From data to decisions: A CFO's guide

‍The ability to use data as an advantage  

Most companies are data-rich but insight-poor, meaning there is often a translation problem between data and action. Data is now abundant and is no longer a competitive advantage on its own; how a company uses and applies its data is its differentiator. The differentiating edge belongs to those companies that can turn the data they collect into information, assess it, and then translate it into action before their competitors.

The same applies to AI; however, it’s only as useful as the foundations of data on which it is built. Most organisations aren’t struggling to access AI; they’re struggling to extract actionable value from it. Despite heavy investment, many businesses are failing to translate their plans and ambitions for AI implementation into progress. There are a few core reasons why this is happening.

Today, CFOs are increasingly recognising that AI and data are essential to faster, smarter decision-making, and in business, momentum and moving at pace are essential. AI can create a genuine point of difference, but the value only comes from the combination of high-quality data, utilising the right platforms, and skilled people.  

‍

Start with solid foundations  

Companies that deploy AI without addressing underlying data quality issues do not just fail to gain value, but they also risk compounding existing fragmentation. Data structures and platforms are therefore foundational. Forward planning is essential as having the right platform fortoday is needed, but also one that can evolve with the pace of change, internaland external to the organisation.

Prior to onboarding any tools or systems, assess data quality and be absolutely clear in the issue you are using AI to solve, and work backwards from this.

Vendor lock-in and long-term contracts remain a persistent challenge for CFOs. In a rapidly evolving AI landscape, prioritising platforms that remain AI-agnostic during the AI race will enable future flexibility toadapt as the landscape shifts, often in real-time.  

‍

People are pivotal to transformation

Technology does not create transformation; people do. This is an aspect that is often overlooked. Internal capability and a mindset shift where everyone is brought along on the journey and just as important as the technology and data.

I have found there to be an advantage to keeping this in-house, instead of outsourcing, to bring the team along on the journey and toreach an AI-fluent end state by the time the project reaches the implementation phase. Similar to any technology project, AI tools should be developed and onboarded in conjunction with the end user. It’s sensible to test new AI projects and tools on pilot groups, making any required iterations before organisation-wide rollout.

In order to ensure adoption, a carefully devised training and feedback program needs to be implemented with a clear benefit to the enduser communicated to build the motivation to learn how to use the new system. In my experience, technology only creates value when the internal teams who will use it, understand it, trust it, and see the benefit to them as individuals.  

When people don’t understand AI and aren’t brought into the development process, they tend to focus on the negative aspects and resist it.This could include fearing job replacement, viewing AI as an increase in their workload to meet training requirements, and they often have doubts surrounding its suitability to their use case.  

Once the tool is developed and training is delivered, it’s important to have a continuous feedback loop. To do this, assign a dedicated person or team to manage the tool, akin to an internal 'Account Manager' who upskills on new updates, collates feedback and acts as a middle point between the business and the tool development team.

‍

CFOs are accountable for responsible AI  

AI will change how companies operate but caution is equally important. Opening AI access across all data without the necessary controls and governance in place could be catastrophic for individuals, brand reputation, a company’s valuation and profitability. Decisions made in relation to AI need to be deeply considered, and organisations are individually responsible and accountable for the use and impact of AI within their businesses and the tools that they create. As business leaders, it is important we are responsible with AI. This extends to relationships with vendors and partners, and companies should check the AI policies of the companies with which they enter contracts.  

‍

Prioritise AI investments strategically

Structured approaches to prioritising AI initiatives and assessing their risk profile should be implemented as part of an AI and wider technology implementation roadmap.

Finance teams are uniquely positioned to upskill in this area, helping organisations tackle gaps across markets.

‍

Embrace AI With a Strong Foundation

AI offers enormous opportunities, but must be managed with care. AI advancements are inevitable, and therefore it’s important that leadership embraces this change, whilst building responsible and robust foundations that support the now but also will endure in the future for their employees, customers, and the wider community.

‍

To close:      

AI will not wait for organisations to catch up. For CFOs, the challenge is not whether to adopt AI, but how to do so responsibly and strategically. CFOs need to lead by example with the implementation and use of AI as they set the tone for the rest of the company.  

Those who can build strong data foundations, invest with discipline, and bring their people with them will stand at the front and define the competitive standard for others to follow.