Christian Mellado Hjortshoj, founder of Juristic and former Big Four and Associate at a Danish tier one law firm, shared practical insights on how small and medium-sized enterprises (SME) law firms can successfully integrate AI tools into their practice. With his unique background spanning both legal practice and full-stack development, Hjortshoj provided concrete strategies for firms struggling to navigate the AI transformation whilst managing daily operational pressures.

Drawing from real-world implementations across multiple jurisdictions, the discussion addressed the fundamental challenges SME firms face: overwhelming vendor choices, limited budgets, and the need to demonstrate tangible results rather than theoretical efficiency gains.

Start Small

Hjortshoj’s methodology centres on identifying specific daily workflows rather than pursuing comprehensive AI overhauls. He cited a Danish advisory firm that transformed client engagement through intelligent automation. The firm developed a system that monitors the Danish Tax Agency’s publications, automatically scraping relevant updates based on client-specific keywords, then generating draft emails for targeted client communications.

This approach delivered measurable results: client intake increased by 20-25% simply through consistent, timely communication. “It’s not even fully automated, but it reminds him to send to Client A or Client B.” Hjortshoj explained. The system eliminated the common problem of forgetting routine but valuable client updates, whilst maintaining the human touch through personalised additions to AI-generated drafts.

The key insight lies in addressing what Hjortshoj termed “getting the boring stuff done” – automating routine tasks that lawyers often delay or forget, thereby improving client perception without requiring fundamental practice changes.

Understanding AI as Pattern Recognition

Rather than viewing AI as mystical technology, Hjortshoj advocates understanding it as advanced pattern recognition. “I look at AI and think it’s mathematically the right way to look at it, just as an advanced autocorrect,” he noted. This perspective helps firms identify practical applications by mapping their existing workflows and determining where predictive capabilities add value.

The approach emphasises incremental integration rather than wholesale replacement. Firms should focus on areas where AI can enhance human decision-making – extracting dates from documents, identifying patterns in case outcomes, or flagging high-risk scenarios based on document volume and complexity.

Future applications will likely include ambient AI that provides real-time insights during work. “What if your tool could tell you that what you’re doing now looks pretty risky before you even thought about it?” Hjortshoj suggested, envisioning systems that analyse timelines with thousands of events but only two supporting documents, automatically flagging potential evidentiary gaps.

The Human Element

Successful AI adoption requires addressing human psychology rather than just technical implementation. Hjortshoj emphasised that lawyers aren’t inherently resistant to change but demand concrete explanations of how technology solves specific problems.

“If I come to you and said, I can make you more efficient, you’re gonna be like, how?” he observed. The solution lies in presenting tangible use cases: “I don’t want to be more efficient. But I do damn well want to make a timeline much better so I can work with the client much faster without having to discount the hours.”

This approach acknowledges that efficiency rhetoric has become meaningless to many lawyers. Instead, firms should focus on specific pain points – creating better timelines, improving client communication, or reducing administrative burdens – and demonstrate clear before-and-after scenarios.

Community Learning and Cost Considerations

SME firms benefit from collaborative approaches to AI adoption. As guest contributor Knut Magnar Aanestad noted, mid-sized firms often demonstrate higher quality standards than larger practices and show greater willingness to share implementation frameworks without revealing specific solutions.

“They’re happy to share not the specific way of solving it, but that things are solving and the framework of how to solve things,” he explained. This community approach helps cost-sensitive firms learn from peers’ experiences rather than expensive trial-and-error implementations.

The collaborative mindset extends to professional relationships. When one firm improves efficiency whilst opposing counsel remains slow, client experience suffers. “It’s actually in everybody’s interest that we all upgrade,” creating industry-wide incentives for adoption.

Practical Implementation Strategy

Hjortshoj recommends a structured approach beginning with workflow mapping. Firms should document daily activities, identify routine tasks suitable for automation, and start with small-scale pilots. “Don’t try to let AI do A to Z. Just make it do smaller steps,” he advised.

Critical early steps include:

• Taking prompting courses to improve AI interaction skills
• Understanding that AI requires explanation, much like training junior lawyers
• Focusing on workflow tools that integrate with existing value chains
• Building data foundations without necessarily restructuring document management systems

The goal isn’t replacing lawyers but enabling them to work differently. “Technology is not a zero-sum game,” Hjortshoj noted, challenging the binary thinking that assumes AI adoption means job elimination.

Financial Models and Client Value

The discussion addressed concerns about billable hour models in an AI-enhanced environment. Rather than predicting the death of time-based billing, Hjortshoj suggested firms will differentiate between commodity work suitable for automation and higher-value advisory services.

“Make sure you understand what part of your work is a commodity and make sure you understand what part is want. Then automate the commodity part,” he recommended. This enables faster delivery of routine work whilst potentially commanding higher rates for complex advisory services.

The transformation also requires lawyers to become more numerically literate. “How are you a trusted advisor if you don’t know how to do numbers?” Hjortshoj challenged, noting that clients operate in risk-assessment frameworks lawyers often ignore. Understanding that a 50% legal risk on 300 agreements translates to 0.14% practical risk for the client changes the advisory conversation entirely.

Future Talent Considerations

AI adoption will become a recruitment differentiator for younger lawyers entering the profession. However, the attraction won’t be superficial AI branding but demonstrated capability to deliver differentiated pricing models, faster service delivery, and genuine advisory value.

Firms that successfully integrate AI will offer three key advantages: enhanced billing flexibility, improved speed of service, and elevated trusted advisor capabilities through better risk quantification and pattern recognition.

Conclusion

SME law firms cannot afford to wait for AI adoption, but they also cannot afford unfocused experimentation. Success requires mapping existing workflows, starting with routine task automation, maintaining focus on client value rather than internal efficiency metrics, and building community knowledge-sharing relationships.

The firms that will thrive are those that view AI as a tool for enhancing human capabilities rather than replacing them, focusing on specific problem-solving rather than general efficiency claims, and maintaining the quality and client focus that defines successful SME practice.

As Hjortshoj concluded, the approach should be methodical: “Start with routine tasks. Map out the workflow and let AI do smaller steps. Have it help you. And then make sure to evaluate the billing after that.” This practical, incremental approach offers SME firms a realistic path to AI integration without risking their core business relationships or financial stability.