AI, as the first self-generative technology, is a radical break from the past. Never before has a technology been able to improve itself without human assistance.
Cloud computing, now the foundation of IT, offers an on-demand tool set that dwarfs previous generations. Most significant: it’s endlessly scalable.
While cloud and AI have separate challenges and distinct growth paths, their development is inextricably intertwined in ways that don’t get much attention. The two technologies are merging into a single entity. In many ways, they’ve already combined at a fundamental level.
For instance, the phenomenal AI chatbot ChatGPT relies on the compute power of its host cloud platform, Microsoft Azure. Without the cloud’s support, AI would be a mere gleam in a futurist’s eye.
Cloud, in turn, benefits greatly from AI. For example, AIOps is playing an essential role in cloud management — a role that will become more crucial over time.
Also see: Top Cloud Companies
Cloud and AI: Mountains of Money
The projected revenue for both the cloud computing and AI markets is nothing short of stunning.
Cloud market revenue was estimated to be $380 billion in 2021. With a compound annual growth rate (CAGR) of 17% between now and 2030, the cloud market is projected to hit a lofty $1.6 trillion by 2030.
AI boasts an even more remarkable trend line. Revenue for the AI market in 2021 was $136 billion. Growing at a fevered 38% CAGR, AI revenues are forecast to zoom up to $1.8 trillion by 2030.
It’s the combined revenue that’s the real stunner. Assuming the forecasts for 2030 are correct, add cloud’s $1.6 trillion to AI’s $1.8 trillion. The combined AI-Cloud market will be a jaw-dropping $3.4 trillion by the end of this decade.
Bottom line: Cloud and AI providers will be making mountains of money in the years ahead.
Bottom line: Cloud and AI providers will be making mountains of money in the years ahead.
Also see: Top AI Software
Cloud and AI: Big Promises (and Big Frustration)
Cloud Computing: Fast Start, Quick Headaches
Now that cloud is mainstream, its slow start is forgotten. Cloud as we know it debuted in 2006 with the launch of Amazon Web Services. Yet by 2012, a mere 12% of enterprises had applications in the cloud. By 2014, this had leapt to 69%. In this rapid growth spurt, established vendors were accused of “cloud washing,” the deceptive practice of calling tired legacy software as “cloud focused” to make it seem forward-looking.
Now in 2023, the war is over, and cloud has won. Multicloud adoption saturates business. But despite its fast rise, cloud produces no small frustration in enterprise leaders.
Many companies migrated to the cloud without planning — the COVID-19 pandemic rush was especially pell-mell. Because the cloud is still relatively new, and still rapidly evolving, there aren’t solid guidelines to guide companies.
The frustration around multicloud is acute. I hear from many executives: Challenges with juggling different providers and different tool sets are cause for big concern.
Cost is especially concerning. The cloud was originally sold as a cheaper alternative to the data center. But it has morphed into a more powerful and flexible — and sometimes far more expensive — alternative.
In frustration, some companies are repatriating their workloads; actually migrating back to the data center to save money. Cloud is great, but it’s not ideal for everything.
AI: From Turing Test to ‘Expensive Science Experiment’
In contrast to cloud, AI has been in development for more than 70 years. Alan Turing introduced his famed Turing Test in 1950, and the 1960s saw serious tinkering with early machine learning models. In 1997, IBM’s Deep Blue used AI to defeat world chess champion Gary Kasparov.
Yet even with AI’s long gestation, companies are struggling to fully harness its potential. A Deloitte report from October 2022 noted that “unfortunately, many organizations are struggling with middling results, despite increased deployment activity.”
Companies have had success with AI deployments, but there’s also been plenty of “expensive science experiments” — failed initiatives that were written off as learning experiences.
A few executives have told me quite frankly that AI is not ready for prime time. The Deloitte survey identified the top challenges in both starting and scaling projects:
- Insufficient funding for AI technologies and solutions (30%).
- Lack of technical skills (29%).
- Choosing the right AI technologies (29%).
More positively, Deloitte noted that:
- 79% of leaders reported full scale deployments of three or more types of AI, up from 62% a year earlier.
- 76% reported that AI investments will increase “somewhat/significant” in the year ahead.
AI’s core challenge is that many members of the C-suite don’t understand it. And that’s no surprise — AI is far more complex than earlier enterprise technologies, with its seemingly esoteric elements like deep learning, neural networks, and large language models. AI is widely viewed as akin to a magic potion; simply sprinkle it on and the software will sing and dance and boil an egg.
AI’s core challenge is that many members of the C-suite don’t understand it.
Most confusingly, companies shopping for an AI solution have no clear way to test it out and compare vendors’ offerings. Is one provider’s AI better or worse than another’s? It’s impossible to quantify like, say, a storage system. From a business buyer’s view, AI is a black box.
Also see: Best Machine Learning Platforms
Cloud and AI: The Symbiotic Relationship
Despite the contrasts between cloud and AI, a deeply symbiotic relationship combines them: Both technologies drive the growth of the other.
Cloud and AI are locked in a “virtuous circle” in which the growth of one necessarily drives the arc of the other. This is a mutually supporting upward spiral that evolves in multiple ways.
How Cloud Drives AI
Cloud AI Developer Services
The big drivers in this category are the top cloud hyperscalers that offer AI development platforms. AWS, Azure, Google Cloud, and other cloud leaders all sell what’s known as cloud AI developer services.
These cloud-based platforms offer a large and growing tool set to develop AI. Users log on and build their company’s AI using software development kits (SDKs), APIs, or applications. In some cases, users don’t even need expertise in data or AI to accomplish effective work.
Cloud-Based Prebuilt AI Tools
A huge cohort of software-as-a-service (SaaS) vendors offer AI tools. These cloud-based AI tools run the gamut of enterprise functions.
In particular, the emerging extended detection and response (XDR) technology in the cybersecurity market rests heavily on cloud-based AI. Another sector that leverages cloud-based AI is application monitoring and application observability. Data management and automation are also popular SaaS tools.
There are numerous low-code and no-code apps available in a SaaS format. Remarkably, these low-code tools enable nontechnical staff to create AI-assisted applications.
AI Vendors Leveraging Cloud
A large and growing handful of stand-alone AI vendors leverage their own cloud platforms to offer AI – a few of them are quite successful. Examples include H20.ai, which offers the H20 AI Cloud, and DataRobot, with its AI Cloud Platform.
These vendors compete with the cloud hyperscalers in the AI market. This market battle creates a big question about the future of AI: Which type of vendor will dominate the AI sector, the cloud hyperscalers or the cloud-based stand-alone AI vendors?
This market battle creates a big question about the future of AI: Which type of vendor will dominate the AI sector, the cloud hyperscalers or the cloud-based stand-alone AI vendors?
The smart money probably picks the hyperscalers: Customers already do business with them, and these deep-pocketed cloud players can buy most any smaller player.
On the other hand, the success of cloud-agnostic data businesses like Snowflake and Databricks suggests that customers value independence from the hulking hyperscalers. So perhaps the stand-alone AI vendors will win the AI sector in the end.
Or: the AI market is so lucrative (and technologically heterogenous) that there’s room for both categories of vendors. My forecast is that both types of AI vendors will not just survive but thrive in the years ahead.
How AI Drives Cloud
AIOps Provides Cloud Management
Still in its infancy in this role, AI is evolving into a core role in cloud management. This is an urgent need because multicloud environments are stunningly complex; companies often complain about the headaches of managing these complex technologies.
The emerging solution is called AIOps, or artificial intelligence to manage IT operations, of which cloud is the central element. AIOps assists in creating and monitoring the automation of multicloud.
Jim Gray, a computer scientist who won the Turing Prize in 1999, predicted an AI-managed cloud world. Gray foresaw what he called a “server in the sky” — in essence, today’s cloud. His goal was to “build a system used by millions of people and yet managed by a single part-time person.”
AIOps represents that vision of simplified cloud management – but multicloud won’t be managed by a single person in the foreseeable future.
AI Demand Builds Cloud Storage Market
The gargantuan amount of data storage required by AI requires the capacity of cloud storage. AI is always hungry for data; it devours data and asks for more. The scalability of the cloud enables this oceanic data storage. Need more storage? Just click a few buttons on your cloud control panel. A static data repository — yesterday’s data centers — could never support today’s AI growth.
AI’s need for ever-more storage will continue to boost cloud’s growth. As AI grows rapidly, cloud storage will spiral upward right along with it.
AI Enables a Vast Cloud-Based Tool Set
AI increases the functionality of the cloud by enabling cloud vendors to offer a cornucopia of AI-based tools. All the leading cloud players, including a big cohort of smaller SaaS vendors, offer a menu of AI-enhanced software.
Customers access this leading-edge tool set by logging on to their cloud provider of choice, making the cloud still more essential in the continuous race to stay competitive.
Also see: The History of Artificial Intelligence
How Will Cloud and AI Transform Business?
The true revolution in enterprise IT will be when these two powerful technologies work together to a greater extent. This process has only just begun.
Cloud, AI, and the Democratization of Tech
Arguably the biggest result of the cloud-AI combination will be a greater democratization of technology. No longer will powerful tech tools be accessible only to the most wealthy companies. Even a fledgling business, leveraging the cloud and AI-enhanced tools, will have significant market power without vast funding.
Cloud itself has always been a great democratizing force. By offering compute on a rental basis, it enables small businesses to compete with enterprises that have elaborate data centers.
AI adds a greater democratizing effect by providing tools that have a “multiplier effect.” For instance, AI-based automation and machine learning can do the work of many employees.
On the Other Hand: Cloud-AI Helps the Megacaps
While the cloud-AI mix enables the democratization of tech as mentioned above, there’s another side of this coin.
Building the most advanced Cloud-AI deployments is highly expensive. It requires an experienced, knowledgeable team that commands top salaries and a lengthy process of architecture and ongoing development.
But once built, this formidable platform enables a market-beating competitive advantage. The ability for the largest companies to leverage such a sophisticated tool set will exacerbate the gulf between the big players and their modestly funded competitors. In essence, the AI-cloud combination will enable the rich to get richer.
In essence, the AI-cloud combination will enable the rich to get richer.
The Future of Cloud and AI
As cloud and AI merge into a single entity, the future becomes harder to predict. The combined evolution of these two powerful technologies could produce a remarkable array of outcomes. Yet a few likely scenarios seem clear in the distance.
The Cloud-AI Skills Gap
This new world of AI-Cloud will require a vast legion of experts to continuously build and maintain. Many of these jobs will be lucrative and will require upper-level skills, often including college-level math and computer education.
Here the skills gap rears its ugly head. It’s an obstacle that has bedeviled IT for years and shows no sign of ceasing. The problem is twofold:
- Complexity: The IT industry has made a hockey-stick move toward complexity in the last three to five years. Cloud and AI have contributed, as have edge computing, cybersecurity, and fintech.
- Adoption: The adoption of cloud, AI, and related technologies have grown even as their complexity has grown. Businesses are realizing these technologies are more central to their strategy, and there’s a corresponding increase in investment.
In effect, the challenges of today’s IT are not only more complicated, there are more of them.
So the lack of skilled personnel may slow cloud-AI’s torrid pace of growth, but there will be no lack of well-paying job openings for the foreseeable future.
Supercloud and AI
On the horizon is the rise of supercloud, which is a management abstraction layer over multicloud. Some experts predict this management layer will eventually encompass all of enterprise IT. Supercloud will manage everything from in-house data centers to far-flung edge computing networks.
AI is the engine of supercloud. Managing tomorrow’s multicloud will be impossible without AI’s assistance with countless automation and management tasks.
Supercloud will, for instance, use AI to manage AWS, Azure, and Google Cloud as a single entity. Supercloud administrators will rely on AI for anomaly detection, predictive analytics, and overall performance monitoring.
AI vs. Cloud
Given AI’s leapfrogging growth rate, it’s probable that AI will shape the cloud far more than cloud shapes AI.
Cloud offers a multi-faceted foundation and a development cycle that supports ever more hyper-advanced functions. Yet AI is self-generative, as ChatGPT so aptly demonstrates. AI’s ability to iterate without human input means it will be technology’s single most important tool, even as it requires cloud’s support.
Extrapolating AI’s growth curve out 7 to 10 years, AI is on a path to radically reshape most all aspects of the enterprise, not to mention many aspects of human life. And cloud’s constant scalability will play an integral, intertwined role.
Also see: The Future of Artificial Intelligence