2024 Prediction: The Demand for Tech Talent Heightens, GenAI Grows Up

A few months ago, McKinsey & Company published an article titled, 'Yes, you can measure software developer productivity', which created quite a buzz in the developer community.

Reaction to the article (some might say backlash) was swift. A quick Google search will render many examples of commentary from folks like Kent Beck, one of the 17 original signatories of the Agile Manifesto. Beck stated in a recent article, “We believe that introducing such a framework is wrong-headed and certain to backfire. The McKinsey framework will most likely do far more harm than good to organizations – and to the engineering culture at companies. Such damage could take years to undo.”  

Why such a strong reaction? It’s not that developers refuse to be held accountable for their work or what they produce. Rather, it’s more about the attempt to measure output in pure quantified metrics for a role that requires creative thinking and problem solving. Software development is not an assembly line producing widgets, and yet each of us in corporate functions must have our work measured and evaluated.

I choose to open my annual prediction by citing this article – and the reaction it caused – because the concept is applicable to what I see happening in broader terms as we pass into 2024, specifically as we see more automation tools being adopted across the enterprise, and as we see more AI functionality being released across all product types.

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Is automation helping or hurting developers?

Last year, in my first annual prediction blog, I talked about the demand for tech talent to become more competitive, despite recent waves of layoffs at some tech giants that had caused a pool of experienced folks to suddenly be available. I believe this heightened demand for specialized experts in specific tech disciplines remains high, with evolving dynamics further impacting the situation.

The first evolving dynamic is the balance between centralized control of technology and federated empowerment of those outside of IT and development teams to create change and deploy technology with the help of IT. On one hand, centralized IT is the old way of doing business. Gone are the days when all technology requests – big and small – were submitted to an internal team and put into a queue. That creates backlogs, and ignores the host of tech advancements that enable users to act, to get done what they need to get done.

The other side of this coin is governance and oversight. The more tech that’s deployed outside of IT’s control, the more risk of security problems and tech sprawl that becomes uncontrollable, often poorly planned and executed. Are the end users trained in understanding security and data privacy, and the data protection implications of their actions? And what about checks and balances on technical debt? Do departmental teams understand the implications of integrating their technology to central applications and data sources in terms of versioning, performance impacts and other implications? 

So, the balance lies somewhere in between. But that balance isn’t easy to strike. The pressure is on to transform, but also to continue to produce. Make no mistake, although we are empowering more teams, central IT is here to stay. But it doesn’t look or act like it did just a few years ago.

I see the excitement of the potential of GenAI tempered by the need to understand the consequences. I’m not talking about doomsday scenarios that are discussed in the media and elsewhere, I’m talking about practical matters like privacy and data protection implications for those entering personal or customer data into these tools. Also, I’m talking about the ROI of investments in AI – how much time and budget are you spending, and what are the planned outcomes?

Ultimately, the AI ​​winners – at least in the short term – will be those who choose GenAI use cases that extend what are already differentiators for their businesses and products. In fact, there are Several great AI-enabled software offerings that are being widely used by enterprises for use cases ranging from data science and predictive modeling to digital marketing and meeting note-taking.

The worst-case scenario is to look like a Luddite like those caught off guard by the emergence of ChatGPT a year ago. The best-case scenario is a strengthening of your offerings without any unintended negative consequences.

Here at Digibee, we’re focused on leveraging AI to continually improve the experience of developers and software engineers using our platform. These are the people who are most impacted by the tug-of-war between centralized IT control and a federated approach.

We are using AI to help empower all the developers to more easily build and maintain integrations, so the federation we empower is not across the entire organization, it’s across the skilled technology professionals who are qualified to be proficient, fast and responsible in this work. 

What is Digibee doing in 2024?

At the start of this blog, I mentioned the recent article that prompted so many individuals in the developer community to defend how they choose to work, and how they prefer to be judged. As Digibee strives to remain the developer’s choice for integration, we hear this loud and clear.

We’ve talked about developers being caught between the bottleneck of centralized technology management and the expansion of federated end-user technical skills. The Digibee Integration Platform is the kind of tool I described above – purpose-built to empower developers to abstract away the complexity and unnecessary mundane coding to focus on more important tasks. 

And this mission to empower developers is reinforced by practical applications of AI technology. To learn more about what we’re doing to make this happen, check out my last blog.

I highly encourage leaders of development and architecture teams to schedule a demo with Digibee and let us share our capabilities.