AI, Cloud & IT Strategy in 2026: From Hype to Strategic Value...I Hope!

Across industries, 2026 marks a pivotal shift from early experimentation to measurable business impact in how AI and cloud technologies are adopted and governed. Organizations are moving beyond pilots to embed AI and cloud deeply into core IT strategy, operations, and competitive differentiation. Recent research from Gartner, enterprise tech leaders, and industry reports highlight three overarching forces shaping this transformation: agentic and domain-specific AI, AI-embedded cloud services, and enterprise-level governance and security.

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1. AI: From Pilots to Strategic Engines of Growth

Agentic AI and Business Outcomes

AI is evolving beyond standalone models toward agentic systems capable of goal-oriented planning, executing multi-step tasks with human oversight. These AI agents will be increasingly embedded into core business processes, evolving from standalone pilots and catalyzing improvements in productivity, customer satisfaction, and risk management.\

Strategic Enterprise Trends:

• AI moves from novelty to operational ROI, shifting CIO/CFO expectations toward accountable outcomes rather than experimentation. Forbes. In other words, it's time to put up or shut up!

• Domain-specific language models (DSLMs) are emerging as a key differentiator — offering higher accuracy and compliance for industry-specific tasks. Gartner

Vertical Impacts:

• Financial Services: AI is increasingly used for dynamic credit scoring, fraud prevention, and automated trading workflows. According to Microsoft, in 2026 agentic AI will support complex customer engagements and risk models. 

• Healthcare: Predictive diagnostics, personalized treatment planning, and scheduling automation benefit from AI decision augmentation, enabling data-driven care delivery.

• Retail: Personalized customer experiences powered by real-time AI insights can reshape demand forecasting and supply chain responsiveness.

• Entertainment: AI agents support content recommendation engines, creative workflows, and audience segmentation at scale.

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2. Cloud: Embedded AI, Hybrid Models, and Industry Solutions

AI-Enabled Cloud Services

Cloud providers are embedding AI directly into core infrastructure and platform services, redefining cloud from compute and storage to intelligent operational fabric. Gartner projects that 80% of enterprises will deploy industry-specific AI agents across multi-cloud environments by 2030 — a dramatic acceleration of cloud-AI convergence. 

Key Cloud Trends:

• Multicloud & Hybrid Cloud Dominate: Organizations increasingly adopt hybrid and multicloud architectures to balance performance, cost, and compliance. Gartner

• Industry Cloud Solutions: Recognized as drivers of differentiation, industry-specific cloud offerings help accelerate vertical-focused digital initiatives. Gartner

• Cloud Security Grows Strategically: Investments in cloud and AI security are expanding rapidly, with security now a critical component of cloud roadmap planning. Gartner

Implications for Verticals:

• Financial Services: Hybrid cloud supports regulatory data residency and resilience while enabling high-performance analytics.

• Healthcare: Cloud platforms accelerate AI workflows for imaging analysis and interoperable records while managing sensitive data securely.

• Retail: Scalable cloud architectures support omnichannel personalization and inventory optimization.

• Entertainment: Intelligent cloud platforms enable on-demand content creation, distribution, and audience insights.

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3. Governance, Security & Strategic IT Evolution

From “Cloud-First” to “Cloud-Smart” with Governance at the Core

IT strategy is shifting from blanket cloud migration to cloud-smart modernization: focused on cost visibility (FinOps), risk management, and compliance as integrated capabilities, not afterthoughts. 

AI & Security Realities:

• AI adoption continues to outpace readiness: only a small fraction of enterprises are fully prepared to exploit AI’s benefits, highlighting gaps in governance and controls. TechRadar

• Software supply chain threats and AI-related vulnerabilities are emerging as serious business risks, demanding automated monitoring and compliance frameworks. TechRadar

• Enterprises are increasing cloud security spending to meet regulatory and threat detection imperatives. Gartner

Strategic Framework Shifts:

• Formal AI governance, accountability, and risk controls are rapidly maturing from optional to required, especially under emerging frameworks like the EU AI Act. eWeek

• Security is increasingly centered on protecting unstructured data (text, audio, video) as GenAI reshapes data risk profiles. Gartner

Vertical Impacts:

• Financial Services: AI governance and embedded compliance (ex: KYC/AML) are becoming core product features, not add-ons. 

• Healthcare: Privacy, data ethics, and identity security become strategic imperatives in cloud-AI deployments.

• Retail: Integrated compliance helps manage loyalty data, pricing algorithms, and customer trust.

• Entertainment: Balancing personalization with content IP protection and consumer privacy drives new governance practices.

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4. Ecosystem Shifts & Competitive Dynamics

New entrants and strategic investments are reshaping the cloud-AI vendor landscape. For example, Brookfield is launching a cloud business focused on AI infrastructure, challenging traditional hyperscalers and highlighting the commoditization of cloud plus AI ecosystem services. Reuters

Concurrently, the market is seeing tighter expectations for business outcomes, tighter ROI scrutiny by boards and investors, and greater demand for smaller, high-impact AI-augmented teams. Business Insider

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Outlook: Strategic Alignment Over Technology Adoption

As I've argued for years, success in part comes from leveraging other people's assets, and that's what AI leaders are banking on happening in 2026. Success will favor organizations that:

• Integrate AI and cloud strategy with measurable business outcomes rather than novelty use cases, and accompanying real time analytics to demosnstrate the return on investment.

• Invest proactively in security, governance, and compliance frameworks that scale with AI adoption to ensure the humans remain in charge.

• Align IT strategy with vertical-specific imperatives; risk and resilience in financial services, patient impact in healthcare, conversion optimization in retail, and immersive experiences in entertainment. It's not sexy, nor new, but it's crazy how often deployments miss the mark.

This evolving technology landscape may be the "thing" we've sought for decades that elevates IT from cost center to a strategic growth engine; one that balances innovation, risk, and responsible governance to deliver sustainable competitive advantage.

AI's Distributed Future

I've been an expert in and advocate of distributed systems for decades. I was lucky enough to find my way into the automotive world at GM where multiple computers cooperatte to make a car operational. From there, luck struck again as I stumbled into Grid Computing at IBM where I learned about enterprise distribution and the inherent challenges and the promises of moving compute and storage as close as possible to where data and decisions live. One of my greatest successes was getting Duke Energy to reverse course and push simple computations they had planned to put on their mainframe, and overload it, down to the smart grid controller. I proved to Todd Arnold, then SVP Smart Grid, that only through distribution could they even come close to providing their customers with the service they designed. 

I've been considering writing this post for a week, fearful it was too far out there for people to grasp. Thankfully Aravind Srinivas, the CEO of Perplexity gets it. He stated last Saturday that on-device AI threatens the massive data center build-out strategy employed by just about everyone in the AI space today. Remember, this is a company backed by Nvidia, the ones making billions by filling those AI data centers with their product. 

Why is AI's future distributed? Cost.

Time is money, and automated systems need answers faster than humans starting at a screen. It takes time for input data to be shipped out, a decision to be made, and for that decision to trek back. Data has a unidirectional trend: growth! The amount of data we generate and consume grows every year, and it's not just cat videos. All those billions and billions of Interent connected devices, the sensors and controllers being distributed everywhere to control everything, are generating data that AI will need to consume. There's too much data to move quickly enough to meet the millisecond needs of next generation systems.

Beyond speed, compared to the cost of moving data, everything else is free. There's a reason cloud hyperscalers charge nothing for data ingress but gleefully bill for data egress. 

As we see announcements of hundreds of billions of dollars being dumped into AI data centers, generating fear of a bubble, it breaks my core belief that success comes from leveraging other people's assets. That phone in your pocket is capable of MUCH more than you use it for beyond cat videos. As a thought experiment, I was challenged by the CTO of Wells Fargo a decade ago to come up with a solution to providing 100% uptime. It was a test. Would I BS him, or admit there is no such thing. What he didn't expect was me handing him a workable solution within 30 minutes: store an emergency copy of financial transaction data on each person's phone in the secure sandbox your app creates. Yes, it would work, and he knew it. We are surrounded by smart devices that are I/O bound, meaning they spend most of their life doing NOTHING! That's what Aravind gets.

Here's a wrinkle though. What if the price of RAM goes up so high that companies pull back on IoT deployments? What if they have to scale down the available headspace of their devices to keep the costs down? What if laptops and desktops surged in price by 50% or 100% because we're competing with IBM and Google for silicon? Perhaps that's why, despite the obvious business value of narrowing down to the AI market, Nvidia has maintained availability of it's products for consumers, unlike Crucial who is withdrawing to focus their silicon capacity on commercial products exclusively.

How it plays out will be interesting, but just as I advocated for what we now call Edge Computing over a decade ago, I'll advocate for pushing AI workloads out to the endpoints, onto devices that are ready now in ways people don't understand.

What Is Customer Success?

Customer Success (CS) represents the start of a revolution, not just an evolution. For decades, the post-sales support function has been owned by sales teams, heavily incented to view prior sales as a burden to be offloaded. Consistent delivery is tough, and as these weak structures ran into challenges, customer frustration would eventually overflow the vendor bond, leading to a disruptive and ultimately avoidalbe split. Today, Customer Success destroys the self-centereed approach in favor of a business outcomes focus, one in which strategic discipline focused on what the customer needs to achive drives retention, revenue growth, and long-term company valuation. 

At its core, Customer Success ensures that customers achieve their desired outcomes while using a company’s product or service, creating mutual, measurable value over time. It's a proactive, outcomes-focused approach to managing customer relationships. Unlike reactive support models that address issues after they arise, Customer Success anticipates customer needs, guides adoption, mitigates risk, and aligns product value to the customer’s business goals.

In practice, Customer Success sits at the intersection of product, sales, support, and strategy. Its mandate is simple but powerful: when customers win, the business wins. Successful organizations reduce churn, expand accounts, accelerate time-to-value, and turn customers into advocates.

Key outcomes of effective Customer Success include:

• Higher retention and renewal rates

• Increased expansion and lifetime value

• Stronger customer advocacy and referrals

• Deeper insight into product-market fit

The Most Important Attributes for Customer Success

While tools, playbooks, and data are essential, Customer Success ultimately succeeds or fails based on people, mindset, and execution. The following attributes are foundational.

1. Leadership with a Customer Outcome Obsession

Great Customer Success leaders focus relentlessly on customer outcomes, not features, not usage metrics in isolation, and not internal assumptions. They deeply understand what success looks like for each customer and continually align engagement, onboarding, and value realization to those goals. These leaders have made a fundamental shift, from telling to asking. Asking better questions, listening actively, and validating the defintion of success in the customer’s terms provide the structure for leaders to marhsal resources into teams, build the required programs, and shift the focus from "what can we sell" to "how can we help".

2. Strategic and Business Acumen

Modern Customer Success professionals must understand their customers’ industries, business models, and economic drivers. This allows them to move from tactical support to strategic partnership—connecting product capabilities to measurable business impact such as revenue growth, cost reduction, risk mitigation, or efficiency gains. Without business acumen, CS risks becoming order-taking rather than value creation.

3. Proactive Engagement and Risk Management

The most successful CS organizations identify risk before it becomes churn. This means using data, health signals, and qualitative insight to anticipate issues related to adoption, stakeholder alignment, or changing priorities and acting early. Even better, bringing ideas and innovations to the table, taking the risk of missing the mark to demonstrate an interest in the customer's future; proactively builiding trust and positioning CS as a guide, not a firefighter.

4. Strong Communication and Influence

Customer Success requires influencing without authority—internally and externally. CS professionals must communicate clearly with executives, align cross-functional teams, and translate customer needs into actionable insights for product, sales, and marketing. The ability to tell a compelling story, value driven and tailored to different audiences, is a critical differentiator.

5. Data Literacy and Operational Rigor

Successful Customer Success teams balance empathy with analytics. They understand how to interpret usage data, health scores, and lifecycle metrics while also applying structured processes and playbooks to scale effectively. Operational rigor ensures consistency, predictability, and the ability to grow without lessening quality.

6. Adaptability and Continuous Learning

Customer needs evolve, markets shift, and products change. High-performing CS teams embrace change, iterate quickly, and continuously refine their approach based on feedback and results. Adaptability is especially critical in fast-growing SaaS and technology-driven organizations where yesterday’s best practices may not apply tomorrow.

7. Cross-Functional Collaboration

Customer Success does not operate in isolation. The strongest CS organizations partner closely with sales, product, engineering, marketing, and support to deliver a seamless customer experience. This collaboration ensures that customer insights inform roadmap decisions, go-to-market strategies, and innovation priorities.

The Bottom Line

Customer Success is a team today, but it's influence on the company, building a philosophy anchored in long-term value creation, nests in the organization's DNA. Companies that invest in the right people and attributes, align CS to business outcomes, and empower teams with both empathy and rigor are best positioned to build durable customer relationships and sustainable growth.

In an increasingly competitive and subscription-driven world, Customer Success is no longer optional—it is a strategic imperative.