Artificial intelligence is transforming the modern enterprise, and nowhere is that change more visible than in how companies hire, train, and evolve their workforces. AI is no longer just the domain of engineers and data scientists. AI solutions bring immense change with their technology, which in turn is redefining every job role, reshaping processes, and introducing entirely new career paths.
We sat down with Don Howren, COO of Beyond Limits, to get his take on how the top AI SaaS providers like Beyond Limits are navigating this talent transformation themselves, and what that means for C-level leaders and professionals in any organization and industry as they prepare for a new, quickly evolving AI workforce.
AI SaaS providers are not just improving individual tools and applications; they're helping customers by reshaping how they work and deliver value. By blending human creativity with AI, machine intelligence, and Generative AI, organizations have the ability to be more adaptive, efficient, and data-driven than ever before. What’s really interesting is this isn’t just another technology platform shift like we’ve experienced in the past, because it’s not just happening in the IT and development functions. It’s permeating organizations at all levels and changing how we think about business.
One of the most recognizable examples of this shift is how AI helps streamline repetitive tasks like data entry, onboarding new customers, or processing invoices. By automating these time-consuming activities, companies are able to free up employees to focus on more valuable, creative, and strategic work that drives the business forward. At Beyond Limits, one area we’ve seen Generative AI drive new efficiencies is in finance operations. For example, ingesting large volumes of PO, AP or other structured documents using AI & ML to perform complex matching, categorize and curate, and automate workflows without human intervention.
But it’s not just happening in finance – HR teams are also leveraging AI to improve recruiting and evaluations. Tasks like resume screening, shortlisting candidates, generating job descriptions and interview questions, and even automating outreach messages, are streamlining hiring and supporting better decision-making. Likewise, customer support is evolving. AI is now handling Level 1 tasks like FAQ resolution, ticket triaging, and even basic chatbot or voicebot interactions in call centers, freeing up human agents to focus on more complex issues.
Of course, Generative AI tools like ChatGPT are used widely to improve individual productivity for content creation, code assistance, and communication. Collaboration tools now include AI-powered summarization, translations, and meeting transcriptions that collapse time-to-value across teams.
So we’re definitely seeing how AI has the ability to positively impact all aspects of an organization. But let’s be clear, any digital transformation and AI journey towards full automation must have guardrails. A focus on a governance framework that is both explainable and auditable to ensure trust, transparency, and compliance is essential.
No doubt this is a glass full/empty discussion. What we’re seeing with our customers and the market in general is that while some tasks are being automated, we’re not seeing jobs eliminated wholesale. And we don’t expect to per se, but every role moving forward must incorporate AI capabilities into their daily tasks to remain relevant.
For instance, in marketing, AI is now streamlining email campaign creation, writing blog posts with SEO optimization, and even creating ad copy and visuals using multimodal tools. Skilled marketers remain necessary to manage the process, ensuring that a company’s branding, strategy, messaging and value proposition remain a primary focus. Similarly, IT helpdesk tasks like password resets, device provisioning, and basic troubleshooting are being handled by AI-powered tools and RPA scripts, freeing up support teams to focus on more complex issues.
And new roles are also emerging. Prompt engineer was the hot new job for AI SaaS companies 2 years ago, but as AI models get better at understanding us, and as more employees learn how to use them, there’s less need for a dedicated prompt specialist. The Wall Street Journal, Fast Company and Techspot have all recently reported that prompt engineering, once hype, is “on its way out” because models have become smarter, and more people are trained on prompting. In other words, prompting is becoming a common skill rather than a unique job.
That said, the original objective of creating the prompt engineer role, which was to understand how to communicate with AI, is still critical. It’s just now embedded into many jobs throughout every organization. New roles are focusing elsewhere, like AI model trainers or AI platform engineers. And interestingly, some previously niche roles are gaining prominence. For example, data engineers who can prepare the necessary massive datasets for AI, or ML engineers who deploy models at scale are becoming a hot job right now. Even the software developer title is morphing; companies are hiring AI software developers or engineers who might spend their day building APIs for AI services or fine-tuning models.
The key is adaptability: roles will continue to evolve as AI technology evolves. One day you’re a software engineer, next you’re an AI engineer guiding an algorithm. And that’s a trend we expect to accelerate.
Clearly, data scientists and ML engineers are critical, but we also need AI Developers and Technical Solution Architects who combine industry-specific domain knowledge with AI expertise. The magic happens when you marry those two. It’s not just about having strong AI talent. It’s about applying AI where it can make a measurable business impact.
It’s changed tons. Being successful isn’t just about writing code or having a background in data science. According to Gartner, 30% of ML projects fail because of insufficient data or a lack of explainability. Today’s AI developers need access to quality, centralized data. This is tough, since most enterprises have over 175 different content/data sources! Also, strong collaboration skills to work across business and technical lines is important. AI cannot be fully utilized without interactions with others throughout the company, to understand better where AI projects can be successful.
And last but not least is the ability to incorporate subject matter expertise (SME) into their business outcomes. We’ve all had those situations when something goes wrong and we say, “ask Joe, he knows this system better than anyone.” Well, what happens when Joe leaves or retires? SMEs retiring or leaving a company often leave a big data hole so finding ways to capture that institutional knowledge for use with AI is a necessity. And once you get the model right, deploying and maintaining it in real-time is the next step. That’s why AI engineers today need end-to-end thinking: data prep, model development, integration, and governance. Success no longer depends on technical brilliance; it now requires cross-functional skills and strategic insight.
We’re also seeing big shifts in how engineering roles are evolving, especially in non-tech sectors like manufacturing, retail, and energy. With generative AI and low-code tools, engineers now spend less time writing boilerplate code and more time designing systems, integrating platforms, and validating AI-generated outputs. This opens the door for a broader range of professionals, like process engineers or analysts, to build functional tools and automations, without needing a computer science degree.
That shift has sparked a rise in "citizen development," where business users leverage tools like Microsoft Copilot and ChatGPT Plugins to build their own scripts, dashboards, or workflows. Traditional engineers aren’t going away, but their focus is shifting to higher-impact projects, complex systems, and supporting non-technical colleagues. In fact, we’re seeing more demand for hybrid roles – engineers who understand infrastructure, data, and AI integration, and who can embed AI safely and strategically into core operations. The bottom line is that engineers are becoming more valuable, especially those with the flexibility to work across systems and domains.
As AI becomes a business driver, we need a multidisciplinary workforce. The big picture is that AI is becoming part of every function, in every company, so we get these blended roles. For executives planning their workforce, it means you should expect to hire or develop people who have one foot in a business or technical domain and another in AI. These multi-disciplinary roles are what’s going to drive AI adoption deeper into the enterprise.
For example, roles like product marketing are essential for applying AI to solve real business needs while respecting ethical and compliance boundaries. UX designers are also critical. AI interfaces, especially for chatbots and copilots, need intuitive, user-friendly design. Customer success teams must understand how end users interact with bots, and where human intervention is still needed. Think of online commerce or travel booking. Those AI tools have to be both functional and friendly.
AI literacy is the new digital literacy. One report put it well: “Every employee needs AI literacy training to remain relevant in their role,” and I couldn’t agree more. From the top down, it’s got to be intentional across the organization. Many non-technical staff feel intimidated by AI. It can be abstract, the terminology isn’t always consistent, and the pace change is FAST. But training and upskilling need to be a priority for all staff. I like to think about it similar to college courses – classes have different level classes (100, 200, 300, etc.), each with increasing level built upon learning from the previous classes.
A 100-level equivalent class can focus on AI terms & definitions as they apply to the organization. This level of literacy creates a common foundation for communication and understanding. This step focuses on ensuring everyone within the business is speaking the same language.
A 200-level equivalent class can include online programs that are role-specific that teams can leverage quickly and cost-effectively from LinkedIn Learning and other AI Learning platforms.
Beyond this there should be considerations for advanced programs like technical sandboxes for teams to explore AI. We’ve also found internal mentor and leadership programs to be effective.
In cooperation with Caltech and one of our largest customers, we have created and implemented an AI academy designed to bridge the gap between theory and practice of AI. We offer our customers exclusive access to some of the world's most advanced research facilities, labs, and companies in an immersive training program divided between Caltech and Beyond Limits.
What makes this program unique is that it's not just academic – it’s hands-on and focused on building a core group of AI advocates from within our customers' organizations. We’ve taken promising engineers from various disciplines including chemical, software, and systems, and given them the technical foundation to drive real transformation. Many of them aren’t traditional data scientists, but we train them to integrate AI into mission-critical projects and serve as internal champions for AI adoption. We’ve already ‘graduated’ hundreds of students who are successfully applying these skills in the field. There’s no silver bullet to future-proofing a company. But smart organizations aren’t just reacting to AI, they’re proactively transforming themselves from the inside out. The ones investing in upskilling and cross-functional collaboration will lead in the AI era.
The future of work in an AI-driven world is full of opportunity, but it demands a commitment to continuous learning. Whether you're just starting your career, leading a company, or somewhere in-between, getting familiar with how AI works and where it fits in your field is crucial. You don’t have to become a coder or hold a PhD, but understanding AI’s strengths and limitations will make you more valuable. The most successful professionals will be those who pair domain expertise with a working knowledge of AI. It simply has to become a strategic priority for anyone in the corporate world.
Soft skills matter, too. As AI takes over more routine tasks, things like creativity, communication, and ethical judgment are becoming more important. And executives must lead by example, embracing AI and encouraging thoughtful, responsible adoption across the organization. In general, the roles we have today might not be the roles we need tomorrow, so stay adaptable. Organizations and people that thrive will be the ones that treat AI not just as a tool, but as a catalyst for reinvention.
Follow Don on Linkedin for more updated > https://www.linkedin.com/in/don-howren-55869/
