What Adam Smith can teach solicitors about AI - a guide to staying compliant, efficient and risk-aware

From rethinking workflows to optimising knowledge management and reinforcing ethical frameworks, Dr Corsino San Miguel presents a strategic roadmap to help firms stay compliant, efficient and risk-aware when implementing AI.

In The Wealth of Nations, Scottish philosopher Adam Smith famously posited that wealth and prosperity arise from the division of labour – a system in which work is broken into specialised tasks to enhance efficiency and productivity. This principle, which revolutionised industrial production and shaped modern economies, is now being redefined in the age of artificial intelligence (AI).

In a previous article, ‘From metal behemoths to agentic AI: what Scotland’s legal sector can learn from bridging innovation’, I explored how agentic AI models – designed to carry out highly granular tasks – enable an unprecedented level of specialisation within the legal sector. AI agents remain a compelling vision for the future of legal services and have become a dominant narrative in AI legal marketing. However, while the term is often used broadly to describe AI tools connected to large language models (LLMs) via application programming interfaces (APIs), not everything leveraging these technologies is truly agentic AI.

The future of legal specialisation will not be defined by AI’s capacity alone but by how well firms align limitless technological potential with structured, ethical and intelligent adoption.

Rather than AI operating autonomously, the real transformation in legal practice will come from workflow AI: structured, process-driven systems that seamlessly integrate into legal operations. The advancement of legal AI is not aimed at supplanting lawyers but at strategically integrating LLMs within workflows to boost efficiency, sharpen decision-making and optimise complex legal processes.

For Scottish law firms, the question is no longer whether AI will reshape legal services – it already is – but how to implement it strategically. Successful integration goes beyond adopting new technologies; it requires a fundamental rethinking of workflows, optimisation of knowledge management and reinforcement of ethical frameworks. This article cuts through the noise surrounding AI terminology and, after outlining workflow AI, presents a structured, actionable roadmap to ensure firms implement AI in a manner that is compliant, efficient, risk-aware and aligned with the realities of legal practice.

Recent approaches in legal tech often frame AI in terms of agents, yet the term encompasses fundamentally different architectures. Some define agents as fully autonomous systems where LLMs dynamically direct their own processes and tool usage, maintaining control over how they accomplish tasks. Others apply the term to structured, rule-based implementations, where AI follows predefined workflows with limited adaptability.

The key distinction lies in control and orchestration. Workflow AI systems function within structured, coded pathways, ensuring predictability, efficiency and compliance by integrating AI tools and LLMs into legal workflows. Agentic AI (stricto sensu), by contrast, dynamically directs its own processes, adapting tool usage in real time with greater autonomy, performing tasks and recommending actions based on context and data inputs.

For law firms, this is more than a technical distinction – it defines how AI should be practically deployed. While agentic AI remains largely conceptual and aspirational, workflow AI is already transforming legal operations. Advances in LLMs with expanded context windows are enhancing workflow autonomy, improving accuracy, efficiency and process optimisation while preserving professional legal judgement. With the ability to retain contextual awareness, workflow AI moves beyond simple automation, supporting multi-step legal processes with greater coherence and precision.

With a clearer understanding of workflow AI’s role in structured legal processes, the next step is identifying where it can provide the most immediate and measurable value within law firms.

The first step is to map current workflows to identify AI opportunities. Every firm is different and not all legal tasks are suited for AI-driven enhancement. Thus, firms must carefully analyse their existing workflows to pinpoint areas where AI can deliver the most immediate and measurable impact.

While smaller firms may succeed with AI solutions operating within structured, predefined workflows, as seen with Microsoft Copilot, medium-sized firms face greater complexity, requiring flexible and tailored AI integration. These firms should begin with targeted use cases that deliver measurable return on investment, prioritising workflows with clear success metrics to demonstrate AI’s value quickly and effectively.

Success lies in integrating AI not as a disruptor but as a partner – enhancing legal practice while safeguarding professional insight and integrity.

Only then can firms progress towards developing a unified intelligence layer that seamlessly integrates legacy databases, cloud platforms and case files, transforming fragmented systems into a cohesive, data-driven legal operation. At this stage, AI evolves from single-purpose workflow automation into an orchestrated ecosystem of specialised workflow AI systems, each designed to collaborate on complex legal processes. Workflow orchestration, driven by an LLM, emerges as the next frontier – where AI components no longer function in isolation but as an interconnected framework, dynamically coordinating tasks across eDiscovery, legal research, document analysis, contract review, predictive analytics and other legal/administrative processes.

AI is only as good as the quality of its data, making legal knowledge optimisation a critical step in AI integration. Knowledge management in the legal sector traditionally comprises explicit and tacit dimensions. Explicit knowledge is systematically documented and easily transferable to digital repositories. Tacit knowledge, by contrast, is intangible, anchored in professional intuition and strategic judgement gained through extensive practice.

The emergence of AI, particularly LLMs, introduces a third dimension: AI-driven knowledge. This new category refers to insights and patterns previously undetectable by conventional technology frameworks, now accessible due to the sheer volume and complexity of data AI can process. Notably, AI-driven knowledge emerges organically from the integration of diverse datasets, bridging explicit repositories with previously inaccessible aspects of tacit expertise.

For Scottish law firms, AI implementation demands rigorous data governance and the standardisation of data formats, ensuring consistency, reliability and accuracy. Poor data hygiene poses substantial risks – undermining compliance, eroding operational efficiency and diminishing trust in AI outputs. By embedding both human expertise and AI-generated insights within clearly structured, standardised AI-trainable formats, firms ensure that AI performs at its highest potential, as its effectiveness is only as strong as the quality of its underlying data – making legal knowledge optimisation a critical step in AI integration.

However, firms must remain vigilant to the risks posed by major cloud providers who not only supply essential AI infrastructure for AI models but frequently offer proprietary LLMs. This vertical integration exposes firms to vulnerabilities, including the risk of tacit knowledge leakage. When law firms adopt cloud-based AI solutions, tacit knowledge may become inadvertently exposed or commoditised. Whether through training datasets containing nuanced professional insights or via LLM-generated patterns that subtly replicate professional intuition, the risk of tacit knowledge leakage to external providers or competitors is tangible and significant. To mitigate these risks, firms should adopt a framework of ‘retaining strategic knowledge’, ensuring intentional protection, careful management and proactive leveraging of AI-generated insights.

As Scottish law firms navigate this new frontier, embedding AI within an explicit and robust ethical framework becomes imperative. While workflow AI offers significant opportunities, ethical considerations – particularly fairness, transparency, accountability, privacy and continuous professional competence – must remain central to its deployment.

• Bias mitigation: AI-driven tools, however sophisticated, risk perpetuating biases embedded within their training data. Firms must commit to ongoing audits and critical evaluations of their AI systems to ensure fairness, safeguard professional integrity and maintain public trust.
• Accountability and transparency: AI increasingly shapes critical legal decisions, yet accountability boundaries between AI providers, legal practitioners and firms remain unclear. Professional liability policies must be reviewed and adapted to reflect AI-enabled workflows, ensuring legal practitioners remain ultimately responsible for AI-assisted decisions.
• Privacy and data security: Law firms operate under heightened ethical obligations regarding confidentiality. Comprehensive data governance frameworks and security audits are essential to maintain compliance with GDPR standards.
• Continuous professional learning: AI literacy is no longer optional. Structured training programmes must be institutionalised to ensure legal professionals remain technically fluent and ethically competent as AI evolves.

Adam Smith’s division of labour transformed industries by maximising efficiency within a finite workforce – specialisation was bound by human capacity. Law firms, operating within the same constraints, now face an unprecedented shift: workflow AI systems introduce limitless scalability, an ecosystem of specialised systems that can continuously refine, expand and accelerate legal processes without the natural limits of human expertise.

Yet, limitless potential demands disciplined implementation. Without structure, AI risks disrupting rather than enhancing legal practice. Adoption must be measured and strategic, progressing from simple applications to more sophisticated architectures, ensuring efficiency gains do not come at the cost of professional insight.

The future of legal specialisation will not be defined by AI’s capacity alone but by how well firms align limitless technological potential with structured, ethical and intelligent adoption. Success lies in integrating AI not as a disruptor but as a partner – enhancing legal practice while safeguarding professional insight and integrity.

Written by Dr Corsino San Miguel. PhD, LLB in Scots law and graduate in Spanish law; co-founded and led European Telecom Company before entering academia. He is now a member of the AI Research Group and the Public Sector AI Task Force at the Scottish Government Legal Directorate. The views expressed here are personal.