Komninos Chatzipapas (Κομνηνός Χατζηπαπάς), Founder at Orion AI Software.
Artificial intelligence is rapidly reshaping professional services, but its impact is not uniform across industries. Some fields are experiencing rapid, visible transformation, while others are evolving more gradually. The extent of change largely depends on how structured, repeatable and risk-sensitive the work is.
Software development, legal services and accounting offer a useful lens for understanding these differences as some of the most exposed industries, according to research from Anthropic. I see these dynamics firsthand through running a software development agency that works with clients across these professional services industries. That experience has given me a practical view of how AI is beginning to reshape workflows, expectations and value creation in these fields.
Software Development: From Execution To Orchestration
In software development, AI-powered tools like Cursor and Lovable are fundamentally changing how software gets built. These tools can generate code, refactor entire systems, debug issues and even create full applications from simple prompts.
Tasks that once took hours, such as writing boilerplate code or troubleshooting errors, can now be completed in minutes. This has increased productivity and lowered the barrier to entry, enabling small teams or even solo founders to build products that previously required large engineering organizations.
Because of this, we’ve encouraged engineers at my agency to integrate AI into their daily workflows where it meaningfully improves the process. I’ve found this especially valuable when building features that aren’t fully fleshed out, since giving AI more freedom to fill in the gaps lets us iterate quickly and surface design questions earlier.
That said, the transition hasn’t been without friction. The biggest challenge we’ve encountered is error handling, as AI-generated code has a tendency to swallow errors, which leads to silent failures in production. Our engineers now spend meaningful time auditing AI output for this specific issue, which has persisted despite major improvement in other areas of coding over the past year.
AI is also shifting the role of developers. Instead of spending most of their time writing code, engineers are increasingly focused on guiding AI, reviewing outputs and designing systems. The job is moving from execution to orchestration, where the ability to frame problems and leverage AI effectively is becoming more valuable than manual coding itself.
Legal Services: Incremental Change, Not Transformation
In contrast, the legal industry has experienced a slower and more cautious adoption of AI. While tools are now commonly used to draft contracts, summarize documents and provide preliminary insights, they have not fundamentally changed how legal work is performed.
Business owners and stakeholders often use AI to generate first drafts or get a second opinion, but these outputs still require careful review by qualified lawyers. Legal work involves high stakes, nuanced interpretation and jurisdiction-specific knowledge, which are areas where AI is not yet fully reliable.
Concerns around accuracy, liability and confidentiality further limit widespread automation. As a result, AI in law functions primarily as an assistive tool, improving efficiency but not replacing core workflows.
Working with clients in the legal industry, I’ve seen how lawyers are integrating purpose-built tools like Harvey and Legora into their workflows. These platforms are primarily used to search through large document sets and draft proceedings more efficiently, rather than to replace substantive legal reasoning.
What’s notable is that even with these tools available, the lawyers I speak with treat AI output as a starting point that demands close review, not a finished product. The technology is genuinely useful for accelerating the mechanical parts of legal work, but the judgment layer remains mostly with the human professional.
Accounting: Automation Of The Back Office
Accounting has seen more meaningful automation, particularly in routine, process-driven tasks. Platforms like Ramp and Rho can automatically match invoices with transactions, categorize expenses and reconcile accounts with minimal human input.
This has significantly reduced the need for manual bookkeeping. Processes that once required hours of work can now happen in real time, with greater accuracy and consistency.
I’ve observed an interesting geographic split in how this is playing out. Accountants in Europe, where standards are stricter and mandatory audits are far more common, have been particularly active in adopting AI tools to match invoices against banking records, a task that historically consumed enormous amounts of junior accountant time. A handful of fintechs have built this matching directly into their platforms, effectively removing the workflow from the accountant’s plate altogether.
A Shift Toward Higher-Leverage Work
AI’s impact on professional services is uneven, but the direction is consistent. Fields with structured, repeatable tasks, like software development and accounting, are seeing faster transformation, while more complex, high-risk fields like legal are evolving more gradually.
Across all industries, however, the role of the professional is changing. AI is not eliminating jobs so much as redefining them. The value is shifting away from routine execution toward judgment, strategy and oversight.
Professionals who learn to work effectively alongside AI are likely to gain significant leverage and adopt fundamentally different ways of operating. In many cases, the advantage will come less from replacing human expertise and more from amplifying it.
This shift carries implications for both individuals and organizations. For employees, the challenge is not simply whether AI will change their role, but how quickly they can adapt to new workflows and expectations. While some disruption is inevitable, AI also has the potential to shift more work toward judgment, coordination and higher-value problem-solving. The professionals I believe will benefit most will likely be those who learn how to use these tools thoughtfully and integrate them into their daily work early.
For companies in professional services, waiting for the technology to fully mature before adopting it may prove riskier than experimenting early and learning through iteration. Early adoption will inevitably involve mistakes and workflow changes, but those experiences also help organizations build internal expertise and operational understanding. In a rapidly evolving landscape, the organizations that develop those capabilities sooner are likely to be in a stronger position than those that delay adoption altogether.
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