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.

What do foot-worn paths on grass have in common with AI?

Have you ever seen an expanse of grass with paths worn down to dirt where people walk to short cut the provided sidewalks? I was once told the Walt Disney Company looks for these patterns to understand how people move and where they need to re-think access, often replacing the grass with a new walkway. People find their own solution when one isn't provided, and that reality is a threat to the adoption of AI for customer services in the world of retail.

I've had the same issue crop up multiple times over the past year with Amazon, and all thanks to their use of AI. As happens on occaision, a package out for delivery from Amazon never arrived. Yesterday I went online to follow up and was given the option to cancel the order. I clicked the link and was dumped into the customer support Bot. I explained I wanted to cancel the item selected because it never arrived. Amazon's AI had other plans. Instead of cancelling the order, it informed me the item was en route and asked if I'd like help with anything else. Uh, yes. I want to cancel the item that never arrived. Instead of helping me, I was presented a list of recent purchases, excluding the one in question, and asked which one I wanted help with. Irritated, I started over again. This time the AI informed me the item that didn't arrive on the 19th would arrive on the 20th despite it being the 27th. 

Even more irritated at having my time wasted, I sought out a customer service agent via chat and was finally able to get cancel and get a refund for the item that never arrived. That the AI decided my probelm was solved initially with no input from me was irritating. That I had to fight the system only to give up and go to a humn was very irritating. But the ultimate irritation is this repeated "experience" when trying to use Amazon's return process as implemented. What I've learned is not to waste my time, their AI isn't ready for prime time and I'm not a beta tester.

But it's not only Amazon who's failing. Enter Target.

Our family loves the game Catana, so much so we needed another copy to keep here at the house instead of always chasing down our daughter to bring hers home on visits. My wife was notified that Target had it on sale for 20% off, an AI generated message. Awesome! She ordered it for pick up at our local store, but when the pick-up ready notification arrived, it was out of stock and she was asked to choose another store. A second option, about 15min further away, showed 6 in stock so we drove over. Nope, not a single one in stock. The explanation? Their AI can only see what was in stock recently, not real time. Wonderful. At that point she gave up on the garbage AI and got her refund, and we stopped at a third target on the way to dinner and picked up the game.

My third immutable law of AI is that AI always gives an answer, no matter how nonsensical. It's not enough to implement AI and pray; the customer experience has to matter too! Any organization who wants to make effective use of AI needs to recognize it's limitations, and until it's proven infalible, provide alternatives. 

Retailers would we wise not to expect people to continue using a broken process. Customers are too smart not to see the dirt paths criss-crossing the landscape.

My Three Immutable Laws of AI (from the 1990's)

I started in AI R&D in 1991 at Allen-Bradley followed by three years in the AI Development Group at Eaton-Cutler/Hammer. I learned three lessons that have proven to be immutable truths over the past 30 years:

1. AI is Biased - thankfully this is no longer an issue hidden on the underbelly of the beast, and the truth is that likely all algorithms are biased. There are many biases built into training data, including the decision of what training data to include and where to source it. Be aware that finding uniqueness, that diamond in the rough, is highly unlikely if the data set includes no prior examples.

2. AI Cannot Predict the Future - There's a specific bias many overlook: time. AI is biased entirely in favor of the past; it cannot predict the future. Instead, what we call "predictions" are really just the recognition of patterns from the past, extrapolated into a guess about the future. Keep in mind the hot hand fallacy because AI often doesn't. Probability itself is based on a biased assumption that we can predict the likelihood of future events, and being close, even often, is not the same as being right.

3. AI Always Gives an Answer - today we call it "hallucinations", but the problem with AI giving non-sensical answers is not new. AI can be trusted, until it can't, and there's no way to know in which realm the answer you're provided sits. Given my background in plant floor automation and embedded systems, you can understand how this was, and continues to be, a showstopper in many ways. 

AI matters. It’s helping us solve problems and gain efficiencies never realizable before. It's a great tool, but it's not a replacement for independent, critical thinking. And always be skeptical of any AI deployment where safety and security are paramount. Back in 1994, some of my AI research in fuzzy logic and neural networks was focused on self-driving vehicles. And we're still waiting.

Gold Mining or Equipment Rentals?

If you owned a gold mine, would your focus be on extracting the gold, or helping others extract the gold by renting them the mining equipment? Telcos would jump at the chance to provide equipment rentals.

After a decade and a half in the Telco world, in May of 2025 I escaped. I had joined the telco world with the belief that they would own the cloud. Who could possibly be better positioned in the burgeoning world of cloud computing than a company entrusted to transport data? I knew in my heart as a strategist that leadership was ready and understood the fatal flaw in the cloud strategies of Amazon, Microsoft and Google: large, regional data centers. In contrast, telcos had available infrastructure all the way down to the highly coveted last mile. Given the adage that compared to the cost of moving data, everything else is free; the opportunity was obvious. Able to put the compute and storage at the last mile, as I blogged about a decade ago, and telco's would rule the cloud.

Was I right about the opportunity? Yes. Edge computing as it's now called, proved it. Was I right about telco leadership seizing the opportunity? Not even close.

After stints at AT&T and Verizon I have learned one immutable truth: there is no innovation in telecommunications. That era ended with the forced divestiture of AT&T Labs during the famous 1980's DOJ breakup of Ma Bell. Executives at telcos have one idea they live and die by: maximize return on assets. It makes sense to a degree given the massive investment they've made in copper, fiber and spectrum. In the telecommunications world, innovation is outsourced. Ericson, Nokia, Cisco, Juniper, etc. What is sold as innovation is acutally integration. Telco's own the assets, everyone else owns the innovation. 

If you were involved in cloud between 2010 and 2015 you know that enterprise customers didn't trust public cloud and often railed against it. Customers wanted cloud, just not public cloud. They needed something more secure that hung off their backbone and enabled easier and faster integration with their partners (innovation that was outsourced again, this time to Equinix). And the truly visionary CIO's wanted a step further, to push process down into the network.  

While at AT&T, I navigated my way to the cloud computing product team and worked to influence their thinking by bringing an outsider point of view and the voice of our customers. I had financial services customers who envisioned pushing settlement down into the network, healthcare customers interested in performing image analysis at the edge, and one bank that wanted to buy up to 600,000 compute nodes. AT&T's NetBond product served an important tactical need, connecting customers to the public cloud providers they were starting to use. But the big opportunity was providing the secure, reliable, trusted compute and storage that Fortune 500 CIO's felt wasn't available in the cloud market. Whomever satisfied that need was poised to talk about the SDN and Edge Computing, capabilities that would threaten the hyperscalers. 

NetBond was the begining, and the end, of cloud at AT&T.

Despite providing a strong business case and making introductiosn to the founders of starups able to bring the needed capabilities into the network, there was no interest. On the surface, it's hard to find money for anything other than spectrum at a telco. But hidden under that argument there was an even better reason. While speaking at InterOp in Vegas I received a phone call from an AT&T SVP explaining my deal to sell hundreds of thousands of compute nodes was dead. I pushed for an explanation and was told the internal cost of compute was an order of magnitude greater than the hyperscalers. Gobsmacked, I asked how we made money on the customers we already had. We weren't, but luckily we'd sold less than 2k nodes. AT&T shut down it's cloud operations shortely thereafter. 

After a short stint at Tangoe, I arrived at Verizon, knowing their cloud story a bit better from previous experience. In a former life I'd been asked to partner on a plan to help Terremark avoid a second bankruptcy. I asked to be read into their strategy. Cloud computing. Yes, but what's the strategy? VMWare. No, that's a product, what's the strategy? No, that's the strategy. Well, I said in my not so humble opinion, they're destined to fail because VMWare is crazy expensive and will lock them into innovations a generation and build cycle behind the public cloud providers. Nobody listened. Verizon bought Terremark, then wrote it all off soon after I joined months before Verizon divested the Terremark assets and sold the business to IBM. What remained was Verizon's NetBond competitor, Secure Cloud Interconnect, with the same value prop: lock in the innovations of everyone else in exchange for locking in a portion of the transport; transport that will be devalued every year as customers demand lower and lower prices.

How could I have been so wrong? After significant soul searching, I realized what I saw was invisible to the generations of telco executives focused exclusively on transport. They talked in assets, whereas I talked in architectures. What I inherently understood escapated their notice: the company who owns the architecture owns the future. Telcos by nature don't believe in owning architecture, and asset owners don't innovate, they manage. 

I learned a valuable lesson painfully. Now I see the fingerprints of architecture vs assets everywhere. Why are car companies developing their own infotainment systems? Architecture. Why are streaming companies steaming to the precipice of their own demise? Assets. Why are the naysayers warning of an AI bubble? Assets.

If only I'd re-read my own articles on the evil of assets in 2012, or that economics always wins in 2013 about leveraging the assets of others. 

Now the telcos are in a race to the bottom of the cost curve, the eventual reality of every company who cannot differentiate the value of their product. Firings are the norm. AT&T fired 9.5k people in 2024, Verizon fired 13k people just before the holidays this year. Why? Their executives were sitting on a gold mine but were focused on the mining euipment.

Ugh

Newton’s Three Laws and the Public/Private Cloud Debate

For the past five years I’ve noticed a change in the conversation with CFO’s on the use of cloud computing.  I’ve always known CFOs are the driving force behind the adoption of public cloud.  Who could say no to better, cheaper, faster with lower capex and the ability to reduce the hard assets of IT which all too often anchor technology in the past?  However, today I increasingly hear CFOs push back on public cloud driven by one singular concern: predictability.

From a financial point of view, there is a critical assumption built into the pay-as-you-go model: that consumption and therefore cost is predicable.  In the early days public cloud was always cheaper, however, great strides in private cloud technology, maturation of the space, and the challenges in moving to a cloud centric IT platform have muddied the waters.  As someone who has never been a proponent of private cloud, I feel the water is as clear as ever.  IT infrastructure has never been built on predictability.  For decades, networks, servers, and storage have been designed to a “just in case” standard.  The result is tremendous bloat which is being engineered out through virtualization, but the workloads are no more or less predictable.  What’s missing is the understanding that public cloud isn’t just about capex vs opex, it’s also about momentum and friction.

Infrastructure assets are evil; they anchor us in the technology of the day and act as a damper on innovation.  I’ve lived the nightmare of being forced to architect a solution around existing infrastructure assets, sometimes merely to justify their existence regardless of their impact on the solution.  Assets grow like a planet, swirling gasses of expectation coming together forming a gravitational field which attract more mass in the form of processes and protections.  Soon the planet becomes so massive it starts attracting its own moons of ancillary assets into orbit.  Where there’s mass we have to respect Newton’s three laws.  Objects at rest tend to stay at rest and thus have no momentum, not the message the CEO wants to hear in relation to innovation.  Objects require a force to accelerate dampened by friction; the greater the mass, the more friction will work against the building momentum.  And since for every action there is an equal and opposite reaction, to generate the momentum required for change, ever increasing investments in time and energy are required.

Like the car travelling toward the horizon will get closer to the mountains but not the moon, private cloud gets a company into virtualization but not cloud computing.  I know some will argue I’m too optimistic, however I point to the success of companies such as Netflix, whose competitive advantage is the result of investing early in learning the lessons of public cloud.  I also point to respected executives like Chris Drumgoole, COO of IT at GE, making bold public statements about their migration not just to cloud, but to public cloud.  What he has said publicly many others have told me privately, but first they must get their CFO’s to look past the head fake of relating predictability to public cloud risk.

Cloud and the Omni-channel Customer Experience

We are truly living in a connected world.  Today I checked the weather on my computer and paid some bills before checking my email on my phone en route to my SUV.  Once I sat down, my SUV automatically connected to my phone and minutes later showed an incoming text from my daughter, whom I called back via voice command.  She texted me her order for a meal I bought via an app while waiting in a parking lot, and returned home to watch a video she created earlier in the day, by mirroring her phone with the TV.

Impressive to say the least.  Remember NONE of the solutions I used were designed by or bought from the same company.  Yet amazingly they meet my need, to help me make the most of my time.  In our connected world we have the opportunity to always be busy, to cram as much as possible into our daily life.  Mobility, eCommerce, Social Media, Collaboration all let us take advantage of those previously lost moments in time, whether waiting in the car or sitting at the airport.  And at the foundation of all of this technology, which is able to work so seamlessly together, is cloud computing.

While my consumer oriented world is full of neat new toys, the world of retail is trying to figure out how to play a bigger role; not just Retail as in stores, but retail as in producer/consumer interaction including banks and healthcare, automotive and high technology.  I haven't seen a business strategy or talked to a C-suite executive in retail the past three years without the topic of Omni-channel finding it's way into the conversation.  An Omni-channel Customer Experience is a simple concept: the creation of a common look and feel across all channels through which a customer interacts with a company.  Simple in concept, yet frustratingly difficult in reality, but companies know their customers expect a consistent experience.  CIO's know whoever delivers on the promise best has the opportunity to create some daylight as they pull ahead.

Why is omni-channel difficult to implement in a single company yet already a thread holding my personal life together?  The difference is cloud computing.  Each of the solutions I used today was built in the last five years on a cloud foundation.  However cloud continues to prove elusive for corporate America, and for numerous reasons.  There is simply no way to build an omni-channel customer experience and avoid cloud computing, yet focusing on cloud computing won't deliver the experience nirvana either.  It's easy to understand why CEO's to CIO's are frustrated.  It's the enigma of modern IT: all the data, compute and storage one could possibly ever need, and nothing put together in a way that makes it truly useful.  It's the cost of holding on to outdated models content to reap the benefits of one technology generation without considering the next.  Companies today are islands; islands of applications and data if not servers and storage as well.  Data in isolation is almost worthless in today's world of Real Time Analytics and Big Data.  And making it all the more frustrating, you can't hurry cloud, you just have to wait.

Cloud Computing is the single most important technological shift which has happened in Information Technology.  For the first time it's not about a domain, such as the network or data or applications.  Cloud is about everything, from technology to taxes.  Like the artillery shell that's a degree off when fired, those who get cloud wrong will simply miss the mark, measured in cost in the short term, but ultimately measured in customer satisfaction and solvency.

The Last Mile and the Future of Cloud

"Look back to where you have been, for a clue to where you are going"

It's an unattributed quote, but I find it applies repeatedly throughout technology.  So often what appears new is really a twist on a tried and true approach.  Anyone who's spent any time around networks knows the phrase "last mile".  It's a reference to the final leg of the network connecting a home or office.  When AT&T was broken up in 1984 by the US Government, AT&T emerged as the long distance company while local service was divided into seven "baby bells" including Pacific Telesis, Ameritech, and BellSouth.  Experts believed long distance held the promise of higher profits while the Regional Bell Operating Companies (RBOC's) were doomed to a capital intensive, low margin struggle.

The experts were wrong.

Owning that "last mile" turned out to be very profitable; so profitable one of the RBOC's, Southwestern Bell, was able to buy those other three RBOC's, changed it's name to SBC, and then bought it's former parent, AT&T.  Although mobile networks are great for connecting smartphones and tablets and satellites can deliver radio and television, it turns out nothing yet can replace fiber and copper for bandwidth and low latency.  After the Telecommunications Act of 1996, last mile services exploded and today instead of just the local phone company there are a variety of competitors including Google.  And providing that last mile of service continues to be a significant revenue driver.

Let's put the last mile conversation to the side and switch gears.  Today large corporations are investing billions of dollars in Big Data; growing their analytic capabilities to generate the oxygen required by their growth engines.  These fierce competitors are slowly realizing there simply isn't enough time available to:

1. capture data at the point of origination
2. move the data across the country
3. filter the data to focus on the most valuable elements
4. combine the data with other data to broaden the perspective
5. execute analytics on the data
6. generate a result
7. communicate the result back across the country
8. leverage the result to drive some benefit

If the network operates at the speed of light, how can there not be enough time?  Beyond the reality that light slows down in fiber (by about 31%), there is not one single direct link between the user and the corporate data center.  Users have to be authenticated, security policies applied, packets routed, applications load balanced.  The multitude of events that occur, each one very quickly, add up to a delay we call latency.  In a world where everything is measured in minutes latency goes unnoticed, but in our Internet world we are moving from seconds to sub-second time frames.  Think about how patient you are when navigating through a website.  After that first second ticks off the clock, people begin to wonder if something is wrong.  It's the byproduct of taking our high speed access to the Internet for granted.  Marketers want to collect metadata about what you're trying to do, figure out how they can influence you, and insert themselves into your decision chain; and they only have the time between when you click the mouse and when the browser refreshes.  Hopefully now you can understand why that eight step process, moving data across the country is so unappealing.

For the past three years I have advocated an alternate approach; putting their servers as close to the end user as possible (commonly called "the edge").  Where "the edge" is located depends on the conversation, however the furthest it can be is at the start of that last mile, the last point on the network before it connects to the end user.  Today the edge extends as far as the same city for large populations, more often it's a region or even a state.  Although my serverless computing concept could be part of the answer and move the analytics into end user's computer, in truth at least some analysis needs to occur off-site, if for no other reason than to stage the right data.  Moving analytics closer to the edge requires us to move compute and storage resources closer.

Let's return to the "last mile".

If you looked at a map of the network which serves your home or business, you would notice the wire goes from your house, out through a bunch of routers, switches and signal boosters until it finally reaches a distribution point owned by your provider (or wherein they lease space).  These locations are often large, having previously housed massive switching systems for the telephone network, and they are secure, built like cold war bomb shelters.  What if these locations were loaded with high density compute and storage available much like a public cloud to augment the resources within the corporate data center?  If a business can operate while leveraging public cloud resources, what we lovingly refer to as a Hybrid Cloud model, then wouldn't it make sense to push the resources as far out toward the edge as possible?

I'm hoping you are increasingly buying into this idea, or at least skeptical enough to wait for it to implode, and want to know how this is broadly applicable.  I do not see this as a panacea, any more than I do serverless computing, cloud, big data, mobility or any other technology.  However I do see it at a minimum as moving the conversation forward on how to deal with a world where our endpoints are no longer fixed, and at a maximum another arrow in the quiver.  Consider how much data is being collected today; from the GPS location in your phone to the RFID tag on your razor blades, we are living in the Data Age.  Every single device powered by electricity is a likely candidate to be internet enabled, what we call the Internet of Things.  Each of these devices will communicate something, creating new data every day and adding to the pile of data that already exists.  To deal with the onslaught, companies need to filter what's coming in, remove the noise, and then execute their normalization routines (standardizing date formats, to get the data ready for use.  Since compared to the cost of moving data, everything else is free, there is an economic incentive to move data as short a distance as possible.  Handling the grunt work of analytics locally could have a dramatic impact on overall system speed.  And over time, having local compute resources will enable software architects to push analytics closer and closer to "the edge".

Today I am unaware of anyone working on this issue, despite pushing for it and finding a few leading edge Fortune 500 executives already facing this challenge.  The truth is we live locally, we're served locally, why not compute locally?  I see this as a gift of future revenue, sitting on the doorsteps of the telco's and cable providers waiting for them to create the product.  However I don't believe they realize what they have.  There is no last mile provider who has made a splash in cloud or big data. They own the last mile; they're the ones who own the gateway that links the world to their customers. Moving the public cloud from super-regional data centers to the local central office where the last mile connects could make the telcos instantly relevant in cloud and give them a nearly insurmountable competitive advantage over today's public cloud leaders like Amazon and Microsoft (perhaps this is part of the reason Google created Google Fiber).  Imagine a legacy infrastructure being resurrected to meet an emerging need.  But then, as I said at the beginning, "Look back to where you have been, for a clue to where you are going"

P.S.  I was so excited to see the headline "IBM, Juniper Networks work to build networks capable of real-time, predictive analysis", until I realized it was the opposite of integrating data analytics into the network.  Oh well, my quest lives on.

Value * Easy = Consumption

Over the past decade I've witnessed a constant stream of IT executives and technology professionals view cloud as a threat to their careers.  When viewed through the eyes of an internal IT shop where the business has been a captive customer I can understand their worry.  Now they're being asked to enable innovation instead of taking orders; bring solutions to the business instead of begrudgingly accept new challenges.  However I can't understand why they don't see the other side of the cloud coin, the very equation which drives cloud adoption: Value * Easy = Consumption.

Public Cloud has been built on two value propositions.  First providing value through availability to resources, in a short time period, without capital investment.  Those three values align with the strategic goals of every CxO no matter how you write them:

• "Do more with less"
• "Improve agility, elasticity, efficiency"
• "Reduce costs"
• "Shift from maintain to innovate"
• "Remake the cost curve" (*my personal favorite)

Those are just a sample of quotes from CxO's I've worked with over the past decade.  Moreso, each of the CxO's had a common opinion of IT: too slow and expensive for the value delivered.  This is the environment into which AWS started selling it's cloud capabilities, back before we had the phrase "cloud computing".  It's important to remember AWS grew out of Amazon's own internal needs, it was not the result of market surveys and product development.  Although Fortune 1000 adoption of public cloud has been slow, the concepts of cloud computing rapidly penetrated corporate America in an attempt to bring the AWS value proposition to the enterprise.  Shifting from a hardware centric view to a capability centric view of infrastructure is a major upheaval in approach.  

Given very few companies have been successful in adopting cloud, what's the holdup?

Whereas a CIO can buy "Value" in the form of tools (BMC, VMWare, etc.) or rent it (AWS, Google, Azure, etc.), the truth is they can't buy, rent, lease, borrow or even steal "Easy".  Making something easy isn't easy, and cloud is anything but easy.  Put yourself in the shoes of a business executive such as the Chief Marketing Officer or the Chief Financial Officer.  In your world you have very few hard assets, having shifted most everything to a lease model; from office space and PC's to digital advertising and audit.  You can shift your spend as your business changes throughout the year.  What you need are technology solutions able to meet your need for agility, elasticity and efficiency.  How does your IT team respond?  Building out large scale data centers, buying servers, writing software.  Do any of these approaches appear to be in synch with the CMO and CFO's needs?  No.  In fact strategic planning with IT is so difficult, these leaders are increasingly willing to go outside the company, which is not easy, to get what they need.  They're willing to invest their reputation and the success of their team in taking a risk to convince the CEO, corporate security, office of the general counsel, and fellow business leaders that going around IT is the right strategy.  Then they spend money on consultants and hire talent to move in the new direction.  And yet all of that is considered easier than getting solutions from IT, the place they would prefer be the first, last and only stop.

Without a concerted effort to make cloud use easy the entire equation is upset.  Easy is the governor on the economic engine of cloud.  Having cloud capabilities, being able to deliver the "Value", isn't enough.  When done right, "Easy" is a multiplier of "Value" and drives consumption significantly beyond expectations.  At that point IT executives and technology professionals don't view cloud as a threat to their careers, it morphs into a driver of career opportunity.  Their own value increases dramatically, and their strategic value to the long term success of the business in particular.  In my experience it's much more rewarding to have a seat at the table to discuss how to accomplish some new goal than being berated as the barrier to accomplishing an old goal.

Cloud in the enterprise will never be a success without "Easy".

We're Well On Our Way to Serverless Computing

As I discussed in my first post, I came up with an idea I titled "Serverless Computing" in 2002.  At the time I was frustrated by the limitations of web, application and data server capabilities.  I was implementing a rather amazing B2B marketplace for a drug company; a leading edge architecture I had developed using XML at its core along with Java messaging services and styling objects to render the final views.  The same architecture and implementation had to support multiple lines of business without any crossover.  My frustrations led me to start questioning everything.  If things weren't working, why was I continuing to do everything the same way I had before?

In the middle of a snow storm in central Connecticut, sitting in a frozen rental car waiting for warmth (I'm from the South), I had an epiphany.  I realized most of the constructs of computing are driven by human needs, not the computer.  It dawned on my all my architecture work was about putting stakes in the ground as anchors for our thinking and development of the portal.  I was reasonably good at refactoring applications to improve security, efficiency, and efficacy; why not apply the same thinking to architecture?  I scurried down a path of thinking that led me to the conclusion our modern architectures are built on so many layers of abstraction that we've lost sight of the why.  I was perpetuating the problem by blindly following the norm.

As yourself this: where did the concept of a "server" come from?  Many people refer back to the origination of client/server computing; decoupling the processing unique to each user (the client), from the processing common to everyone (the server).  Once software was decomposed into two complimentary applications, it could run on two different computers where the server does the heavy lifting and is therefore optimized for its workload.  In reality client/server is really an extension of the mainframe architecture where desktop PC's replace the dumb green screen terminal and, by their nature of having a processor on board, share some of the processing load.  That's all good, but what drove the creation of the mainframe, and therefore client/server, was economics.  By centralizing processing power and enabling remote access, mainframes delivered a reasonable economic model for the automation of basic business tasks.  Dumb terminals made sense when people and the mainframe were local, few applications existed, applications were simple, and the costs of infrastructure was high.

Today none of the original drivers of mainframes and client/server exist, yet we still use the architecture unchanged.  If you took an ultra-modern data center and walked someone from 1965 through the center, there is no way they wouldn't mistake the mass of pods for mainframes.  Those massive data centers are nowhere near where the people who use them work.  In fact we can no longer assume employees even work in buildings, or from 8am to 6pm. The software landscape consists of billions of applications with thousands more created every day.  And the cost of infrastructure is so low thanks to density and scale, a modern smartphone has more processing power, memory, storage and network bandwidth than a "server" did just a decade ago.  We are surrounded by highly capable, network accessible computing devices which spend the majority of their life I/O bound, just waiting around for something to do.  Why are we letting all that computing power go to waste?  We're ignoring the real promise of cloud computing, a concept closer to P2P than the Internet and what we think of as public cloud today.  I'm talking massive distribution of applications, data and infrastructure; the kind of infrastructure people cower at when talking about cloud, but fully embrace when talking about the Internet of Things.

We need to rethink our approach to computing.  Period.

When you tear out the non-value added elements of client/server, the one tenet which survives is decoupling: separating the user interface from the business logic.  Decoupling's primary value proposition is at the software layer, not hardware, as we are reminded of every time a web server goes down.  And hardware, when viewed through cloud computing optics, is nothing more than a pool of resources (compute, memory, storage, etc.).  If we take the widely available computing resources we have on our client devices and run our user oriented "server" software there, we gain several benefits:

• decreased impact of "server" outages
• reduced complexity of "server" environments
• federation of power consumption over the entire power grid
• elimination of the need for large, centralized data centers
• reduced long haul bandwidth requirements
• raises the barrier for DDoS attacks while reducing the risk of penetrations as key data never leaves the premises

Preposterous!  You're crazy! Insane! Never! Yet that's precisely the direction we're not only heading in, but we are fast approaching the arrival platform.

The whitepaper I submitted to multiple outlets in 2002 told me I was crazy (including my employer at the time, IBM).  Nobody asked me to explain my thinking or even gave the idea a second thought.  Yet today I'm more convinced than ever it's the endgame of where we're heading.  Consider the rise in the popularity of Docker, a container oriented tool which approaches virtualization correctly (as opposed to the crazy idea of virtual machines which replicate the bloated operating systems multiple times over).  Consider the rise of microservices, self contained services which are distributed with the core application.  We are at the threshold already.

Moving over the threshold requires a tweak to Docker so it can be deployed seamlessly as part of an existing operating system install similar to Java, and the management tools required in a massively distributed system.  Second we need similarly scaled data federation tools which I don't believe exist today (for more on data federation see my entry on The Data War and Mobilization of  IT, or my  upcoming entry on Data Analytics in the Network).

Just imagine how the world of business computing would change if we eliminated just 20% of the web and application servers?  How about reducing web and application server instances for consumer  cloud offers such as Office 365, or for your bank.  Go way out on a limb and consider the adoption of P2P tools such as BitTorrent Sync.

And by the way, I'm still waiting for someone to provide mainframe based public cloud services.  Where is the new EDS?

Going Against the Grain

I've struggled for the past two months to write this entry.  It started with the topic of innovation and why companies are struggling at it, but that quickly devolved into a "how to be more innovative" treatise.  However if you're like me, you've already read several great articles and heard numerous speakers lay out a foundation for innovation.  And at some point you realize you're reading the same thing over and over again because, for whatever reason, nobody's listening to the message.  So another person jumped into say it a second time.  Then a third time.  Fourth time.  Fifth.  Well that's obviously a broken path, so rather than be the sixth I realized I needed to take a new direction.

In that moment of despair after endless edits, thinking perhaps my argument was flawed (which would explain why it was so hard to capture), it dawned on me to go back to the basics.  Yes, innovation is a struggle. But why?  Is it really just because innovation requires a willingness to invest in failure which is anathema to a company focused on quarterly results?  I don't think so.  I think the problem is more basic and has to do with the cultural proximity of the smart people who invent, the entrepreneurs who innovate, and the public who wants everything better, cheaper and faster.

Through the 1960's in the US we had a healthy habit of churning out earth shattering inventions which drove economic growth for decades.  However invention requires patience, a tolerance for failure, and funding.  As companies tightened up their bottom lines through the 1970's and 1980's we subdued this habit in the name of global sourcing and cost cutting, moving our research off-shore to locales with lower cost labor.  Of course nobody considered the opportunity cost of this shift.  One of the most often discussed result of this approach is how billions of dollars in economic growth have been shifted from the US to foreign countries, raising their standards of living and education while ours have remained stagnant or dropped.  However there is another opportunity cost rarely considered; what happens when you move research half-way around the world to a new culture which doesn't share the same appetite for change and risk as the United States?

Our culture in the United States has an acute case of individualitis.  In fact the "American Dream" is based on the concept of the individual controlling their destiny through dedication and hard work.  Our government was established to protect the rights of the individual from tyranny.  The reason a free market capitalist system works in the US is because it's the only system in which the talent and effort invested by the individual delivers a powerful dividend.  Capitalism is the great equality engine because it rewards innovation.  The rise of American economic power started with the Industrial Revolution, but it wasn't fueled by invention as is so often argued.  Innovation, the use of inventions to solve real problems, was the real rocket fuel.  Alexander Graham Bell invented the telephone, but the switchboard was the innovation which connected people over great distances.  On it's own the phone did very little.  Morris Tanenbaum invented the silicon transistor, but the silicon wafer was the innovation which brought microprocessors to the masses.

Our American culture embraces innovation.  We like cool, new technologies which purport to make life better, even when they don't.  Although there are certainly pockets the world over, there is no better market for launching new products that challenge the status quo or establish entire new segments.  Our software businesses prove this on a daily basis.  Despite repeated efforts by large companies to move software development off-shore, the most innovative software is still largely developed in the United States for the US market.

So back to the question then, why are companies struggling with innovation?  I believe it's because we've added noise between each step of the invention to insight to innovation process by separating the functions on a cultural plane.  I've learned over the past thirty years not to underestimate the importance of culture.  If you want to sell on anything other than price, you have to innovate.  But to innovate, you need access to invention, and that access is much more than reading whitepapers and listening to lectures.  People need to share more than a language, they need to share cultural experiences.  How are we as leaders enabling cultural exchange to occur as part of our daily routine?  How are we growing ourselves by creating interactions with other cultures at work and at home?

We need to consciously choose to go against the grain; to recognize even when we speak the same words, we can mean two different things when cross the culture divide.  Now that my eyes are open, it's incumbent upon me to make the time to move forward.  I'm sure to many this is all very obvious, in which case although I'm admittedly late to the party, at least I'm on my way.

Disposable Software

For the past 20+ years we have been on a journey in application development; moving from large, monolithic stovepipes to decomposed, distributed services.  Along the way we've identified, tried and discarded more approaches than any one person can possibly remember.  Over time we evolved to a point where we differentiated between back-end enterprise service, such as billing or scheduling, and the user interface.  That's the point at which the whirlwind began and software development exploded along with the democratization of technology.  

We are now entering a new age where our success will drive us to ingest the last vestiges of "traditional" software development, and in the process make applications truly indispensable to business success.  We are entering the era of disposable software.

The days of building applications are over; we're done, or we'd better be very soon.  Business executives have lost their appetite for large software projects spanning multiple time zones with timelines measured in months and budgets measured in millions of dollars.  Emboldened by the stories of success, business software has advanced from a nice to have to a core requirement, from a solution you go somewhere to use to a solution you use wherever you are.  Software is now expected to work when needed, where needed, and be no more difficult or expensive to implement than the often repeated story of Flappy Birds.  

If you're not among those on the inside of this transformation, look to the mobile app world for inspiration.  Apps are built by leveraging pre-engineered services and frameworks.  Agile is the approach because it accommodates imperfect requirements and short windows of opportunity.  The leaders in this space have made software so invaluable it's become disposable.  First, enabled by cloud SaaS services, open source, and the ability to rapidly develop new solutions; business users can no longer be held captive by their software solution.  The pace of business and rate of change require solution owners to seek solutions which maximize agility, efficiency and elasticity.  Any software unable to meet these requirements will increasingly be disposed of, not updated.  Second, the line separating the apps we use in our personal lives and those at work is blurring rapidly. Just as the public is always on the lookout for newer, better, more innovative apps; so are business users.  

The reality of disposable software requires us to look differently at how we manage our development teams and budgets.  We need the back-end services plus several supporting services to be in place and accessible across a distributed infrastructure.  We need frameworks which minimize the time spent in foundation work so our end customer can see the majority of the value of their spend.  Finally, we need to rethink how we hire, train and incent our developers so they are focused on collaboration, communication and reuse rather than viewing software as their magnum opus.  Of course wrapping all of this together highlights the importance of DevOps; the glue which makes this approach work.

I know many experts bristle at my advice.  They liken modern software development to "hacking", and defend it as nothing more than shoddy software engineering.  These are the very people who will be waving from the side of the track as this train roars through their careers.

Where's the Business?

Despite all my attempts it appears cloud computing continues to be a technology topic, at least in the Fortune 1000. Over the past few years secrets of cloud, including Facebook's private cloud, Zynga's use of 12,500 Amazon EC2 instances, and NetFlix creation of their Chaos Monkey, have made big company executives ask their CIO "what are we doing with public cloud? What are we doing that we can tout in Fortune magazine or at our next board meeting. After 30+ years of being beaten into submission that IT is about avoiding risk, now CIO's are being asked how far outside the box they're thinking, how entrepreneurial they are.  I'd be amazed if none have broken down in heaving sobs or curled up on the floor with their thumb in their mouth.  The truth is, cloud doesn't start with the CIO, it ends at the CIO.

The preconceived notion is that cloud is all about TECHNOLOGY. Guess what, it's not! In reality each of those examples, and the ones CEO's and CFO's trot out to "motivate" their CIO's have nothing to do with technology. Rather, each has to do with the business. It's the needs of the business which drove the application of the technology, not vice versa. Therefore the right question is not CEO to CIO but CIO to the CEO: "Here is what cloud enables businesses to do differently. How can we take advantage of it?"

As clouds are being built, the enemy of efficiency, fiefdom, is following right along. With the number of business savvy CIO's in the Fortune 1000 I'm surprised at how few have engaged the business in a discussion outside of cost savings. Great, run applications and all at a lower cost. But that wasn't the goal of Facebook, Zynga, Twitter, or just about anyone other cloud based company.  Just look at NetFlix who single-handedly put Blockbuster out of business and forced the cable operators to take notice.

Cloud is a platform, a new business enabler for the 21st century.  It's about revenue generation,  It's about providing an on demand foundation to deliver technology where needed, when needed, in the most efficient manner possible.  How it happens isn't important.  The focus needs to be on WHY!

(I held on to this post for a year or two before publishing to validate it's applicability.  Then, all of a sudden, I started seeing a torrent of posts and articles on this very subject.  So then I held on to it for a few more months.  But finally I've come to my senses, so my apologies for helping to push the conversation earlier.)

Where is the Hybrid Cloud Adoption?

Since the early days of the enterprise cloud adoption discussion the concept of Hybrid Cloud, combining the control and security of the private cloud with the breadth and economics of the public cloud, has been a topic.  Cloudbursting emerged as one of the early, highly touted use cases for cloud, and I was one of those consultants adding to the hype.  Yet today with valid reasons for adopting Hybrid Cloud, where are the adopters?

I believe there are several reasons Hybrid Cloud has yet to take off including:

• Offerings - the offerings in the market fall well short of what companies need because none provide any orchestration, policy management or governance tools.  Buyers need a way to specify the environment blueprint to automate the provisioning at the provider.  The environment needs to be  configured precisely as needed in an automated fashion and this must include the ability to specify the location based on the location of other virtual machines and storage.  Policy management across providers, applications and data are necessary to ensure data is protected, business rules are enforced, and costs are effectively managed.  And without governance tools in place administration and change management become ad-hoc to a large degree.  There are tools available to help, but without strong provider integration the tools are merely best efforts.
• Critical Mass - there are companies who are using Hybrid Cloud successfully today, but they rarely talk about it.  Whether its to maintain a low profile to the public and shareholders in case the bet doesn't pay off, to protect competitive advantage, or simply because they don't feel others should benefit from their investment we have very few stories to learn from and retell.  Most of IT progress is predicated on following the leaders.  CRM, SFA, ERP, MDM, eCommerce, Web 2.0, Mobility, M2M, Analytics.  All of these technologies are pioneered by visionaries and derided by the status quo until a tipping point is reached after which the critical mass of thinking drives widespread adoption.  However in Hybrid Cloud the benefits of sharing the stories in the form of larger, more robust Public Clouds at lower costs points will benefit the early adopters.  Telling the stories will yield dividends.  There is very little competitive threat of the followers suddenly catching on and accelerating into a pioneer.
• Risk vs Reward - we know many CIO's have no interest in doing cloud let alone leveraging public clouds because they don't see the reward for taking the risk. Risk is nothing more than a euphemism for fear: fear of failure, fear of losing control, and fear of personal risk. Although all the experts say it's the only way to go and a fundamental part of transforming IT into a business services organization, cloud isn't easy.  And anything that isn't easy is rife with risk.  Therefore it remains easier to follow along at a snails pace talking up the desire to leverage Public Cloud resources in a hybrid model than actually take any steps.  Often these organizations are characterized by having a less than robust private cloud and have done no organized research into making a Hybrid Cloud reality. 
• Learning Curve - learning anything new takes time, and with an expansive domain such as cloud becoming an expert seems impossible.  Focusing on private cloud enables organizations to build skills related to cloud and move up the learning curve at reduced risk because there is no threat of losing control.  However Private Clouds provide a false sense of accomplishment because the end of their road is rarely much further than mass virtualization, well short of the value propositions of cloud.
• Business Value - the concept of cheaper, better, faster has made inroads at the virtual machine level, but few in the business understand how Hybrid Cloud can drive real business value and thus are allowing IT more leeway than necessary.  The last thing many IT leaders want is a business to expect the deployment of entire environments within minutes, scale up and scale down within minutes, and frankly deployment of entire new capabilities in minutes.  It makes IT look slow and incapable.  Slowly but surely IT executives are getting their arms wrapped around the idea that Hybrid Cloud is an enabler, but at the same time they have to gain comfort with giving up control.  

Just as cloud isn't easy, neither is transforming IT or being an IT executive at this point in time.  Control has been the hallmark risk mitigation technique of IT for 40+ years and is unlikely to change soon.  And why is this the case?  Because the business wanted it that way.  Consider it the unintended consequence.