AI is no longer a side project for the technology enthusiast in the firm. It has quickly become part of everyday accounting software, document production, research, client communication, workflow and pretty much anything else that goes on inside an accounting and advisory firm. The strategic question is therefore not whether the firm will use AI, but how it will use it deliberately, safely and commercially.
A good AI strategy does not need to be a lengthy technology plan. It should be a practical management framework that connects the firm’s purpose and priorities with clear choices about people, process, technology, risk and investment. The following 13 elements provide a useful checklist.
The starting point:
AI is an enabler, not the strategy itself. The firm should be clear about the outcomes it wants before selecting tools.
1. A clear vision and purpose
Understand and explain to everyone in the firm why AI matters to the firm and how it supports the broader business strategy. For example, the aim may be to improve turnaround times, create capacity, lift consistency, strengthen client service, enhance the employee experience or develop new advisory services. A clear purpose helps owners make investment choices and stops the firm unnecessarily accumulating disconnected tools and experiments.
2. Leadership, ownership and culture
Adoption will be shaped more by leadership behaviour than by technology. Leaders should model appropriate use, encourage curiosity and create a safe environment for controlled experimentation. Nominate a partner/director sponsor and an operational owner or AI working group. Their role is to coordinate priorities, remove roadblocks and ensure that lessons are shared across the firm rather than remaining with a few enthusiasts.
3. A prioritised portfolio of use cases
Create an inventory of potential uses and rank them by value, feasibility and risk. Start with repetitive, high-volume and reviewable work -for example meeting summaries, first drafts, internal knowledge search, workflow triage, data extraction and client communication. Avoid launching too many pilots at once. Each use case should have an owner, a defined problem, an approved tool, a review method and a decision point for scaling, changing or stopping it.
4. Practical resources and support
Provide a budget for licences, training, specialist advice and controlled pilots. Just as importantly, provide time. People cannot learn to use AI effectively if every hour is already committed to client deadlines and chargeable work. Build experimentation, documentation and improvement into work plans. Small firms do not need a large innovation budget, but they do need visible permission and realistic capacity.
5. Governance, policy and accountability
Document what is allowed, what is prohibited and who is accountable. The AI policy should link to the firm’s broader risk management framework, privacy obligations, confidentiality requirements, cyber controls and quality management. Establish an approved-tool list, rules for client data, escalation pathways, incident response and consequences for non-compliance. Governance should enable sensible use—not become a blanket ban that pushes activity underground.
6. Privacy, confidentiality, cyber security and insurance
Client information should not be entered into a public or unapproved AI service. Assess how each product stores, uses and retains prompts and files, whether data may train a model, where it is hosted, how access is controlled and what contractual protections apply. Include AI in cyber-risk assessments and vendor due diligence. Review professional indemnity and cyber insurance for exclusions, notification requirements or conditions relating to AI-enabled work. Review engagement letters for appropriate disclosures.
7. Human oversight, professional judgement and quality control
The accountant / advisor remains responsible for the work. Define the level of human review required for each use case and make it proportionate to the risk. Technical, tax, audit, valuation and advisory outputs require source checking, professional scepticism and an understanding of the client’s circumstances. Team members should be trained to detect hallucinations, outdated information, bias, missing context and plausible-sounding errors. The review process—and, where appropriate, the use of AI—should be documented.
8. Technology architecture and vendor strategy
Understand what the firm’s existing software applications already provide and what’s on their AI roadmaps. This should be one of the first checks in the strategy. Almost every application used by accounting firms is now embedding AI in some way, whether through document summarisation, email drafting, workflow triage, data extraction, insights, automation or natural-language reporting. Established vendors often have larger budgets, deeper product knowledge, stronger integration, identity management and data controls than a small firm can build on its own. The opportunity is to understand and leverage those capabilities before buying stand-alone tools. Specialist AI products may still add value, but assess whether they solve a genuine gap, integrate with core systems, avoid duplicate data and meet the firm’s security requirements. Maintain a register of AI-enabled products, including embedded features that team members may not recognise as AI.
9. Skills, knowledge and reusable assets
AI literacy is now a core professional capability. Training should cover not only prompting but also tool limitations, verification, confidentiality, ethical obligations, workflow design and good judgement. Build reusable assets such as approved prompts, agents, templates, checklists and playbooks. These should be tested, version-controlled and connected to the firm’s knowledge system. Supplement internal capability with trusted external specialists where needed.
10. Adoption, collaboration and change management
Technology creates value only when people consistently use it in redesigned workflows. Communicate what is changing, why it matters and how success will be judged. Use demonstrations, coaching and peer champions. Regular face-to-face sharing is often more effective than relying only on a Teams channel or knowledge portal. Celebrate practical wins, discuss failures openly and address concerns about job design, review responsibilities and the development of junior team members.
11. Process redesign and service innovation
Do not simply insert AI into an inefficient process. Map the work from beginning to end and ask what can be eliminated, standardised, automated, augmented or moved closer to the client. Consider how AI changes review layers, turnaround promises, pricing, capacity planning and the mix of skills required. Also look beyond efficiency. AI may enable better client insights, more frequent reporting, proactive advice and services that were previously uneconomic to deliver.
12. Measures, review and continuous improvement
Set a small number of measures for each initiative – for example time saved, turnaround time, rework, error rates, adoption, client response, employee experience and financial return. Compare actual results with the baseline. Review the strategy regularly as tools, regulation, risks and vendor capabilities change. Retire weak use cases, scale proven ones and update policies, training and controls.
13. Stay informed and look ahead
To say AI is moving quickly is an understatement! Make regular horizon scanning a defined responsibility, not an occasional activity. Keep an eye on what your core software vendors are releasing or planning, what emerging specialist tools are offering, and what changes are occurring in privacy, cyber, quality and professional guidance. The aim is not to chase every new release, but to stay aware of developments that could improve client service, efficiency, quality or risk management, and to ensure the strategy remains current.
The bottom line
The firms that thrive with AI will not necessarily be those with the most tools. They will be those that connect AI to a clear purpose, redesign work thoughtfully, protect client trust, build capability and measure whether the investment is producing better outcomes.