The AI Chronicles – Navigating Economic Uncertainties with AI (Part 1)

Welcome to our first blog series of the Blackstraw AI Chronicles titled ‘Navigating Economic Uncertainties with AI’. In this blog, we will delve into the topic of how CFOs and finance professionals can leverage and adopt Artificial Intelligence (AI) to achieve their business goals. As the business landscape continues to evolve, the role of AI becomes increasingly crucial. This is the first part of a 2-part blog series where we will explore the changing landscape of AI adoption and provide valuable insights on how CFOs can successfully navigate this terrain. Here is a discussion between Atul Arya, CEO & Founder of Blackstraw, and Siegfried De Smedt, President, Blackstraw, who bring a wealth of experience as an avid advocate of AI and accomplished business leaders.   [Discussion Begins]    Atul: Let’s start by discussing the changes and challenges in the AI landscape. From your perspective, Siegfried, do you think AI adoption has become more accessible and accepted, or does it still present hurdles for businesses?   Siegfried: Well, Atul, I have been a heavy user of machine learning/ artificial intelligence for a very long time. My focus was always on change and automation of workflow processes. However, I’ve witnessed significant advancements in AI adoption over the years. The resistance to change remains a common challenge, not only among employees but also in the C-suite itself. Traditional business leaders can be hesitant to embrace automation through innovation. Additionally, many businesses struggle with aligning AI initiatives with their overall objectives, leading to scattered and small-scale implementations. However, what has changed is the speed of AI development. Today, AI solutions are developed at the speed of light, thanks to improved hardware capacity, processing speeds, and interoperability. The strength of algorithms has grown exponentially, enabling rapid deployment and scalability. So, while challenges persist, the capability and power of AI have transformed significantly.   Atul: Indeed, the advancements in AI have been remarkable. Now, let’s focus on the role of CFOs in AI adoption. Why should CFOs get involved in leveraging AI for their organizations?   Siegfried: CFOs play a critical role in driving innovation and optimizing working capital. Economic and political instability, rising costs, and the need for optimization create a necessity for CFOs to explore AI adoption. By educating themselves on AI capabilities and successful use cases, CFOs can understand how AI can deliver a positive return on investment for their organizations. To be effective, CFOs should act as executive sponsors, driving initiatives and encouraging the organization to persist through challenges. It’s crucial to maintain close links with both internal teams and external experts to leverage their expertise and gain unbiased insights. Ultimately, embracing AI can positively impact an organization’s valuation, which is a top priority for CFOs.   Atul: That’s insightful, Siegfried. CFOs can truly drive change and innovation within their organizations. Now, let’s discuss the practical steps for CFOs to get involved with AI adoption. How can they transition from being external parties to actively participating in the AI adoption process?   Siegfried: Firstly, CFOs need to educate themselves about AI and its potential. This can be done through various channels, such as exploring successful AI use cases and understanding how AI aligns with their business objectives. Secondly, CFOs should focus on creating “bullet train” initiatives—high-speed projects that prioritize short-term return on investment. By leveraging AI solutions, CFOs can optimize working capital and accelerate innovation. Lastly, CFOs must encourage the organization to persist through challenges and setbacks. This includes providing the necessary capital and support, collaborating closely with internal teams, and maintaining strong relationships with external partners for expertise.    Atul: Thank you for that insightful discussion, Siegfried. Now let’s shift our focus to how we can humanize AI for business leaders who may not have a technical background. It’s crucial to make AI real and relatable to them. The key is to articulate the problem they have and not worry about the technology that will solve it. By clearly defining their pain points and challenges, business leaders can seek out the right partners, whether internal or external, who can provide solutions tailored to their specific needs. The role of true partners is to understand the problems and determine whether AI or any other technology is the appropriate solution. It’s important to avoid approaching it as a solution in search of a problem. Instead, the focus should be on solving real-world challenges and leveraging technology, including generative algorithms, when they align with the problem at hand.   Siegfried: That’s a great perspective, Atul. Understanding the problems and seeking solutions that match those needs is crucial. Speaking of generative models, how do you see them driving ROI for CFOs and organizations? Can you provide some examples where generative algorithms have delivered ROI within a relatively short timeframe?   Atul: While generative algorithms, such as large language models, hold tremendous power, it’s essential to approach them by identifying specific use cases and the problems they can solve. Rather than searching for ways to apply generative algorithms, it’s more effective to focus on the challenges at hand and explore how generative algorithms can provide solutions. For instance, in the financial world, generative algorithms can be employed to automate copywriting tasks or assist in creating personalized customer communications. In marketing, these algorithms can generate engaging content, leading to increased customer engagement and conversions. Additionally, generative algorithms can streamline processes by automating email composition, as seen in Gmail’s feature that suggests ready-made responses based on the context of the conversation. The key takeaway is that generative algorithms have potential applications in various domains. By first identifying the problems faced by CFOs and their organizations, we can then explore how generative algorithms can be leveraged to drive ROI within a specific timeframe.   Siegfried: That’s a valuable insight, Atul. It’s important to approach generative algorithms as a tool within the broader toolkit of solutions, rather than searching for problems to fit the technology. By understanding the business case and the specific challenges, CFOs and organizations can unlock the full potential of generative algorithms and other AI technologies.   Atul: Absolutely, Siegfried. It’s about understanding the power of AI and applying it in a way that truly addresses business needs. By focusing on real problems, engaging in meaningful conversations, and collaborating with true partners, CFOs can drive successful AI adoption and achieve tangible ROI for their organizations. The discussion between Atul Arya and Siegfried De Smedt will continue in the second blog of the Blackstraw AI Chronicles series. Stay tuned as we dive deeper into the intricacies of navigating economic uncertainties with AI.   

Conclusion:

In conclusion, this blog has delved into the ever-evolving AI landscape, highlighting the crucial role of CFOs in driving AI adoption within their organizations. We have explored practical steps for CFOs to actively engage in this process, emphasizing the significance of understanding business challenges, finding suitable partners, and implementing AI technologies that align with specific needs.  By embracing a problem-centric approach and harnessing the potential of AI, CFOs can effectively guide their organizations through successful digital transformations, ultimately fostering sustainable growth in today’s dynamic business environment.  In the upcoming second part of this blog series, we will delve into further insights. Topics to be covered include the key learnings during AI adoption, strategies for implementing change management within the organization, methods for identifying appropriate use cases for AI, and the initial steps an organization should take when embarking on an AI project.