The Spark Fades Fast: Why IBM, ServiceNow, and Seagate Prove That Finding It Once Is Never Enough

Is Day 1 the hardest day for any organization? According to Jeff Bezos, it’s actually Day 2. Why? Because Day 1 is all about change. It’s your first steps. It’s something new. Meanwhile, Day 2 is where stagnation begins. Ideas get lost in decision-making loops. You don’t know what’s next as competitors stride on. In the end, the spark is gone.

Nowadays, many companies hit their Day 2 amid AI-led innovation. Many business leaders wonder: “What are we doing wrong?”

From my point of view, it’s not about what they’re doing wrong – but about what has changed in recent years. Because a lot has changed – and no, it’s nothing you have ever dealt with before.

The new normal isn’t here…yet

If you as a leader, feel that things are moving too fast – you aren’t wrong. Despite the constant future-driven positivity, this speed is not normal. This speed doesn’t equal quality.  Instead, it creates a chaotic environment, where traditional rules no longer apply, and every competitive advantage is seized immediately. We all remember when ChatGPT was the hottest topic. Then came Anthropic. Then DeepSeek. Then, ChatGPT again. Every shift affects stock markets, decision-making, and even the internet. As US and China keep competing, while Europe debates establishing regulations at the cost of slower innovation, we aren’t anywhere close to globally accepted AI use frameworks which would have normalized the landscape. However, right now enterprises can’t slow down right now. They need to move through the murky waters – because certainty is shaped by those who found their way through the uncertainty.

So, how can leaders deal with the great unknown?

Adapting to the change: lessons from tech giants

Although we’re yet to see what the real new normal is going to look like, it will certainly have new leaner models that are independent from service vendors. AI is responsible for this upcoming change because it lets enterprises fulfill their needs in-house. Due to this, we’re entering the “winner-takes-all” phase, where the cost of inaction is higher than the cost of experimentation. What can be done about that? Let’s take a look at the available routes illustrated by well-known companies.

IBM: A lesson in diversification

Once the default name in enterprise computing, IBM stagnated through the 2010s, weighed down by slow-moving business units and a lack of clear direction. But IBM’s leadership realized that no company survives by clinging to yesterday’s model. Instead, it refocused on three areas where it could deliver uniquely: hybrid architectures, enterprise-grade AI, and long-term exploration of quantum computing.

Though strategic diversification of its expertise areas, IBM was able to strengthen its positions, evolving from a company surviving off legacy services into one leading future-facing technologies.

ServiceNow: Survival through AI guidance

ServiceNow reached a point where incremental growth wasn’t enough. To stay relevant in an AI‑first world, it needed to grow beyond workflow automation. Its concept of an AI Control Tower was a recognition that as companies adopt AI routines and models, someone must orchestrate all of it. ServiceNow positioned itself as the system of record and action that ensures AI behaves consistently, securely, and intelligently across the enterprise. By absorbing companies like Armis, ServiceNow strengthened its ability to secure sprawling digital ecosystems. Rather than chasing shiny features, it was building the AI proliferation platform that companies will rely on.

Western Digital and Seagate: See opportunity, not obstacles

Hard‑drive manufacturers Western Digital and Seagate both posted revenue increases of roughly 30% in their most recent quarters, a level of growth that would have been unimaginable only a few years ago.

They have AI’s insatiable demand for data storage and rising prices for high‑capacity drives to thank for that.  Western Digital reported shipping 190 exabytes of storage in its latest quarter, a 32% jump year over year, while Seagate saw an even steeper increase, shipping 45% more exabytes over the same period—evidence that AI’s footprint is reshaping even the industry’s most established hardware segments.

The bottom line

It’s not enough to find your spark just once. You have to do it again and again, as many times as it takes. The intensity of competition demands no less. Therefore, you must make sure your business model and leadership approach go beyond one-time effort.

I can share several key practices:

Normalize experimentation

Experiments are scary, yet necessary. As a leader, you must shift your business model towards rapid idea validation – and be open to suggestions from your team. Initiatives should be rewarded and encouraged and fear of risks should be addressed instead of blindly leaned into.

Encourage developer-to-innovator communication

Team employees aren’t the only ones apprehensive of new things. Many good projects can be staggered or even put on hold by resistance from development stakeholders who weren’t explained about the new imitative or technology. This is the development you, as a leader, want to avoid. Make sure your innovators have a robust platform for communication and collaboration – and that they have supporters and evangelists in every department. At the end of the day, innovation is a team effort – everyone has to be on board.

Reduce routines, grow people

Many companies are quick to cut their stuff upon facing change or embracing AI technology – for the purpose of growth and resource optimization. However, this a band-aid, not a long-term investment. People are your main power. Their ideas change operations and spark discovery.