The Perfect Storm

The software industry, particularly the digital /martech space, is engulfed in a perfect storm. Several macro trends are causing significant churn, and these challenges are not distant threats but urgent issues that demand immediate attention.

a) Innovation overload: The rapid influx of new marketing technology products into the market, as per Martec charts, has made it humanly impossible for executives such as CIO/CTO/CPO/CDO/CMO to stay current with most of this innovation. The number of new marketing technology products debuting into the market has grown at an average rate of over 1000 each year over the past decade. In the previous year alone, the pace has accelerated to over 3000. The pressure to adopt these new products is immense, underscoring the need for effective strategies to manage this overload.

b) Technology bloat: The average number of technologies on most customer facing web sites is already north of one hundred, indicating that technology bloat is already a severe problem for tech executives.

c) GenAI disruption: With every major release of GenAI, many AI startups become redundant. In fact, Sam Altman (of openAI) cautioned Harvard students embarking on entrepreneurial journeys not to focus their companies on peripheral use cases that can become a core part of the next version of a main GenAI engine. This complicates decision-making even further for the tech executive as the odds are stacked against any new technology/company they want to do business with. (i.e., 90%+ of startups don't eventually survive). So, is it safer for them to stick with the established choices supported by the analysts (Gartner, Forrester, IDC, etc.), sticking to the old adage "No CIO got into trouble for going with IBM?" But that would mean missing out on most new advancements and foregoing the early mover advantages.

d) Executive turnover: The average tenure of a C-suite technology exec is now down to around three years. Whenever a new exec takes over, they are expected to show results pretty rapidly, and they are also raring to make their imprint on the company. This opens a wave of new people, ideas, and technologies and sometimes throws out previous thinking and initiatives without allowing the time to mature and come to fruition. How many of you can relate to this scenario where the direction changes, and sometimes the investments made in the last few years just go down the drain because a new executive is now in charge?

So, what could be some strategies for the C-Suite to navigate this perfect storm of technology bloat, innovation overload, GenAI disruption, and executive turnover while maximizing ROES (Return On Engineering Spend)?

1. MACH , i.e. composable technologies provide one approach to mitigate this. They provide a framework to treat the IT landscape as a giant jigsaw puzzle where pieces can be combined or split easily and replaced by other pieces. This significantly reduces risk and also helps deliver results faster. Whenever there is a new proposal to try new software, whether it provides net new functionality or improves upon existing functionality, it can be piloted by directing a small percentage of the users /traffic and creating an AB test to validate the results and ROI & TCO assumptions. A MACH approach makes this relatively easy, and one example of how to pilot new technologies and eventually cutover is detailed here. Note that some legacy technologies might need additional pre-work and decoupling to enable this approach, but it is entirely possible.
   
2. To handle the pressure to deliver rapidly when a new executive takes charge, one approach that worked well is to Repair- Refactor - Replatform. Usually, the new executive is told that the existing system(s) is/are too old, brittle, and poorly designed, and re-platforming is the only solution. But seasoned execs double-click on that and buy some time before they bite the bullet and approve a major re-platforming, as usually, that takes much longer to deliver and causes much disruption. The way to buy time is to first identify the most pressing pain points and the key metrics that move the needle in terms of business outcomes. Usually, with some workarounds and or minor changes to business processes, those friction points can be "Repaired” to move the needle on the business outcomes and/or kill some pain. This has multiple benefits.
   
   a) the energies of the key SMEs on the team are laser-focused on improving the metric/ decreasing pain. The team demonstrates quick wins, usually within 90 days, which builds confidence and credibility for the team.
   
   b) This provides more time and context for the new exec to understand the ecosystem and underlying issues better. Process or data quality or people issues are often obfuscated and reported as platform or technology problems. Attempting a re-platforming without understanding these root causes would result in carrying over these issues to the new platform.
   
   The learnings from the "Repair" phase will provide inputs on which parts of the system or processes need to be "Refactored" for optimal gains. This would produce the next wave of business outcomes and/ or pain reduction. These outcomes further build confidence and credibility with stakeholders. By this time (usually 90-120 days), the new exec would have gained enough context and data to make an intelligent replatforming decision. This approach, which aligns with the MACH mindset, might explain the key findings of the recent Forrester Wave report which shows that a) there are no vendors in the leader's circle of e-commerce platforms and b) very few B2C digital businesses are shopping for a new e-commerce platform.
   
3. Another approach to reducing some of this churn is discouraging shadow IT, which is proliferating because it is easy for anyone to subscribe to a SaaS solution using their credit card and expense it. Encourage it to go through a trial process and a proper experimentation framework. Include the right stakeholders in the decision process with ROI justification that accounts for TCO. Negotiating a free trial period for the experimentation allows for a better understanding of the license, training, deployment, customization, testing, and cutover costs, as well as preparing the fit-gap analysis for the decision-makers. This approach would reduce some of the technology bloat. Again, the MACH mindset helps enable such an experimentation model to AB test parts of the ecosystem to evolve to an optimal tech stack.

Ultimately, enterprises today must strive to strike a 'goldilocks' balance between innovation, speed, and process rigor to maximize ROES (Return On Engineering Spend). This is not just a goal but a necessity to stay competitive and cost-effective in the face of these industry challenges.

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