The best products solve a concrete need. They fill a gap that its users see day in and day out. But solving problems with AI requires product leaders to identify the real need, translate it into actionable information, and constantly validate assumptions and understanding. At a time when technology promises businesses autonomy, human control still matters, especially in finance.
On the latest episode of the “What’s the BUZZ?” podcast, Ariana Smetana (CEO of AccelIQ.digital) joined me and shared how she and her team are accelerating document processing in finance with the help of AI. Here’s what we talked about…
Solving Real Problems Instead of Chasing the AI Hype
One of the biggest mistakes organizations make with AI is starting with the technology instead of the problem. There’s often a long list of exciting use cases, ideas brainstormed across teams, and ambitious strategies, but many of them never translate into real impact. What actually works is far simpler and far more practical.
Start with a clear, recurring pain point. In finance, that pain point is often buried in document processing and reporting. Even in large, sophisticated organizations, teams still manually pull data into spreadsheets, stitch together reports, and spend hours validating numbers. It’s slow, error-prone, and reactive. The opportunity is to fix what’s consistently broken.
When you focus on a specific bottleneck, such as financial data ingestion or report generation, you create immediate value. You also build trust internally because people can see the improvement in their day-to-day work. AI becomes meaningful when it removes friction from real workflows. Not when it adds another layer of complexity.
NEW BESTSELLER — The HUMAN Agentic AI Edge
Organizations are racing to deploy Agentic AI, yet few are ready for the risks that emerge when employees use AI without structure, standards, or oversight.
The HUMAN Agentic AI Edge offers leaders a practical blueprint for building accountable AI-ready teams that consistently produce high-quality results. Drawing on real-world knowledge and insights from interviews with more than 50 AI leaders and experts, Andreas Welsch shows how to combine human judgment with Agentic AI capabilities to achieve the performance many organizations expect but rarely deliver. This book prepares you to shape the next generation of AI-ready teams delivering high-quality results with high accountability.
There’s a common concern in finance: AI isn’t always right. And in a field where accuracy is non-negotiable, that concern is valid. That’s why AI systems still need humans to remain in control. Instead of relying entirely on probabilistic outputs, the most effective approach is layered. Data is first validated. Then, calculations are handled deterministically by using reliable mathematical models. Only after that does AI step in to summarize, explain, or generate insights.
This structure ensures that critical numbers aren’t “guessed” by a model. It also gives finance professionals visibility into where errors might occur and the ability to correct them before decisions are made. AI should enhance expertise and enable professionals to guide the process, from selecting inputs, validating outputs, and shaping how results are generated, thereby building confidence in the system. Over time, that confidence becomes trust. At the end of the day, trust enables deeper adoption.
The Shift From Manual Work to Strategic Thinking
What’s happening right now in finance is a shift in how work gets done. Traditionally, finance teams spend a large portion of their time gathering data, cleaning it, and preparing reports. By the time insights are ready, they’re already looking at the past. By accelerating data processing and enabling on-demand reporting, finance professionals can move faster and focus on what actually matters: interpreting results, identifying risks, and guiding business decisions.
But this shift doesn’t happen overnight. It starts with experimentation. Getting hands-on with tools, testing small use cases, and learning what works and what doesn’t. There’s also an important mindset change. Instead of waiting for perfect, fully automated systems, teams need to embrace gradual progress. Start with tools that support your current workflow. Build familiarity. Then expand into more advanced capabilities, such as automation or agents.
Summary
AI in finance becomes powerful when it’s grounded in real problems, designed with human control in mind, and used to shift teams toward more strategic work.
Start by identifying your biggest bottlenecks. Focus on tools that enhance accuracy and transparency, not just speed. And most importantly, get hands-on, because understanding AI comes from using it. The organizations that move forward will be the ones who apply it with clarity and purpose.
Equip your team with the knowledge and skills to leverage Agentic AI effectively. Book a consultation or workshop to accelerate your company’s AI adoption.
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The accuracy + trust piece is what holds most finance leaders back from committing to AI tools. Great breakdown of the three-step approach.