Why We Should Rethink AI, Cloud, Platform and Core Enterprise Technology Competency – 4 Trends and 3 Enablers

Amazon Cloud Service launched its S3 storage solution in 2006. How far back was 2006? The first iPhone was launched in 2007 and I felt lucky to get one. A 3.5-inch screen on the iPhone felt huge - that’s how far back 2006 is.

Fast forward to 2023, much of the industry still talks cloud with an infrastructure centric view. However, the early signs of new thinking are already on the horizon for strategic enterprise leaders to embrace for the next 3 to 5 years.

Here are the 4 trends and 3 enablers I see:

1. Generative AI has crossed the threshold for enterprise practical use

Strong AI has been a dream of computer scientists since the 1950s. The idea is that behind a curtain, a machine can exhibit intelligence indistinguishable from a human. If it is indistinguishable, does it really matter if the machine has consciousness, living or not? That is more a question for philosophers. For business, especially for the areas traditionally underserved by enterprise IT such as Marketing, Legal, Procurement, HR etc, the value of generative AI is undeniable. These business areas also generally value analytical intelligence over mathematical precision which remains a weak spot for Generative AI.

In the latest executive survey by CANAPI, a large VC firm, 26% of respondents have already started utilizing Generative AI, even though 66% felt the benefits are still hard to tell. The first two biggest challenges are, not surprisingly, data privacy and regulatory concerns.

For technology that’s in the early adopter stage, these are remarkable results in a short period of time. All these numbers represent a call to action for enterprise leaders to figure out a balanced approach for prudent and risk-weighted Generative AI adaption. The good news is we have done that many times before (e.g. introduction of mobile smartphones). Technology aside, business inherently deals with risk all the time. Every loan given is an educated risk taken in exchange for value generation for both the customer and the business.

2. Cloud is moving up in value, becoming more application and data centric, less infrastructure centric

Cloud has rapidly evolved to an economic model for delivering application and data services. The center of gravity for cloud has moved from infrastructure hosting to rapid business value generation through SaaS (Software as-a-Service) and PaaS (e.g. platform as-a-services). Instead of focusing on more virtual boxes to provision and scale, the center of action has pivoted to more agile and resilient business capabilities (e.g. rapid and compliant account opening).

In this context, data center migration should increasingly be focused on application modernization, leveraging cloud-native services through a composable architecture that business can relate to, with positive impacts customers can experience. While enterprise IT still needs to set ambitious goals to retire legacy data centers to fundamentally reduce operating footprint complexities, the strategic imperative should start with an application and data centric view to enable business value creation.

3. The rise of platform becoming “Super Apps” / “App Store” like with network effects

When I was a developer and later turned into a technical architect, I was hypersensitive to the uniformity of the layer-cake technical architecture to enable simpler integration. Platform to me back then was about technical architecture, tools and programming language. Today, enabled by the cloud, platform is about creating a “Super App” experience, where the platform essentially becomes the “App Store” for various applications to co-exist in an ecosystem for greater value through the network effect. Workday for example is no longer just HR, it is also a general ledger, and offer many other application capabilities through ecosystem partners. The same pattern can be seen with Saleforce, and modern web/mobile channel platforms.

4. Networking and identity management form the next wave of core technology competencies

The aforementioned composable architecture and “App Store” like platform notion rests on secure and reliable data exchange through infinitely scalable networks across multiple cloud network protocols, governed by robust identity management practices. In a legacy data center, computing and storage are the central focus. In the hybrid cloud architecture, network and identity are the security boundary and the physical place where orchestration takes place for a well-organized customer experience.

3 Enablers:

1. Simplification but not simplistic is a form of innovation

Simplification is valued more than ever before. Next to culture, complexity is the number one constraint to innovation and digital transformation. In the FinTech space, we increasingly see a merger of accounts into a single platform across lines of business and account types with attributes driving workflow and personalization. Similarly, banks are consolidating core banking systems, retiring old integration tools to simplify in order to accelerate innovation and change.

In my role as CTO, it is much easier to find people who can make things more complicated. A technologist with the ability to simplify without being simplistic is at least 10x more valuable for the enterprise.

2. Ecosystem to enable Build, Partner and Buy

I was chatting with the EVP of a PE backed firm on digital innovation recently. We both agreed that with the speed and scale of technology innovation, commercially, it makes little sense to build all in-house. In general, a digital leader needs to have an ecosystem view across build, partner and buy arrangements. In the context of this particular PE firm, we came away with the idea to build in the core competencies (e.g., UX), partner in scaling, and buy for “plug in/out” acceleration. The business value, sustainability and scalability for growth should increasingly inform the commercial model, not technology on its own.

3. Human leadership is becoming more valuable than ever

Much is feared of technology being a job killer. Machines are indeed great at what machines do. Humans however are also incredibly good at what humans do. The reality is humans are incredibly good at making judgment calls. Our sense of intuition and our consciousness enables self-examination and gives rise to infinite creativity. Humans also have a deep longing for connection and are incredibly good at making these connections.

As technology does more, humans will do more, explore more and dream more. In this sense, human leadership is becoming more valuable than ever.

I look forward to reading your perspectives and collaborating in learning in the comment section.