Wealth managers can miss out on the promise of AI if they don't get the right foundations in place, the author of this article argues.

The following article by Caro Ames (pictured below), principal at commercial data solutions provider JMAN Group, explores the four AI integration missteps that could impact your bottom line. 

Caro Ames

The editors are pleased to share this content; the usual editorial disclaimers apply to views of guest writers. To respond and become involved in the conversation, email tom.burroughes@wealthbriefing.com and amanda.cheesley@clearviewpublishing.com.
 

As funds and portfolio companies navigate the rapidly evolving technology landscape, AI presents an exciting opportunity to drive value creation and competitive advantage.

However, it can be easy for operators and management teams to get swept up by the transformative potential of AI and fail to realise real value if the right foundations are not put in place. Integrating AI into complex business processes is not straightforward, and the rush to embrace this transformative technology can lead to missteps that not only hinder the potential of AI adoption in the long-term but also impact your bottom line. 

Here we outline four critical mistakes to avoid:

1. Absence of a clear AI strategy aligned to business objectives
The art of the possible with AI is expansive, and the landscape is rapidly evolving. Understanding where to start, balancing ambition with feasibility and remaining ruthlessly focused on delivering business value is critical to achieving success. This challenge is amplified in the private equity context where the diversity of portfolio companies prevents funds from having a single unified strategy for AI adoption. 

However, regardless of business model or sector, successful adoption requires a consistent approach driven by clear objectives that align with business strategy and a value creation plan. Without these, initiatives can suffer from cost and time overruns, a lack of measurable business impact, and a complete loss of trust as a result. To prevent this and build a playbook for initiating a successful AI strategy, funds should set an ambitious vision, identify high-value use cases that address critical business challenges, consider the route to adoption, and put mechanisms in place to measure impact. 

This should be done in collaboration with management teams to ensure alignment with the value creation plan, future business users to ensure successful adoption, and AI experts to mitigate risks, and maximise return on investment. 

The investment required to develop a tailored AI strategy doesn’t have to be significant if well structured and focused. With the above considerations, for example, we lead a three-hour workshop with a mid-cap business to prioritise AI use cases and align stakeholders on the guiding principles for managing the effective development and implementation of their AI strategy. 

When companies abandon their AI initiatives, it is often due to unforeseen costs, risks, data security and privacy issues; funds must find a way to rapidly develop scalable AI strategies across their portfolios which meet the individual needs of their portfolio companies, maximise value, and mitigate risks.

2. Data foundations seen as a barrier to adoption
Historically, high-quality data has been critical to successful AI adoption. The rise of GenAI has lowered the barrier to entry for businesses, and a properly thought-through AI strategy cuts through the buzzwords to enable a focus on the right foundations for implementation. 

Even though we still see more under-investment rather than over-investment in data foundations, a full-scale data transformation is not always required. Depending on the ambition and level of technology maturity, there are plenty of opportunities for businesses to realise value from AI in the short term. Considering how to build the right data foundations in the context of the long-term value of data and AI can help to build the business case for change, and ensure the right level of investment, targeted in the right place. 

At JMAN we have worked with organisations that are realising value from a wider range of AI applications from the roll out of co-pilot, to establishing a customised AI knowledge management tool, and to highly bespoke development and deployment of ML model for churn prediction; all requiring vastly different levels of data investment upfront.

It is critical to take your time to understand exactly what state your data infrastructure is in and which available technology platforms will meet your requirements now, and in the future. Conducting a foundational data and infrastructure assessment, set against the ambitions of the company, should be the very first step of any meaningful data and AI strategy. Investing in the right platform for data management, collection, and analysis is fundamental.

3. Missing governance at the get-go
Whilst AI was historically accessible solely to technology experts, changes in the technology landscape have lowered the barrier to access. This scales the risk from the actions of a small number of individuals who specialise in data and AI to a cross-organisational level. Whilst the AI regulatory landscape is still evolving, the impact of lack of governance over responsible AI adoption could now result in significant reputational, operational and financial damage. 

Risks are not limited to large tech firms; a private equity example of this involved an AI recruiting agent that discriminated on both gender and age, resulting in the portfolio company paying to settle a public lawsuit and suffering huge reputational damage. 

Robust oversight is essential, beginning with clear policies and guidance on AI integration, alongside clear governance mechanisms across people and processes. Rather than being left as an afterthought, getting these structures in place should be established as a strategic priority from the outset at any organisation considering adopting AI to ensure alignment, compliance, and long-term value protection. 

4. Failure to build the right internal capability  
AI adoption requires a change in ways of working, resources and skills within a business. There is a misconception that to correctly implement AI, organisations must hire a team of highly technical individuals, over-educating the business, and consider an organisation-wide change program. Firms are often already behind on this type of internal upskilling from a long-standing under-investment in data literacy, and the risk is that the gap becomes even wider. 

Whether it’s someone in the investment team or within a finance function, the ability to understand, interpret, and engage with AI tooling is now essential for making stronger, collective decisions.

Consideration over exactly the types of skills and capability needed will likely result in a combination of hiring, upskilling, bringing in suppliers and procuring products to match each portfolio company’s maturity and scale of ambition. Without building the right levels of capability within the business, there is the risk of failure to drive adoption and opening the business to risks associated with misuse. Pushing internal growth, knowledge and upskilling resources is therefore becoming a must.

Beyond building a robust strategy and fostering the right internal capabilities, we recommend enabling employees to experiment with small pilot projects. This should be done in a way that has clear key performance indicators for each initiative and frequent reviews conducted to evaluate impact. These projects will provide valuable insights that enable continuous improvement of strategy and implementation, demonstrate the value of investment in AI and support scaling throughout the portfolio companies in the long term. To support your progress, it's always advisable to consider partnering with a specialist data consultancy with the experience that can help support your goals.