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Executive summary
This checklist is based on the AI maturity framework in ThoughtLab’s forthcoming “AI-powered investment firm” global survey, of which Grant Thornton is a sponsor and contributor. The checklist translates its core principles into seven pragmatic steps for asset and wealth management leaders. It shows leaders how to evolve along the maturity continuum, turning into responsible, measurable progress — boosting growth, efficiency and control without overwhelming their teams.
For many asset and wealth management teams, “AI” still feels like a one-size-fits-all buzzword. In reality, each firm needs a level and pace of AI adoption that matches its own goals, capabilities and controls. This checklist — based on the AI maturity framework developed by ThoughtLab, an analytics-driven thought leadership firm — helps you pinpoint where you are on that journey and choose the next practical steps that deliver real value.
The AI maturity framework considers the foundational pillars required for successful AI transformation:
- AI strategy and implementation plan
- Innovation culture
- Use of modern IT platform
- Right mix of AI talent and skills
- Adoption of advanced AI tools
- Effective data management
- AI governance framework
Grant Thornton’s Digital Transformation Survey showed that nearly 75% of asset management respondents said their organizations are using generative AI. But do they have the correct foundations and roadmaps in place to make the best use of those AI tools? And how does your firm compare?
AI maturity framework: Where does your asset management firm stand?
1. AI strategy and implementation plan
What to do:
Create an effective AI strategy and implementation roadmap.
What it looks like in practice:
Your firm sets a business-led AI strategy before implementing any tech roadmap—clarifying ambitions, guardrails and priority use cases. Executives agree on the problems AI will solve, the value expected (revenue, cost, risk) and the risk appetite that guides every decision.
Why it matters:
A shared strategy prevents “random acts of AI” and gives confidence in first moves.
Grant Thornton insight
Your first step to an AI strategy
- Define clear, measurable objectives and success metrics tied to business goals before making any technology decisions.
- Build alignment across the organization to support adoption of the strategy, objectives and metrics, and ensure key leaders are part of the decision-making process.
2. Innovation culture
What to do:
Cultivate an innovation culture that encourages responsible AI experimentation
What it looks like in practice:
Leaders make controlled experimentation normal — lessons learned are shared and successes scaled quickly. Pilot teams present results to leadership every quarter; high-value experiments are expanded swiftly, while unproductive ones are retired promptly.
Why it matters:
Visible and proactive leadership sponsorship accelerates adoption and prevents innovation fatigue.
Grant Thornton insight
Start small with low-risk AI use cases
- Automate prospect meeting notes for financial advisors. Capture and summarize key goals and needs, then surface next steps for follow-up.
- Launch a private internal AI assistant. Let teams quickly find policies, product documents and answers to routine inquiries — without exposing client data.
- Then, work up to:
- Personalized engagement and investing. Offer real-time portfolio views and recommendations tailored to a client’s goals, risk and life stage.
- Agentic AI for operations. Streamline data aggregation, reconciliation, compliance and reporting with software agents for narrow, pre-approved tasks — such as data aggregation, reconciliation drafts and report prep — with human review for any final action.
- Embedding AI across the business. Expand adoption through better data foundations, change management and governance.
3. Use of modern IT platform
What to do:
Establish a modern, cloud-based IT platform to facilitate AI adoption across systems
What it looks like in practice:
Core systems are cloud-ready, secure and flexible enough for AI workloads. When workload spikes — for example, during month-end performance calculations or large-scale risk simulations — the cloud automatically adds computing power, then scales back once processing is complete, all within a zero-trust security framework.
Why it matters:
Modern platforms cut downtime, cost and cyber risk — the bottlenecks that stall AI.
Mini case study
Modernizing to enable AI at scale
- Scenario: An asset manager lacked cloud-based core systems but wanted to pilot and scale AI.
- Approach: We implemented a cloud automation solution using a large language model to process legal documentation, guided by a capability-mapping framework that tied core LLM capabilities to business functions.
- Result: A 340% ROI driven by faster turnaround, reduced manual effort and improved data accuracy
4. Right mix of AI talent and skills
What to do:
Develop or acquire AI talent and skills across the enterprise
What it looks like in practice:
Your people receive role-specific training so they can use AI tools confidently and responsibly in everyday decisions. Investment, operations and client service teams consult AI dashboards or assistants to test scenarios and validate decisions.
Why it matters:
When AI is woven into normal workflows, decisions speed up, adoption sticks and specialist data scientists can focus on higher-value innovation.
Many asset and wealth management teams lack practical fluency in technology use and in making data-literate decisions.
“An optimal method to upskill your organization is through role-based, scalable training tied to business objectives and reinforced by clear responsibilities and career paths,” said Karan Gulati, Grant Thornton Business Consulting Principal.
5. Adoption of advanced AI tools
What to do:
Draw on the latest AI technologies, such as generative and agentic AI
What it looks like in practice:
The firm selects proven, purpose-built solutions that solve real pain points. For example, a ready-made AI text analysis tool can scan prospectuses and research reports in minutes, freeing analysts to focus on developing insights instead of on extensive reading.
Why it matters:
Quick wins build confidence and free up time and budget for bigger, longer-term projects that deliver stronger results.
Grant Thornton insight
How to decide — build or buy?
- Start with the pain point: pick the option that solves a defined business problem.
- Check integration and controls: prioritize data security, audit trails and user management in your environment.
- Compare time to value and total cost: pilot in weeks and track one primary metric (cycle time, error rate or dollars saved).
Shane O’Neill, Grant Thornton Ireland Consulting Partner, explained how choosing the right tool resulted in clear ROI for one asset manager.
“The AI-powered operations copilot automated the firm’s foreign exchange rate and market data variance checks, validated expense accruals against historical patterns and suggested likely root causes for net asset value breaks,” O’Neill said. “That reduced investigation time by 60% and delivered a 3.8 times return on investment in the first year.”
6. Effective data management
What to do:
Build and maintain a system for cleansing, integrating and optimizing firmwide data
What it looks like in practice:
Data is clean, connected and governed before models go live. All critical data lives in one trusted, well-organized source that automatically feeds reports and AI tools, with built-in alerts that flag quality issues before decisions are made.
Why it matters:
Trusted data underpins reliable AI outputs and keeps processes audit-ready.
Fragmented, inconsistent source data is the most common blocker to AI adoption because it erodes trust in outputs.
“Start with a quick data maturity assessment, fix the highest-impact quality and integration gaps and then update each use case so it runs on clean, available data that fits how teams work,” O’Neill said.
7. AI governance framework
What to do:
Install governance policies and structures for the responsible and effective use of AI
What it looks like in practice:
Policies and oversight keep AI transparent, compliant and in line with the firm’s risk limits. A cross-functional council reviews every model for clarity and regulatory fit before launch, then tracks real-world performance and controls throughout its lifecycle.
Why it matters:
Strong governance is the gate that turns small pilots into enterprise-scale solutions that the board, regulators and clients can trust.
“Start small and make governance repeatable,” Gulati said. “Establish an AI ethics committee to review projects, define roles and set standards. Put a control framework in place with model documentation, audit logging, performance monitoring. And ensure there is human review to management exceptions with a right-sized AI literacy program as adoption scales firmwide.”
Consider these AI questions
- Overall, what ROI, if any, are you seeing from your uses of AI? Please consider the full range of benefits and costs of the investment?
- Large negative (over -7%)
- Moderate negative (-6.9% to -3.0%)
- Small negative (-0.1% to -2.9%)
- No/negligible return
- Small positive (0.1% to 2.9%)
- Moderate positive (3.0% to 6.9%)
- Large positive (>7%)
- Don’t know
Tip: AI-mature firms report large positive ROI and are already piloting or scaling GenAI.
- How extensively do you use the following AI technologies across your business?
- Natural language processing: understands and generates human language, such as for chatbots and voice recognition.
- Machine and deep learning: enables computers to learn from data and improve on their own, such as for anomaly detection or predictive analysis.
- Computer vision: understands visual data to perform tasks, such as facial recognition.
- GenAI: creates content and analysis in different formats, such as for document summaries.
- Agentic AI: acts on its own to make decisions and complete tasks, such as fraud detection and response.
- Multimodal AI: understands and combines different types of input, such as text, images, data and speech, such as analyzing information in different formats for investment research.
- Explainable AI: provides clear, understandable explanations for decisions and actions to ensure interpretability, transparency and accountability.
- Quantum AI: uses quantum computing to enhance AI processes, such as for quantum-powered Monte Carlo simulations.
Tip: Wide use of some or all of these technologies indicates an AI-mature organization.
Next steps on the AI maturity continuum
Your position on the maturity spectrum isn’t a label — it’s a launch point. Strengthen the pillar that will unlock the greatest business value next, measure ROI early and repeat. Firms that follow this discipline move from isolated pilots to enterprise-wide AI that grows revenue, cuts costs and stays compliant. Find out how your firm stacks up against peers in ThoughtLab’s “AI-powered investment firm” report, publishing in Q4 2025.
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This Grant Thornton Advisors LLC content provides information and comments on current issues and developments. It is not a comprehensive analysis of the subject matter covered. It is not, and should not be construed as, accounting, legal, tax, or professional advice provided by Grant Thornton Advisors LLC. All relevant facts and circumstances, including the pertinent authoritative literature, need to be considered to arrive at conclusions that comply with matters addressed in this content.
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