2025 is going to be a big year for fintech. Many regulations, collaborations, and innovations that have emerged in 2024 will improve how fintech businesses engage with their customers in 2025. The market was already projected to be more than $608.35 billion by 2029. The valuation is only going to increase in the coming year. With all the changes in banking, payments, investment, and other financial sectors, the fintech space is up for substantial developments in 2025.

We are seeing businesses across industries engage with large-scale projects needing a talent pool experienced in AI, cloud, IoT, or other emerging technologies. However, the fluctuating nature of these projects requires a flexible workforce, which can only naturally comprise part-time hires. This is why we see a steady investment in the staff augmentation market. The 2023 evaluation for the IT staff augmentation market was approximately USD 300 billion in the recent market reports.

A software update failure led to one of the most infamous outages of the year: the Crowdstrike Outage. The incident is estimated to have cost Fortune 500 companies more than USD 5 billion. Per the congressional testimony by Crowdstrike’s officials, the downtime wasn’t attributed to a malicious attack but a configuration and software update failure. Incidents like these make us question how we deal with our digital ecosystems - the configurations, the monitoring, the testing, etc.

Business leaders are worried that despite their fervent enthusiasm for building innovative solutions, they haven’t been able to explore even half of the possibilities that digital has to offer. This anxiety has made CXOs desperate to associate with keywords like Generative AI, multi-cloud, SaaS, and more. However, accommodating these big technologies in a rush often comes at the cost of quality.

Working with teams limited to a geographical location, talent pool, and technology stack can undermine the possibilities of a business’s digital offerings. That is the reason services like software development outsourcing became popular. However, even with global access to talent and technology, outsourcing services must be more cohesive and often need more imagination for modern digital innovations. This is what set the stage for Global Capability Centers (GCCs).

When you’re a popular name in the payment gateway market, like Stripe, you need to maintain a reputation for intelligent payment processing as well as strict payment security. Customers expect proactive fraud prevention and compliance management features to be inherently included in your offerings. Failing to do so will harm your brand, and you might not even get two more strikes to reaffirm your presence.

Over the years, CI/CD has become such an integral part of DevOps that it is difficult to think of one exclusive of the other. However, DevOps’ much broader set of utilities might often undermine the role and scope of CI/CD. The vast DevOps toolchain for configuration management, IaC, monitoring, security, and other such use cases confines the CI/CD strategies to mere code handling.

‍ They started in awe, which soon turned into desperation to keep up, and it is only now that we have started realizing the utility and business value of our Artificial Intelligence (AI) goals. I like this stage of our industrial revolution, where we are no longer expecting magic from AI but are integrating it nevertheless for all the wonders it can still do for our businesses. This was the same space where our DevOps efforts started yielding enterprise-level transformations.

There is a sense of growing maturity in the DevOps market that is also reflected by its evolving tools. For the past some time, I’ve also seen these tools expanding beyond the limited utilities of continuous integration and continuous delivery. They are now seeping into database management, QA automation, and even AI integration in the SDLC. However, I see no deviation in their efficiency that enables teams to transition smoothly from ITOps to DevOps.

I often wonder about Steve Wozniak’s claim that the first computers were built to help “common people rise.” Correlating the evolution of software with the upheaval of our human society brings a very hopeful sentiment. Now, software engineering methodologies cater to more nuanced requirements of our “rising,” such as allowing space to correct mistakes (agility) and openness to feedback (adaptability).