If you’ve been in software development long enough, you know the industry has a history of evolving technologies and shifting skill demands. Just think back to the rise of cloud computing, or even the shift toward mobile-first development.

Nothing has sparked quite as much transformation—or concern—as AI. As it weaves its way into our workflows, AI impacts not just the “how”, but the “why” of software engineering.

AI is redefining roles and expanding what developers are expected to know, from advanced frameworks to new concepts like natural-language prompt engineering and retrieval-augmented generation (RAG).

According to a recent analysis from Gartner, up to 80% of software engineers could find their roles at risk if they don’t actively upskill in these and other related areas. Yet the goal here isn’t to replace developers. Instead, it’s to create fresh opportunities for existing workers and unlock their creative potential by focusing on developing talent for the longrun.

According to one popular YouTuber:

According to the article she mentions, they highlight three key periods of the coming AI wave:

• In the short term, AI will continue to augment developer workflows and provide modest boosts to productivity, mainly impacting senior developers within mature organizational development teams and workflows.
• In the medium term, the advent of AI agents could push boundaries and transform the developer workflow.
• In the long term, significant advances in AI will break new ground and mark the rise of AI engineering roles and still-yet-unforeseen skills and job titles.

According to Philip Walsh, a senior analyst at Gartner:

“To support AI engineers, organizations will need to invest in AI developer platforms. AI developer platforms will help organizations build AI capabilities more efficiently and integrate AI into enterprise solutions at scale. This investment will require organizations to upskill data engineering and platform engineering teams to adopt tools and processes that drive continuous integration and development for AI artifacts,”

In this article, we’ll explore what it means to upskill for the new software developer age. We’ll also discuss how AI is prompting companies to prioritize upskilling and reskilling their current employees over hiring new talent. Whether you’re a seasoned developer or looking to understand the trajectory of software development, understanding the next wave of skills on the horizon will be crucial for keeping pace in an AI-integrated world.

Shifting Demands for Developers

In recent years, the rise of generative AI has transformed industries at every level, far beyond the simple automation of routine tasks. While many predicted that AI would simply make things faster, the reality is actually much more nuanced and impactful: AI is changing the very structure of the software development industry, influencing both day-to-day coding processes and broader workforce needs.

For software engineers, this transformation brings a new wave of skills to master—skills that now require a blend of technical prowess, adaptability, and creativity to keep up with the pace of innovation.

The need to integrate AI into their work has marked a shift from simple coding expertise to a more interdisciplinary approach. Where once a solid grounding in languages like Java or Python could sustain a long career, today’s engineers are challenged to understand and interact with AI systems, from training data sets to crafting precise natural-language prompts.

In an era where job titles like “prompt engineer” are emerging, it’s clear that AI proficiency isn’t just a nice-to-have—it’s essential for a sustainable career.

According to one tech observer, there is a shift happening that points to a more dynamic approach rather than a rigid, classical one:

The pace of this change is pretty profound. Skills seem to be disappearing and created faster than ever before.

This new reality challenges traditional career paths and demands developers learn on the go, often as technologies are still being defined. As mentioned in the above quote, rigidity seems to be a key obstacle everyone in the industry may need to work with.

For junior developers, upskilling and reskilling can mean the difference between an accelerated career trajectory and struggling to make a living. For more senior engineers, it involves rethinking established practices and being open to shifting the skills and workflows needed to continue to offer value to your organization.

This also means that organizations may need to adapt to the rapid pace of skill turnover. Historically, it has been common practice for companies to spend more money on hiring new people and to continually cycle through workers to bring in new skills and perspectives.

Now, with new skills and approaches coming in faster than ever before, this approach may not be cost-effective or practical anymore.

Skills are just changing too quickly these days.

As we’ve mentioned a few times in our posts, the half-life of skills in many tech fields is now only two and a half years. Leaders see that it's not sustainable to bring on new talent and replace workers every 2-3 years, so upskilling and a more long-term approach to employee retention is quickly becoming the new normal.

Not only is it more affordable, but studies are showing that it leads to a more productive, innovative, and cost-effective workforce.

Treating your workers well and giving them a path to success seems like a no-brainer - but implementation of this vision is harder than it may seem. (more on this later on!)

To get a more tangible idea of what these new “skills” and workflows include, let's take a look at prompt engineering and the repercussions and mixed opinions on this new developer role.

According to a recent UNC Executive Development poll, 58% of respondents use ChatGPT in the workplace. However, while the majority report using this tool, it is unclear how many are using it well. A report by the World Economic Forum also found that “many leaders and employees aren’t clear on when generative AI should substitute for human decision-making and when AI should merely augment it.”

New Skillsets on the Horizon for Developers

Prompt engineering involves crafting effective prompts to guide AI systems, demonstrating a shift from traditional one-dimensional technical roles to positions that bridge human creativity, coding, and contextual awareness of the problem attempting to be tackled.

But what exactly is a “prompt engineer”?

According to organizational developer and design thinker Thomas Dettling, prompt engineers are a new breed of worker in the tech industry:

This new type of worker is not someone who simply excels in coding and building software, but who is also experienced in the many different factors that influence the “why” of software development and the proper execution of AI tasks and integration.

Some developers are not quite sold on these new skills, however:

Regardless of these opinions on prompt engineering, it looks like it is here to stay - and prominent senior developers who hold impressive roles in the industry aren’t quite on the same page as these guys.

As it turns out, the effectiveness of AI is all in the context and the prompting.

According to Connor Shorten, a research scientist and generative AI expert, he explains some of the key findings for effective prompting:

So, telling an AI to “think step-by-step” actually helps to improve its accuracy?

He goes on:

AI responding to telling it to take a deep breath? And Star Trek references helping it to improve mathematical reasoning?

In the paper they are referencing, “The Unreasonable Effectiveness of Eccentric Automatic Prompts”, the authors tell us,

“​​Large Language Models (LLMs) have demonstrated remarkable problem-solving and basic mathematics abilities. However, their efficacy is highly contingent on the formulation of the prompt…in most instances, the inclusion of "positive thinking" prompts positively affected model performance….additionally, our findings reveal that the highest-scoring prompt exhibits a degree of peculiarity far beyond expectations.”

As we can see, prompt engineering requires some outside-the-box thinking and unique skills apart from simply being able to code. It may also include an understanding of psychological principles, the context of the application, and the nature of LLMs themselves.

So far as we can tell, prompt engineering is still very new, and while there are some very interesting ideas surrounding it, it still appears to be ripe for new approaches and innovations.

Putting this all together, we can see that the advent of AI is creating some interesting, and quite unpredictable, new job titles and use cases. These new jobs that are emerging may require a new set of skills beyond simply understanding how to code.

Not only that, but it will also require leaders at the organizational level to understand how to create and facilitate a workforce that can adapt to these developments and their inevitable effects in real-time.

As Dr. Rijmenam, an innovation expert and strategic futurist, tells us:

To Upskill, or Rehire?

So, how are companies gearing up to keep their teams AI-ready?

As we know, AI isn’t just changing the tools we use; it’s reshaping how tech teams grow, learn, and collaborate. Organizations have a massive role to play here—not only by investing in upskilling and reskilling in key areas, but by building a culture that embraces change and empowers everyone to work alongside these rapid changes.

We’re seeing companies ride this AI wave in a way that’s shaking up how they think about hiring and talent. As we mentioned, instead of always looking for fresh faces, a lot of companies are shifting to focus on upskilling and reskilling the people they already have.

Why? In a tech landscape that’s changing faster than ever, the folks who already know the ropes—and have shown they can adapt—might just be their best bet.

In fact, a recent article in the Harvard Business Review pointed out that organizations investing in skills development and adaptability see better returns on talent than those trying to hire every time a new tech trend rolls around.

“The future of work will not be determined by technology, but by creating the right mix of education, exposure, and experience needed to develop skills and put them to work, creating a vastly more productive workplace and economy.”

Take it from McKinsey, who also found that companies with robust upskilling programs are more likely to outperform competitors on innovation and employee retention. This same article also tells us that by actively upskilling their workforce, companies can stay ahead of industry trends and readily adapt to changing market demands.

Leading companies are not just looking for coding chops anymore, but hunting for people who bring flexibility to the table. People who can learn fast, adapt to new tools, and even jump into new roles like prompt engineering if needed. Companies are investing more in programs that keep their talent versatile, adding courses on AI fundamentals, model interpretation, and even behavioral science.

When you think about it, these changes seem to be about building an “adaptability muscle.”

People with this skill are way more valuable than those with just the latest buzzword on their résumé. Gartner actually predicts that by 2026, over 50% of tech roles will require specific AI-related skills that aren’t mainstream today, meaning long-term retention is only getting more critical.

One post on X I saw recently sums it up quite nicely:

Interesting, right?

As AI keeps evolving, companies know they’ll need to keep up—or risk falling behind. This means that for anyone looking to stay relevant, the key might not just be about becoming or finding the better coder, but about being ready and open to pick up on what’s next.

The Role of Leaders in Supporting AI Skills Development: The Employee Experience

If companies want to stay competitive during this transition, taking AI skills development seriously may be an important mindset to adopt. We’re seeing more tech leaders recognize that supporting continuous learning is key to navigating this AI-driven future.

One way they’re doing this is by creating training programs and courses that help developers and engineers build confidence with AI-specific skills. Whether it’s working with new AI algorithms, learning ethical AI practices, or understanding the fundamentals of natural language processing, these programs are designed to fill gaps in knowledge and keep everyone up to date with the latest trends.

Now, supporting skills development is one thing, but creating a culture where AI and traditional development can work seamlessly together is another.

Companies are realizing that AI isn’t a separate lane; it’s part of a bigger picture that requires everyone to collaborate. That’s why we’re seeing more organizations encouraging traditional developers to work closely with emerging roles like prompt engineers and data scientists. This cross-pollination of skills is helping teams to blend AI knowledge with traditional software engineering, ultimately building better, smarter products.

To make this happen, companies are shifting their approach to collaboration. Leaders who actively participate in AI skills training themselves send a powerful message to their teams. When leadership invests time in upskilling, it signals that AI is not just a passing trend but a lasting priority.

This shared experience can strengthen team cohesion, making AI adoption a collective effort rather than an isolated task. It encourages a growth mindset and positions leaders as credible advocates who genuinely understand the learning process, thus elevating both individual confidence and the overall employee experience in embracing AI.

A great example of this can be seen with Microsoft’s shift from a “know it all” to a “learn it all” ethos, incorporating “open learning days, informal social learning opportunities, learning data for internal career paths, and new platforms and products for its partner network.”

Effective reskilling and upskilling calls for a blended learning model that goes beyond traditional methods. To truly empower employees for rapid changes in technology and roles, some of the following nontraditional methods could play an essential role:

Enhanced peer coaching allows employees to learn directly from one another, fostering a sense of shared expertise and creating informal mentorship opportunities. Learning networks extend this peer-based approach by connecting employees across different departments and even organizations, enabling cross-functional knowledge-sharing that can break down silos.

The “mass personalization of change” addresses individual learning needs by using AI or data-driven methods to deliver specific learning resources to each employee based on their unique skills, roles, and career goals.

Nudging techniques are subtle, behavioral prompts embedded in the work environment that remind or encourage employees to apply new skills or behaviors, helping to reinforce learning in real-time. This might look like sending reminders to practice newly acquired skills or celebrating milestones that encourage continued engagement with the material.

Altogether, this blended approach enables a continuous, adaptive learning experience, ensuring employees aren’t just equipped with new skills but are also supported in integrating these into their day-to-day work effectively. This model of learning recognizes that skill growth today requires both structured education and real-time, on-the-job adaptability.

This will also make it important for leaders to differentiate between upskilling and reskilling.

Upskilling focuses on advancing employees' existing skills, allowing them to deepen their expertise and remain competitive in their roles. This approach is particularly valuable in software development, where employees may benefit from mastering new frameworks, tools, or specific areas like AI or machine learning to stay relevant.

Reskilling prepares employees to transition into new roles altogether, especially when an industry undergoes major transformations. For instance, as AI integrates more into business functions, companies may need to reskill developers or support staff for roles such as data analysis, prompt engineering, retrieval augmented generation (RAG), or AI ethics consulting. This approach not only bolsters internal workforces but also offers employees new career pathways without seeking external hires.

In a recent Stack Overflow article, they emphasize that by balancing both upskilling and reskilling programs, companies can create a learning culture where employees feel invested in and valued.

A balanced focus on both approaches allows companies to retain talent over the long term, as employees have the tools to evolve alongside their roles. Additionally, organizations with reskilling programs tend to see greater innovation, as employees bring interdisciplinary skills that fuel creativity and problem-solving.

“Creating a culture where developers can upskill and reskill at work gives them a fulfilling, growth-fueled path while also ensuring that your company is ready for whatever the future may hold.”

Overall, a culture that prioritizes both upskilling and reskilling gives companies the flexibility to shift employees to where they’re most needed, creating a more resilient workforce ready for whatever comes next. You can explore more in Stack Overflow’s full discussion on this strategy here.

Employees aren’t the only ones who will need to be upskilled, though, as HR departments themselves will need to improve their knowledge and modernity in order to be able to achieve engagement in these initiatives.

HR departments need to be well-versed in new tools and workflows to effectively support employees and instill confidence. According to Ciara Harrington,the CPO at Skillsoft, when HR understands and champions tools like Copilot, they can demonstrate how AI enhances productivity rather than creating ambiguity. HR’s expertise becomes a stabilizing factor, allowing employees to focus on the benefits rather than the complexities of new technology.

“Leaders typically think of employees in two buckets: technical and non-technical. But going forward, every employee, especially managers and leaders, will need to be technical, too.”

She adds:

“Every people leader now needs to be somewhat of a technology leader. They don’t need to be as in-depth, maybe, as the programmers, but they do still need some education and training on how technology can be used to improve and drive efficiencies for your job and for your team,”

Harrington’s insight highlights that leaders need to go beyond simply providing tools; they must actively demonstrate adaptability and continuous learning and encourage idea-sharing within teams. Upskilled HR departments can foster a supportive learning culture by inviting employees to explore and share innovative uses of technology, creating excitement around new capabilities and collaboration.

This proactive approach ensures that employees aren’t just learning in isolation but are part of a shared journey toward AI fluency, supported by HR as a knowledgeable partner. This results in a resilient, forward-thinking organization where employees feel empowered to adapt, innovate, and grow. For more on this, you can check the full article on HR Brew here.

Wrapping this up, as tech leaders invest in these changes, they’re not just future-proofing their companies—they’re building teams that are more resilient, collaborative, and innovative.

It seems that the future of work in software development, quite simply, is uncertain.

If leaders are going to keep up, they will need to consider how they can embrace change and be ready when they wake up one day and things are completely different. This involves searching for talent to keep on board for a significant portion of their careers, rather than creating a job-hopping industry where employees aren’t valued quite as much.

So, creating an AI-friendly, fluid work culture and supporting continuous upskilling might just be the foundation of the future. We’d love to hear your thoughts!!

Here’s a helpful graphic from McKinsey on some of the key priorities in this new era for organizational leaders to consider: