The following is a guest post from Ashok Manthena, chief innovator at ChatFin. Opinions are the author’s own.
In the finance world, the promise of AI is almost like a new gold rush. There’s a widespread belief that artificial intelligence will eventually revolutionize our workplaces, making everything from accounting to data analysis to regulatory compliance faster, easier and more accurate. However, while the long-term picture might be clear, the immediate future is full of questions.
The journey of incorporating AI into finance functions often begins at a crossroads, contemplating the strategic approach to adoption. On one side, there are sizable challenges within finance departments that AI could potentially solve, but these are often complex and deeply integrated into existing systems. On the other, there are smaller, nagging issues that, while less significant, are easier to manage and might serve as good entry points for AI solutions.
Start with a pilot
Based on what I’ve learned from various AI deployments, every AI initiative should begin with a pilot, regardless of the tool or size of the use case.
Ashok Manthena
Permission granted by Ashok Manthena
Successful pilots typically tackle small but crucial issues and demonstrate potential solutions in action. This approach isn’t about calculating ROI from the get-go; think of it more as a feasibility study and a learning opportunity.
A pilot project serves multiple purposes:
- It tests the technology in a controlled, manageable environment.
- It educates your team about AI capabilities and limitations.
- It adjusts expectations and dispels many of the myths surrounding AI, particularly the fear of job replacement and security
Choose the right starting point
When contemplating the initial steps for integrating AI into finance operations, the decision of whether to start with the most daunting challenges or to focus on smaller, more manageable issues is not merely tactical — it’s strategic. Opting to address less significant pain points might initially seem less impactful in terms of ROI. However, these smaller victories play a pivotal role in the broader AI adoption journey. They not only build trust and credibility around AI technologies within the organization but also establish a solid foundation for taking on more complex challenges as confidence and capabilities grow.
Take, for example, the common yet often overlooked issue of time-consuming data retrieval processes in finance departments. On the surface, improving the speed of data access may appear to be a minor fix. However, if an AI solution could streamline these processes — reducing data retrieval times from several hours to just a few minutes — the implications would be substantial. Such an enhancement in data accessibility can significantly boost the productivity of the entire finance team.
This improvement goes beyond mere time-saving. By liberating finance professionals from tedious data-gathering tasks, AI allows them to dedicate more of their day to higher-value activities such as analysis, strategic planning, and decision support. This shift from administrative drudgery to strategic engagement not only enhances job satisfaction but also contributes to more insightful and impactful financial management.
Moreover, these early successes with AI create a ripple effect throughout the organization. As team members witness firsthand the benefits of AI, skepticism turns into advocacy. This cultural shift is critical as it facilitates smoother implementation of AI in more ambitious projects. Each small win accumulates, building a case for AI’s efficacy and encouraging broader organizational buy-in.
Balance user needs and change management
As we move from pilot to full deployment, the mindset shifts from exploration to strategic implementation. At this stage, it’s crucial to list all pain points, assessing them by potential time savings and effort required. However, not all solutions that are easy to implement are about cutting down time. Some are about enhancing accuracy, others about improving data accessibility.
Picking the right use cases also involves considering how easy they are to manage from a change perspective. Change management is a significant barrier, as there’s often resistance from users worried about AI replacing their jobs. It’s essential to select projects that not only add value but also are likely to receive user buy-in and adoption
Think big while starting small
Even when you start small, you need to think big — not just in terms of potential ROI, but also in terms of change management, human resistance to change, leadership alignment and IT alignment.
Here are a few steps to ensure that starting small effectively leads to thinking big:
- Communicate clearly. From the outset, communicate the benefits of AI not just from a business perspective but in terms of personal impact on team members. How will AI make their jobs easier, more interesting or more impactful?
- Align AI goals with business objectives. Ensure every AI project aligns closely with broader business goals. This helps in securing executive support and aligning various departments towards a common goal.
- Foster a culture of innovation. Encourage a workplace culture that sees technological adaptation as an opportunity for growth and learning, rather than a threat. Celebrate small victories loudly and share success stories to build momentum.
- Continuous learning and adaptation. Implementing AI is not a one-time event but a continuous process. As AI technologies evolve, so should your strategies. Keep learning from new deployments and adapt based on both successes and failures.
Implementing AI in finance does not have to be an overwhelming revolution. By starting small — with carefully chosen projects that demonstrate clear value and are manageable in scope — you can build the foundation for a much larger transformation. Each small project that successfully leverages AI proves its value and sets the stage for more ambitious projects, ensuring that AI becomes a transformative tool for your financial operations. Think big about the potential of AI, but start small to manage risks and learn as you grow
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