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1) Avoid rushing in: AI FOMO (fear of missing out) is real, and it can tempt teams to invest in AI solutions that sound good on paper but don’t necessarily add value to the business. Take time to understand which AI applications will bring the most value to your organization.
As Steve Suarez said: “If you’re not doing AI, it can feel like you’re missing out. And when people feel like they’re missing out, they throw money at things or try things that don’t really add value. That’s why the important thing right now with AI is to put a good strategy together and move ahead with a solid approach.”
2) Align on a cross-functional approach for prioritization, tech choices, compliance, and change management: The most successful AI strategies unify both people and processes.
3) Approach the transformation in steps: Define an AI strategy that aligns initiatives, sequencing, and investments with the organization’s strategic priorities. Take incremental steps, measure the results, and go forward at a pace that makes sense for your operations. That being said, don’t wait until your data is perfect (because it might never be).
4) Enable experimentation: Launch pilots, score quick wins, learn, and build momentum. Establish the tools, supports, and guardrails so your team can pilot AI initiatives effectively and with confidence.
5) Consider small language models and their immediate applications: Small language models cost about 1% of the cost to run a large language model and are often the best fit for what finance teams are attempting to achieve.
“Our language model doesn’t need to know what [everyone is] doing to solve some of our specific use cases, and we don’t have to send it outside of our organization. These models can work on the edge, work with us,” said Kossoras, noting smaller language models can be tailored to specific use cases, generating better outputs, reducing variances and AI “hallucinations,” as well as preventing other big organizations from being trained on your data.
6) Upskill your finance function colleagues to align with the requirements of an AI-enabled organization: Educate your finance team on data disciplines and foster skills that will enable them to leverage AI to contribute to enterprise value creation. Moreover, appoint and empower finance leaders to oversee components of your AI strategy.