Introduction

Artificial intelligence (AI) technologies have caused a dramatic change in software engineering. At the forefront of this revolution are AI-Engineers – the professionals who implement solutions within the ‘Builders’ category of AI adoption, as I outlined in my previous post on Four Categories of AI Solutions. These engineers not only harness the power of AI but also redefine the landscapes of industries.

As I recently discussed in “AI-Engineers: Why People Skills Are Central to AI Success” organizations face a critical talent shortage in AI implementation. McKinsey’s research shows that 60% of companies cite talent shortages as a key risk in their AI adoption plans. This shortage makes understanding the AI Engineer role and its distinct skillset more crucial than ever.

But what is an AI-Engineer?

Core Skills of an AI Engineer

AI-Engineers are skilled software developers who can code in modern languages. They create the frameworks and software solutions that enable AI functionalities and make them work well with existing enterprise applications. While they need basic knowledge of machine learning and AI concepts, their primary focus differs from data engineers. Where data engineers mainly focus on writing and managing data models, AI-Engineers concentrate on building reliable, efficient, and scalable software solutions that integrate AI.

Strategic Business Outcomes

The role of AI-Engineers is crucial in translating technological advancements into strategic advantages. Their ability to navigate the complex landscape of AI tools and tailor solutions to specific business challenges underlines their unique role within the enterprise and software engineering specifically. By embedding AI into core processes, they help streamline operations and foster innovative product development.

Continuous Learning and Adaptability

As I described in “Keeping Up with GenAI: A Full-Time Job?” the AI landscape shifts at a dizzying pace. Just like Loki’s time-slipping adventures, AI-Engineers find themselves constantly jumping between new releases, frameworks, and capabilities – each innovation demanding immediate attention and evaluation.

For AI-Engineers, this isn’t just about staying informed – it’s about rapidly evaluating which technologies can deliver real value. The platforms and communities that facilitate this learning, such as Hugging Face, become essential resources. However, merely keeping up isn’t enough. AI-Engineers must develop a strategic approach to:

1. Evaluate new technologies against existing solutions
2. Assess potential business impact before investing time in adoption
3. Balance innovation with practical implementation
4. Maintain stable systems while incorporating new capabilities

Real-World Impact

The real value of AI-Engineers becomes clear when we look at concrete implementation data. In my recent analysis of GitHub Copilot usage at Carlsberg (“GitHub Copilot Probably Saves 50% of Time for Developers“), we found fascinating patterns in AI tool adoption. While developers and GitHub claim the tool saves 50% of development time, the actual metrics tell a more nuanced story:

• Copilot’s acceptance rate hovers around 20%, meaning developers typically use one-fifth of the suggested code
• Even with this selective usage, developers report significant time savings because reviewing and modifying AI suggestions is faster than writing code from scratch
• The tool generates substantial code volume, but AI-Engineers must carefully evaluate and adapt these suggestions

This real-world example highlights several key aspects of the AI-Engineer role:

1. Tool Evaluation: AI-Engineers must look beyond marketing claims to understand actual implementation impact
2. Integration Strategy: Success requires thoughtful integration of AI tools into existing development workflows
3. Metric Definition: AI-Engineers need to establish meaningful metrics for measuring AI tool effectiveness
4. Developer Experience: While pure efficiency gains may be hard to quantify, improvements in developer experience can be significant

These findings demonstrate why AI-Engineers need both technical expertise and practical judgment. They must balance the promise of AI automation with the reality of implementation, ensuring that AI tools enhance rather than complicate development processes.

Conclusion

AI Engineering is undeniably a distinct skill set, one that is becoming increasingly indispensable in AI transformation. As industries increasingly rely on AI to innovate and optimize, the demand for skilled AI Engineers who can both understand and shape this technology continues to grow. Their ability to navigate the rapid pace of change while delivering practical business value makes them essential to successful AI adoption. Most importantly, their role in critically evaluating and effectively implementing AI tools – as demonstrated by our Copilot metrics – shows why this specialized role is crucial for turning AI’s potential into real business value.

In my blog post “Four Categories of AI Solutions” (https://birkholm-buch.dk/2024/04/22/four-categories-of-ai-solutions), I outlined different approaches to building AI solutions, but it’s becoming increasingly clear that the decision on how to approach AI hinges on the talent and capabilities within the organization. As AI continues to evolve at lightning speed, companies everywhere are racing to adopt the latest innovations. Whether it’s generative AI, machine learning, or predictive analytics, organizations see AI as a strategic advantage. But as exciting as these technologies are, they come with a less glamorous reality—people skills are the make-or-break factor in achieving long-term AI success.

The Talent Crunch: A Major Barrier to AI Success

Reports from both McKinsey and Gartner consistently highlight a serious shortage of skilled AI talent. McKinsey’s latest research suggests that AI adoption has plateaued for many companies not due to a lack of use cases, but because they don’t have the talent required to execute their AI strategies effectively. 60% of companies cite talent shortages as a key risk in their AI adoption plans. (McKinsey State of AI 2023: https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai-in-2023-generative-ais-breakout-year).

This talent crunch means that organizations are finding it difficult to retain and attract professionals with the skills needed to develop, manage, and scale AI initiatives. 58% of organizations report significant gaps in AI talent, with 72% lacking machine learning engineering skills and 68% short on data science skills (McKinsey AI Tech Talent Survey: https://www.mckinsey.com/capabilities/quantumblack/our-insights/new-mckinsey-survey-reveals-the-ai-tech-talent-landscape).

The New Frontier of AI Engineering Talent

Given the rapid pace of change in AI tools and techniques, developing an internal team of AI Engineers is one of the most sustainable strategies for companies looking to stay competitive. AI Engineers are those with the expertise to design, build, and maintain custom AI solutions tailored to the specific needs of the organization.

By 2026, Gartner predicts that 30% of new AI systems will require specialized AI talent, making the need for AI Builders even more urgent (Gartner 2023 Hype Cycle for AI: https://www.gartner.com/en/newsroom/press-releases/2023-08-17-gartner-identifies-key-technology-trends-in-2023-hype-cycle-for-artificial-intelligence).

The Challenge of Retaining AI Talent

The retention challenge in AI isn’t just about compensation—it’s about providing meaningful work, opportunities for continuous learning, and a sense of ownership over projects. Diverse AI teams are essential for preventing biases in models and creating more robust, well-rounded AI systems. Offering inclusive, supportive environments where AI engineers can grow professionally and personally is essential to keeping top talent engaged (McKinsey AI Tech Talent Survey: https://www.mckinsey.com/capabilities/quantumblack/our-insights/new-mckinsey-survey-reveals-the-ai-tech-talent-landscape).

Skills Development: A Competitive Advantage

AI is only as good as the team behind it. As Gartner highlights, the true value of AI will be realized when companies can seamlessly integrate AI tools into their existing workflows. This integration requires not just the right tools but the right people to design, deploy, and optimize these solutions (Gartner 2023 Hype Cycle for AI: https://www.gartner.com/en/newsroom/press-releases/2023-08-17-gartner-identifies-key-technology-trends-in-2023-hype-cycle-for-artificial-intelligence).

Companies that prioritize skills development will have a competitive edge. AI is advancing so quickly that organizations can no longer rely solely on external vendors to provide off-the-shelf solutions. Building an internal team of AI Builders—engineers who are continually learning and improving their craft—is essential to staying ahead of the curve. Offering employees opportunities to reskill and upskill is no longer optional; it’s a necessity for retaining talent and remaining competitive in the AI-driven economy.

Looking Ahead: The Evolving Role of AI Engineers

As organizations continue to explore AI adoption, the need for specialized AI models is becoming more apparent. Gartner predicts that by 2027, over 50% of enterprises will deploy domain-specific Large Language Models (LLMs) tailored to either their industry or specific business functions (Gartner 2023 Hype Cycle for AI: https://www.gartner.com/en/newsroom/press-releases/2023-08-17-gartner-identifies-key-technology-trends-in-2023-hype-cycle-for-artificial-intelligence).

I believe the role of AI Engineers will continue to grow in importance and complexity. We might see new specializations emerge, such as AI ethics officers ensuring responsible AI use, or AI-human interaction designers creating seamless experiences. 

In my view, the most valuable AI Engineers of the future could be those who can not only master the technology but also understand its broader implications for business and society. They might need to navigate complex ethical considerations, adapt to rapidly changing regulatory landscapes, and bridge the gap between technical capabilities and business needs.

Conclusion: Investing in People is Investing in AI Success

The success of any AI initiative rests on the quality and adaptability of the people behind it. For organizations aiming to lead in the AI space, the focus should be on creating a workforce of AI Engineers—skilled professionals who can navigate the ever-changing landscape of AI technologies.

The lesson is clear: to succeed with AI, invest in your people first. The future of AI is not just about algorithms and data—it’s about the humans who will shape and guide its development and application.

As a technologist, it feels like I’m constantly time slipping — like Loki in Season 2 of his show. Every time I’ve finally wrapped my head around one groundbreaking AI technology, another one comes crashing in, pulling me out of my flow. Just like Loki’s painful and disorienting jumps between timelines, I’m yanked from one new release or framework to the next, barely catching my breath before being dropped into the middle of another innovation.

In the last week alone, we’ve had a flood of announcements that make it impossible to stand still for even a second. Here’s just a glimpse:

1. OpenAI Swarm (October 16, 2024)
   OpenAI dropped “Swarm,” an open-source framework for managing multiple autonomous AI agents—a move that could redefine how we approach collaborative AI and automation.
   https://github.com/openai/swarm
2. WorldCoin Rebrands as World (October 18, 2024)
   WorldCoin’s new Orb is yet another reminder that biometric and blockchain technology continues to merge in unpredictable ways. With its iris-scanning Orb, it’s trying to push a global financial identity system—a concept that sparks as much excitement as concern, especially around data privacy.
   https://www.theverge.com/2024/10/18/24273691/world-orb-sam-altman-iris-scan-crypto-token
3. Microsoft’s Copilot with Autonomous Agents (October 21, 2024)
   Microsoft’s second wave of Copilot introduces agents that can perform complex tasks autonomously, elevating Copilot from an assistant to a decision-making, workflow-automating powerhouse.
   https://www.microsoft.com/en-us/microsoft-copilot/blog/copilot-studio/unlocking-autonomous-agent-capabilities-with-microsoft-copilot-studio/
4. Anthropic’s New Models & ‘Computer Use’ Feature (October 22, 2024)
   Anthropic has introduced the latest Claude 3.5 models, including a new ‘computer use’ feature that allows AI to interact directly with applications. This marks a major shift toward AI being able to execute tasks like filling out forms and interacting with user interfaces, making it a significant leap in real-world functionality.
   https://www.anthropic.com/news/claude-3-5-sonnet
   • This seems same but different from https://www.openinterpreter.com/

The Data Speaks for Itself

These developments are not isolated. The latest Stanford AI Index Report 2024 provides some staggering numbers that put this onslaught of innovation into perspective:

• 149 foundation models were released in 2023, more than double the number from 2022, and a whopping 65.7%were open-source. That’s an overwhelming volume of tools, each requiring careful consideration for their potential applications.
• The number of AI-related publications has tripled since 2010, reaching over 240,000 by 2022. Staying on top of this research is an almost Sisyphean task, but these papers provide the groundwork for the rapid-fire advancements we’re seeing.
• Generative AI investments exploded to $25.2 billion in 2023, nearly eight times the previous year. This boom in funding is driving the constant stream of new AI tools and capabilities, each promising to reshape the landscape.
• AI was mentioned in 394 earnings calls across nearly 80% of Fortune 500 companies in 2023, an enormous jump from the 266 mentions in 2022. The sheer presence of AI in corporate strategy highlights how central this technology has become to every industry.

The Technologist’s Dilemma

The rapid pace of AI advancements presents an overwhelming challenge for technologists. Each new tool or framework isn’t just a minor update—it’s potentially transformative, requiring deep understanding and immediate adaptation. For those managing teams and implementing technological advancements, this fast-moving landscape demands constant learning and vigilance.

For more details and insights on the pace of AI developments, you can dive into the Stanford AI Index Report 2024 at this link:
https://aiindex.stanford.edu/wp-content/uploads/2024/05/HAI_AI-Index-Report-2024.pdf

Now I can go to sleep 😴