The tug-of-war between optimization and innovation in the CIO’s office

It’s a budget planning meeting, which always seems to fall on a Monday. This year there is $30 million to spend on IT projects beyond operational costs. In other words, net-new discretionary spending needs to be invested in improving the delivery of IT services.

The list of things that need to be built and deployed or fixed and redeployed is usually well understood. A trade-off is emerging between optimization and innovation in cloud systems.

Defining the question

According to this study, the idea of innovation versus optimization is something that CIOs and IT leaders must deal with in 2023. CIOs are nearly twice as likely to invest in innovation and new technologies in an economic downturn instead of optimizing their current tech stack. They ranked AI enablement as an urgent priority. However, 83% are concerned that they will not have sufficient resources to invest effectively in innovation. This is perhaps because they have no idea of how much this new tech will cost and how many resources will be removed from cloud optimization projects.

IT optimization primarily focuses on making incremental improvements to existing systems and processes to make them more efficient and valuable. This often involves fine-tuning systems, automating tasks, and consolidating resources to reduce costs and increase productivity. This goes right to the bottom line since IT optimization delivers immediate benefits and is crucial for maintaining a competitive edge.

However, the downside of prioritizing optimization is the risk of overlooking opportunities for innovation that could have long-term impacts on the organization’s growth and relevance. Think game-changing new systems, such as AI, that increase supply chain efficiency, or automating steps in manufacturing that speeds up productivity and reduces costs at the same time. Usually, the value of a business is directly defined by the innovations that can drive it. Think about the services we use now, from food delivery to home sharing, with the draw being better customer experiences through innovation.

Emphasizing innovation enables companies to stay ahead of the curve, attracting customers with cutting-edge products and services. Yet, investing too much in innovation without optimizing existing systems can lead to inefficiencies and financial strain, thereby hindering the organization’s ability to deliver on its promises.

Limited resources

I don’t care if your company is 10 or 100 years old, the name of the game is figuring out where to allocate finite resources (money). IT is given a percentage of the overall investment in the industry, so its success will be determined by how they leverage that money strategically to return the most value to the business.

A few things to consider:

First, optimizing systems, cloud or not, means fixing mistakes made in the past, usually the accumulation of technical debt caused by bad decisions, such as migrating to a platform without optimizing the systems on that platform.

For example, take lifting-and-shifting applications to the public cloud without optimizing for that platform. That turns into unexpectedly big cloud bills, driving many companies to fix issues with the applications and data running on clouds and a few companies to move back to the data center. Both paths cost a great deal of money and return little or no value to the business.

These mistakes will kill a company. Taking resources away from innovation and spending them on making things work as they should removes business value. I think we’re going to see a great many businesses spend so much money to fix past mistakes that they’ll end up throwing in the towel. They will likely be disrupted by companies that are making better decisions and investing in innovation.

Second, are you investing in the right innovations? Of course, artificial intelligence is all the rage right now, so the momentum seems to be in AI-enabling core business systems.

However, I suspect that some of this investment won’t pay off the way many believe; AI is being misapplied in systems. For example, bolting a generative AI system on an inventory control application won’t really provide any useful function. This becomes more technical debt and another item on a list of things to be fixed.

The trade-off is real

I’ve given you two core questions about optimization versus innovation. Most CIOs and IT leaders don’t yet understand that this trade-off exists and instead are doing tactical planning. They are adding whatever the consensus believes should be added to the investment list without a clear understanding of how it will return value to the business.

Isn’t that how we got in trouble in the first place?