AIOps), shifting from reactive IT management to self-diagnosing, self-healing systems. Future-ready IT environments will be resilient, secure by design, distributed, and built on evolving architectures that treat AI as a core capability rather than a feature.
A Pivotal Moment
What happens when AI stops being a feature and becomes the backbone of IT? That pivotal moment has arrived. CIOs are now being asked to reimagine IT environments where AI, edge computing, and cloud resources are no longer optional, but foundational, for faster innovation cycles, smarter automation, and next-generation infrastructure.
No matter where you are on your AI in IT journey, this guide provides valuable insights and frameworks to:
- Launch new enterprise AI initiatives from the start
- Expand existing AI capabilities to enhance maturity
- Stay abreast of emerging AI innovation to maintain a competitive advantage
Why Traditional IT Strategies Are Falling Behind
The IT strategies of the past—once sufficient for pilot AI projects or isolated AI use cases—no longer meet the needs of Canadian companies striving to keep pace with rapidly changing technology.
Consider the retail CIO deploying store-level analytics and having to wait for cloud roundtrips when 50 cameras are streaming concurrently. Traditional IT architectures were not designed for real-time, data-intensive, AI-driven operations—and CIOs are now feeling that gap.
Growing AI challenges for Canadian CIOs
As AI spreads across Canada’s business landscape, CIO challenges are surging due to:
- Increasingly complex workloads
- Widely distributed data ecosystems
- Rising expectations for more intelligent automation (e.g. hyper-automation)
Next-generation AI solutions
Requirements for AI deployment in Canada are evolving rapidly, pushing CIOs beyond legacy applications, centralized architectures, and reactive operating models. To succeed, organizations must adopt modern IT infrastructure, supported by strong governance, skilled talent, and clear alignment to business outcomes.
IT Modernization: AI Strategies for CIOs
Many industries are modernizing IT in unprecedented ways to optimize everyday business processes and procedures via AI. Here are some industry-specific examples of how the power of AI is being harnessed:
- Healthcare – edge computing for clinical devices
- Financial Services – embedding AI in risk engines
- Manufacturing – sensor-driven predictive maintenance
- Retail – using AI to generate decentralized analytics
The CIO’s mandate in the age of AI
In the AI era, CIOs are no longer system custodians—they are architects of intelligent automation. The following AI enhancements are taking IT to a new level of transformation:
- Cloud-native applications, built-in AI features
- Multi-cloud provider integration
- Diverse workload patterns
- Deep learning/machine learning
- Predictive modeling & analysis
- Generative AI services
While the possibilities of high-performance infrastructure and complex computing power are endless, they demand more time, expertise, and resources to support; either in-house or outsourced to a managed services company, like Acronym.
To navigate this shift effectively, CIOs need a clear way to assess where their organization stands today—and what capabilities must come next.
AI maturity model
What’s your AI maturity level today? Organizations typically progress through defined stages of AI adoption—from early experimentation to enterprise-wide, AI-native operations. Understanding where you sit on this spectrum helps CIOs prioritize investments, manage risk, and plan a realistic path to scale.
While terminology varies, AI maturity typically progresses across stages such as:
- Awareness & Active – AI pilots and isolated use cases
- Operational – AI embedded into specific workflows
- Systemic – AI deployed across business units with governance
- Transformational – AI embedded across platforms, processes, and decision-making
Adapted from Gartner AI maturity frameworks
AI readiness: Canada’s AI adoption gap
According to Statistics Canada, as of Q3 2025, 14.5% of Canadian businesses – across all sizes and sectors – plan to adopt AI in the next 12 months, up from 10.6% a year earlier. While adoption is steadily rising, the majority of Canadian organizations are still in the early stages of exploring AI capabilities. In fact, almost two-thirds report no immediate plans to use AI, underscoring a broad market readiness gap.
This data reflects the reality that AI maturity in Canada remains uneven, with many small and mid-sized businesses still building foundational digital capabilities. Closing this readiness gap will be critical for Canada’s competitiveness as AI becomes embedded into everyday business operations.
Common AI Pitfalls for CIOs (And How to Avoid Them)
IT organizations are vulnerable to common, yet avoidable, pitfalls on their AI journey.
One major misstep is under-investing in data quality and observability—without reliable, well-monitored data pipelines, the most sophisticated AI models can produce misleading or unstable results.
Another risk is over-automating processes without establishing clear workflows or maintaining human oversight, which can lead to operational blind spots and erode trust in AI systems.
A successful AI plan hinges on:
- Hiring the right talent with the right AI experience
- Providing AI training programs to develop new skills
- Defining precise IT roles that match AI’s growing requirements
- Designing effective organizational structures for sustainable, ethical AI practices
Finally, robust change management is essential to ensure AI in IT initiatives deliver lasting value across the enterprise. This can take the form of playbooks, transparent communication, and incentives to engage teams and reduce resistance.
What a Future-Ready, AI-Optimized IT Environment Looks Like
Resilient. Secure. AI-Native.
Future-ready is a buzzword we all hear in the halls of IT. But what does it mean for CIOs? It’s all about building adaptable systems that are resilient, secure, and AI-native – a term that refers to architectures designed from the ground up to integrate and leverage AI across applications, workflows, and operations. AI-native environments don’t just use AI; they treat it as a foundational capability, enabling automation, intelligence, and real-time decision-making throughout the technology stack.
Looking ahead…
- Modern software apps will be distributed—making them more scalable and resilient.
- Security won’t be an afterthought—it will be “baked in,” with continuous verification ensuring systems stay protected in real time.
- AI-first operating practices will be the norm—embedded directly into DevOps and software development to accelerate innovation and automate intelligently.
And perhaps most importantly, future-ready IT will be built on a living architecture—one that evolves alongside AI-powered tools and agents, adapting to new demands and potential as technology advances.
What’s coming in 2026—Artificial Intelligence for IT Operations (AIOps)
The next year is on track to accelerate the shift toward autonomous IT operations that can self-diagnose, self-heal, and optimize performance without human intervention. Unlike broad AI adoption metrics across the general business landscape, AIOps reflects advanced, enterprise-level AI maturity.
According to Gartner’s article, Autonomous IT Operations 2026: 5 Must-Have AIOps Capabilities, over 60% of large enterprises globally will have moved toward self-healing systems powered by AIOps by 2026. This trend highlights how large organizations, especially those with sophisticated IT environments, are progressing rapidly into higher-level AI operations, even as broader market adoption remains uneven.
For CIOs, this signals a shift from reactive IT management to intelligent, self-optimizing operations that reduce operational friction and free teams to focus on higher-value innovation.
From Strategy to Action: An AI Checklist for CIOs
CIOs wanting to lead with confidence in the AI era need a focused checklist, like the one below, to turn strategy into actionable steps for implementing AI in IT.
AI in IT: CIO Action Checklist
✓ Start by auditing your IT infrastructure for AI readiness—this means evaluating compute capacity, data flow, and latency constraints.
✓ Next, take a fresh look at your cloud services, edge footprint, and SD-WAN posture to ensure they accommodate emerging AI workload demands.
✓ Prioritize closing gaps across data, model, and runtime layers to protect your AI ecosystem end-to-end as security remains paramount, especially in the cloud.
✓ Identify two high-value AI applications that can deliver quick wins and demonstrate tangible impact.
✓ Define an AI partner strategy that enables you to scale intelligently, tapping into external expertise and
platforms that complement your internal IT capabilities.
Acronym Solutions: Enabling the AI-Ready Enterprise
Acronym Solutions empowers the AI-ready enterprise by taking a future-proof approach to modernize IT infrastructure and service delivery.
Our secure connectivity solutions—including managed SD-WAN services—are engineered for low-latency workloads at the edge, ensuring seamless performance across distributed environments.
Our upgraded digital platforms provide resilient, scalable services that support dynamic AI applications and hybrid deployments.
Our comprehensive Managed IT and Secure IT offerings deliver end-to-end AI operations, governance, and security agents “as a service,” simplifying complexity while strengthening reliability.
Putting AI in IT: A pragmatic, three-phase view of AI adoption
Organizations often approach AI adoption in phases as they move from exploration to broader operational use. While every journey is different, a simple three-phase view can help CIOs think through how AI fits into their IT strategy today, and what comes next.
Phase 1: Assessment
Early efforts typically focus on understanding current capabilities, constraints, and opportunities. This includes evaluating where AI may help address specific IT or business challenges, the readiness of data and platforms, and the skills required to move forward. Rather than producing a formal roadmap, this phase helps leaders develop a realistic perspective on what AI adoption could look like within their unique context.
Phase 2: Pilot
Many organizations start with targeted pilots – often within a specific team, process, or application – to explore potential value and uncover technical or operational considerations. These initiatives help validate assumptions, surface integration challenges, and inform future decisions without committing to large-scale change too early.
Phase 3: Implementation and Expansion
As confidence grows, AI capabilities may be introduced more broadly across systems, workflows, or environments. This can include embedding AI into existing platforms, evolving infrastructure and cloud architecture, and establishing the governance needed to support reliable, repeatable use of AI over time.
Acronym supports clients at various points along this journey, bringing experience across IT modernization, cloud, data platforms, and operational enablement. Our role is to help organizations move forward thoughtfully grounded in their priorities, technical realities, and pace of change.
Learn how we can help you create an AI-ready enterprise.
FAQ's
Q: How should CIOs adapt to AI-driven business models?
A: CIOs must refocus their thinking from managing systems to delivering strategic value. This means embedding AI into core workflows, aligning IT with business outcomes, and fostering cross-functional collaboration between data science, operations, and leadership teams.
Q: What infrastructure is needed for AI at scale?
A: Scalable AI requires high-performance computing, robust data pipelines, cloud-native architecture, and machine learning operations (MLOps) frameworks for managing model lifecycles. CIOs should also prioritize observability (i.e. oversight) and zero-trust security to make large-scale AI deployments more secure across all platforms.
Q: Where do AI agents and robotic process automation (RPA) fit?
A: Robotic process automation (RPA) is a longstanding automation technology used to streamline repetitive, rules-based digital tasks. As organizations advance in their AI maturity, RPA is increasingly combined with AI agents to enhance automation with intelligence and adaptability. Together, RPA and AI agents can automate routine work, support decision-making, and improve operational efficiency. They’re especially valuable for service desks, finance, HR, and customer support—freeing up human talent for higher-value work.
Q: How do we measure ROI for generative AI initiatives?
A: ROI can be tracked through metrics like time saved, cost reduction, user satisfaction, and innovation velocity. CIOs should also consider qualitative impact—improved creativity, faster prototyping, and enhanced customer experiences.
To make ROI measurement more practical, organizations can establish a simple baseline-to-impact framework: identify the current performance of a process, pilot a generative AI use case, then compare post-implementation outcomes (e.g., reduced cycle time, fewer manual steps, lower error rates). This approach allows teams to quantify value early and refine metrics as AI capabilities expand.
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Manage IT
24/7/365 monitoring, support, maintenance, reporting and asset management of your IT infrastructure.
About Acronym
Acronym Solutions Inc. is a full-service information and communications technology (ICT) company that provides a range of scalable and secure Network, Voice & Collaboration, Security, Cloud and Managed IT Solutions. We support Canadian businesses, large enterprises, service providers, healthcare providers, public-sector organizations and utilities. We leverage our extensive network expertise to design and build customized, fully scalable solutions to help our customers grow their businesses and realize their full potential. With more than 20 years’ experience managing the communications system that enables Ontario’s electrical grid, Acronym is uniquely positioned to understand the mission-critical needs of any business to deliver the innovative and reliable services that respond to the changing demands of businesses, and support rapid growth and digital transformation initiatives.