The transition of professional services organisations into the intelligence era has stopped being a future concept. It is happening now. In fact, 95% of professionals believe GenAI will continue being a central part of their organisation’s workflow within the next five years. But as law firms, accounting practices, consultancies, and other professional services organisations rush to implement AI solutions, a critical question emerges: are you building what your clients actually want?
As AI becomes standard practice, your clients are developing clear expectations about how these systems should work. They want efficiency and innovation, but not at the expense of accuracy, transparency, or the professional relationships they value.
Understanding these expectations is essential for your firm to implement AI systems that truly serve your clients' needs.
1) Trust and Reliability: The Foundation
Trust forms the foundation of all professional relationships. When AI enters the equation, your clients expect the same reliability they have always demanded from their advisers.
Consistent AI Accuracy
People expect AI to deliver accurate results consistently. An AI system that is brilliant one day and unreliable the next creates more problems than it solves. Clients need reliability they can depend on, regardless of the specific task or context.
It is important to understand, that people are interacting with AI tools for the past 3-4 years, and they understand AI's limitations. Most people do not expect AI to be right 100% of the time. However, they expect it to know when it might be wrong. An AI system that can articulate its confidence level, flag ambiguities, and identify when human review is essential demonstrates the kind of professional judgement clients value.
Behind every AI output, clients expect multiple layers of quality assurance. They want to know that results have been checked, cross-referenced, and validated before reaching them. The black box approach, where AI produces answers without clear verification, fundamentally undermines trust.
The Role of Human Oversight
Professional services clients' expectations of AI reveal an interesting tension. They want the efficiency of AI but the accountability of human professionals.
People expect qualified professionals to review AI outputs, particularly for complex or high-stakes matters. They are not paying for unvetted algorithmic advice. They are paying for professional judgement enhanced by technological capability. When matters become ambiguous or require nuanced interpretation, clients expect seamless escalation to human experts. The AI should recognise its limitations and facilitate access to human expertise.
Most critically, clients expect clear accountability and liability terms for the advice they receive, regardless of how much AI contributed to its development. The professional services model is built on personal accountability, and AI does not change that fundamental expectation.
2) Transparency is key
71% of corporate law clients and almost 60% of corporate tax clients said they do not know whether their outside firms are using GenAI. But, if trust is the foundation, transparency is the framework that supports it. Clients of professional services firms are increasingly sophisticated about AI, and they are asking harder questions about how it works, what data it uses, and how decisions are made.
- Clear Explanations: Clients expect AI systems to explain their reasoning. "Because the AI said so" is not an acceptable answer in professional services. When AI makes a recommendation or reaches a conclusion, clients want to understand the logic, the data sources, and the assumptions underlying that output.
- Value, Efficiency, and Cost: Whilst trust and transparency form the ethical foundation of AI client expectations, practical considerations around value, efficiency, and cost are equally important. Clients are asking a straightforward question: what is in it for me?
- Time and Efficiency Gains: The most immediate expectation clients have of AI is that it will accelerate service delivery without compromising quality.
- Faster turnaround times. Whether it is document review, financial analysis, due diligence, or research, clients expect AI to compress timelines significantly. A process that once took weeks should take days. Tasks that took days should take hours.
- 24/7 availability. Unlike human professionals, AI does not sleep, take holidays, or work within time zones. Clients increasingly expect to access certain services or information at any time, receiving immediate responses to routine queries whilst complex matters are queued for human attention during business hours.
- Scalability without proportional cost increases. One of AI's most attractive features is its ability to handle increased volume without linear cost scaling. Clients expect to benefit from this, whether through handling larger transactions, analysing more data, or exploring more scenarios without corresponding fee increases.
The Pricing Conversation
One of the aspects that creates tension between clients and firms is pricing. Your clients have clear expectations.
If AI reduces the time required to complete a task from 10 hours to 2 hours, clients question why they should pay for 10 hours of work. They expect pricing models to evolve, reflecting the efficiency gains AI provides. Clients want to understand when AI is being used and how it affects their fees. Are they paying a premium for AI-enhanced services, or receiving a discount due to efficiency gains? The expectation for clarity is universal. Interestingly, in 2025, KPMG pushed its auditor for a fee reduction citing AI-driven efficiency. The FT reports the audit fee dropped 14%, from $416k (2024) to $357k (2025), based on filings.
Particularly for ongoing relationships or large engagements, people expect to see measurable returns from AI implementation. This might manifest as cost savings, faster time-to-market, better outcomes, or reduced risk, but it needs to be demonstrable, not theoretical.
3) Domain Expertise and Specialisation
Clients expect AI deployed by professional services firms to understand their industry deeply.
Industry Knowledge
- Regulatory frameworks: A legal AI should know the relevant statutes, regulations, and case law. A financial services AI should understand accounting standards, tax codes, and reporting requirements. Generic AI that requires constant correction or supplementation fails to meet client expectations.
- Industry terminology and context: Every industry has its jargon, conventions, and contextual nuances. Clients expect AI to speak their language fluently, understanding that "material" means something different in legal, accounting, and manufacturing contexts.
- Current best practices: Professional services clients expect AI to reflect current industry standards, methodologies, and best practices, not outdated approaches or generic advice that does not account for sector-specific considerations.
Keeping Knowledge Current
One particular challenge clients are acutely aware of is the currency of AI knowledge. Laws, regulations, and standards change frequently. Clients expect AI systems to reflect current requirements, not outdated frameworks.
In fast-moving fields like technology consulting or financial services, market developments can fundamentally alter the landscape. Legal AI, in particular, must incorporate recent court decisions and regulatory guidance. Clients expect professional services firms to be transparent about knowledge cutoff dates and to have processes for ensuring AI guidance is supplemented with current developments when necessary.
4) Customisation and Personalisation
Generic advice, even if technically correct, often misses the mark in professional services. Clients expect AI to adapt to their specific circumstances, preferences, and objectives.
- Business Context Awareness: Clients expect AI to learn and remember key facts about their business: their structure, their industry position, their strategic objectives, their risk tolerance. Repeatedly providing the same background information undermines efficiency and suggests the AI is not truly serving their needs. In ongoing relationships, clients expect AI to build on previous work, remember past decisions and their rationales, and maintain continuity across engagements.
- Adaptive Learning: When clients provide feedback, whether correcting an error, expressing a preference, or clarifying their needs, they expect AI to learn from it. Systems that make the same mistakes repeatedly or ignore stated preferences frustrate clients and undermine confidence. From communication style to report formatting to analytical depth, clients have preferences about how they want to receive information and advice. AI should remember and honour these preferences without requiring constant re-specification.
5) Ethical AI Use and Bias Mitigation
As AI in professional services becomes more prevalent, clients are increasingly concerned about ethical implications and potential biases that could affect the advice they receive. They are aware that AI systems can perpetuate or amplify biases present in training data. But, they expect professional services firms to actively work to identify and mitigate these biases, particularly in areas like employment and HR consulting, credit and financial analysis, and legal risk assessment.
Clients increasingly expect AI systems to be developed and tested by diverse teams who can identify potential blind spots or biases that homogeneous groups might miss. Most importantly, sophisticated clients expect firms to conduct regular audits of their AI systems to identify and address potential biases.
Building Client-Centric AI
The expectations people have of AI in professional services are both demanding and nuanced. They want systems that are accurate and reliable, transparent and explainable, secure and compliant. They expect efficiency and cost effectiveness, but not at the expense of the human relationships and professional judgement they value.
- Trust is non-negotiable. No amount of efficiency or capability can compensate for unreliable or inaccurate AI. Build quality assurance and validation into every aspect of AI deployment.
- Transparency builds confidence. Clients who understand how AI works and what its limitations are will trust it more than those left in the dark. Invest in explainability and clear communication.
- Humans remain central. AI should enhance, not replace, professional relationships. The most successful implementations create powerful human-AI partnerships that leverage the strengths of both, with human review ensuring quality and accountability.
- One size does not fit all. Generic AI solutions may provide a starting point, but clients expect customisation, personalisation, and adaptation to their specific needs and contexts.
- Ethics and responsibility matter. As AI becomes more powerful, ethical considerations become more important. Proactive attention to bias, fairness, and responsible use is not just good practice. It is what clients expect.
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* This articles' cover image is generated by AI
