Top 5 Enterprise AI Trends Every CIO Should Know
As enterprise AI adoption accelerates, IT leaders are moving rapidly from experimental use cases to scalable, production-grade AI infrastructure—from AI PCs and cloud platforms to real-time edge computing.
In the Blue Mantis webinar, “From Pocket to Cloud: Navigating AI’s New Edge in the Enterprise,” I sat down with Ajay Dholakia, Chief Technologist at Lenovo, to discuss how AI is no longer confined to innovation labs and is embedded into everything. From virtual assistants on our smartphones to automated agents in enterprise workflows and customer experiences, the decentralization of AI is accelerating—and AI is increasingly pushed to the network edge. Ajay and I discussed what this shift in AI infrastructure for CIOs means to the midsized businesses wanting to increase productivity, ensure compliance, and stay innovative.
What does it mean to have AI at the edge?
AI at the edge is the strategic deployment of AI capabilities directly on endpoints (e.g., laptops, mobile devices such as tablets/smartphones, IoT sensors, computer vision, et cetera) rather than centralized servers. This approach positions AI out of the enterprise datacenter and puts it closer to the user. The location of AI is a strategic concern for CIOs because the computations (or inference) are performed on the network edge, it enables smarter, faster decision-making all because the AI is closer to where data is captured and that compute needs to be AI performant.
What are AI PCs?
A corporate-owned Windows 11 device equipped with dedicated hardware optimized for running AI workloads locally. CIOs are considering AI PCs from Lenovo and other OEMs for their employees because these devices efficiently perform AI-oriented tasks like real-time transcription, image recognition, and predictive analytics without relying on communications with the servers (wherever they happen to be).
Ajay and I discussed how the CIOs we interact with daily are concerned with data security, cost control, and overall performance when evaluating use of cloud delivered AI on employee devices versus locally run AI on corporate-owned AI PCs. Everyone recognizes that employees using ChatGPT or other cloud delivered AI pose greater risks around data privacy, regulatory compliance, and third-party exposure. In the 2025 Gartner® Cybersecurity Innovations in AI Risk Management and Use Survey, 79% of IT leaders worldwide either suspect or have evidence that their employees are misusing AI tools at their jobs. Ajay noted these issues disappear using local AI processing on devices at the network edge because sensitive data stays on the device. This method easily supports zero-trust cybersecurity strategies and minimizes exploitable vulnerabilities on your IT stack.
A case can be made that AI PCs enable a more predictable and fixed-cost edge AI infrastructure that aligns well with long-term budget and capacity planning. Because performance and latency are critical, a key decision point for deploying AI is speed. That means cloud delivered AI is dependent on connectivity and server responsiveness, which can affect real-time operations and decision support. On the other end of the spectrum, AI at the network edge—processing data directly on endpoints—delivers faster insights (inference) and greater resilience, enabling smarter decision-making closer to where data is captured.
With these considerations in mind, here are five key takeaways from my conversation with Ajay that every CIO and IT business-decision maker needs to remember when deploying AI over the next 12-24 months:
1. AI Hardware at Scale is Fast and Smart
You’ve heard about how NVIDIA and other manufacturers’ Graphics Processing Units (GPUs) are foundational hardware for AI. GPU(s) are critical infrastructure required to train models and cloud providers have been accumulating that infrastructure for quite some time. For experimentation, cloud providers offer a great opportunity to build and learn. There are many programs (including Lenovo) that have capacity to help you prototype, if you are better served in a private environment and require the use of specialized, state-of-the-art infrastructure, you should consider exploring these programs. AI PCs with NPUs (Neural Processing Units) are game-changing. Neural Processing Units are specialized chips running in tandem with the PC’s AMD, Intel, or Qualcomm-based Central Processing Unit (CPU) to handle AI tasks efficiently. Think of the NPU as similar to how a car’s turbocharger boosts performance without overworking the engine. NPU-powered devices run AI applications faster and with less power, making everyday tools like laptops smarter for business users.
CIO Action Plan: Refresh endpoint hardware standards to include AI-accelerated devices. AI PCs are becoming the baseline for enterprise productivity and innovation. For example, a leading healthcare provider refreshed its endpoint hardware with AI PCs equipped with NPUs, resulting in improved diagnostic imaging processing times and enhanced clinician productivity, as reported by Gartner in their analysis of AI-enabled endpoints in regulated industries.
Most CIOs are counting on AI PCs to increase operational efficiency, with a 2025 study from IDC Research reporting 73% of IT leaders accelerating their PC refresh cycles to integrate AI capabilities. That same report predicts that the percentage of AI PCs in use will climb to 94% in three years—a huge jump considering it was less than 5% just three years ago.
2. AI Workloads Are Moving Beyond the Cloud
While cloud AI remains essential, modern deployments are becoming decentralized and hybrid. Edge computing processes data closer to where it’s generated, like a local warehouse handling shipments instead of routing everything through a central hub, reducing delays and improving speed for time-critical tasks. Hybrid strategies blend AI at the edge (tasks requiring high security and strict compliance) with cloud computing to leverage those vast resources for handling specific tasks that require more computational brawn. This hybrid AI architecture creates a balanced system that’s fast and scalable for midsized and large enterprises.
The main thing to remember is there’s no rule saying: “if you’re this kind of business then you must run all of your AI at the edge.” While CIOs across various industries run AI models at the edge—on factory floors, retail stores, and branch offices—closer to where data is captured, there are some cases where a manufacturer, retailer, or service provider can use cloud-based AI without any issues.
CIO Action Plan: Design AI architectures that include edge computing environments for latency-sensitive, role-specific use cases. Embrace a cloud + edge strategy to optimize performance.
3. Edge AI Enables Industry-Specific Use Cases
Such like a private vault for valuables, ensuring sensitive information stays protected and compliant without relying on external providers. Examples of how Private AI would help highly-regulated industries could include:
- An example of AI in manufacturing would be a factory producing specialized parts that installs smart cameras equipped with edge AI processors at various stages of the assembly line. These cameras use trained machine learning models to detect part defects such as cracks, misalignments, or surface irregularities in real time.
- An example of AI personalization in retail would be in a clothing store. Because fashions are regional and seasonal, private AI enables shoppers to get recommendations that reflect their personal and local style—while maintaining user privacy.
- An example of edge AI in healthcare would be a hospital group using an on-prem private AI model for patient data analysis, improving diagnostic accuracy without ever exposing sensitive information.
CIO Action Plan: Collaborate with department leaders (Operations, HR, Marketing, etc.) to identify your high-value AI use cases at the edge and build your AI foundations.
4. Private AI is Now a Strategic Imperative
With rising concerns around data sovereignty, data privacy, IP protection, and AI governance, CIOs are adopting Private AI models that run in secure, segmented, and controlled environments. Private AI models can be run on premise and cloud if you choose to leverage Kubernetes, with a control plane, to segment and secure data processing to guarantee compliance.
CIO Action Plan: Develop a roadmap for Private AI adoption, factoring in compliance (GDPR, HIPAA), data sovereignty, and custom model control for enterprise differentiation.
5. AI is Shifting from Innovation to Enterprise-Scale Operation
A new generation of AI leadership is emerging, with roles like Chief AI Officer (CAIO) leading strategy, risk management, and alignment across business units. Data Governance has been identified as the leading barrier to entry for AI enablement within the enterprise. As more organizations prioritize AI adoption, the need for accurate data (both inside and outside of the business) is crucial to effectively support AI usage goals. The axiom of bad data leading to bad decisions is one that the C-suite at every company is well aware. The problem is when organizations store their data in separate siloed data marts (e.g., your pricing data isn’t connected to your product database which isn’t connected to your sales data…) and there’s no single centralized data authority at the company. Ensuring data quality for AI requires a published strategy and executive sponsorship.
CIO Action Plan: Invest in an AI Governance Office (AGO) to centralize governance, training, ethics, and deployment frameworks. Your AGO can ensure cross-functional alignment between IT, legal, HR, and data teams to optimize your AI investments.
Blue Mantis Helps You Deploy Secure AI
While it’s still too early to call AI a required (or even mainstream) IT component for businesses, CIOs are currently shifting from experimental pilots to scalable, secure, and production-grade AI deployments—especially at the network edge. Over the next 12-24 months, expect AI PCs to become foundational infrastructure, enabling real-time, local AI workloads that enhance performance, reduce latency, and support zero-trust cybersecurity strategies. The decentralization of AI out of the cloud and out to edge empowers industries like healthcare, manufacturing, retail, and law to maintain compliance, protect sensitive data, and optimize costs. And with private AI models and hybrid architectures becoming strategic imperatives, CIOs must rethink their IT stack to support secure, role-specific AI use cases across endpoints.
Connect with Blue Mantis today and we’ll assess your current IT stack for AI readiness and help you securely deploy AI into your business.
CIO FAQs on Edge AI
Encrypted backups, geo-redundant replication, and container-based failover protocols are key for minimizing AI downtime.
Yes—by minimizing cloud usage and maximizing inference efficiency, Private AI can reduce TCO by 30–50% over 24 months.
Use containerized LLMs, implement telemetry, and establish automated retraining pipelines to maintain model accuracy and performance.
Multimodal AI processes text, image, video, and voice.
Multimode AI scales across cloud, edge, and device. Together, they power adaptive, human-centric AI systems.
Most enterprise deployments move from contract to production in 8–14 weeks with a phased, low-disruption rollout.
Use a parallel integration model that enables real-time upskilling without interfering with daily workflows.
Final Thought: AI Leadership Starts with the CIO
Enterprise AI is no longer experimental—it’s foundational. From cloud AI to edge inference and Private AI deployments, the next phase is about operational excellence.
