Technology AI for executives: how to cut through the noise...

AI for executives: how to cut through the noise and get results

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AI has dominated the discussions not only in the global tech scene, but in the business world as a whole. The impact of applications like ChatGPT and DALL·E is so great that consumers are now fully aware of the wealth of possibilities offered by large language models (LLMs) and generative AI. According to research by AppRadar, new AI apps have been downloaded 23.6 million times by Android users since November. In the past three months alone, more than 700 AI startups have received a total of $7.1 billion in funding. Very few technical innovations have managed to simultaneously capture the imagination of the technology, investor, business and consumer worlds.

Given this broad interest and appetite, there are unprecedented opportunities for companies to experiment with and adopt new AI-driven solutions. However, the potential applications available — everything from customer service to supply chain finance — are so broad that decision makers and investors alike are faced with the challenge of deciding which horses to support and when. After all, those who have recently committed resources to metaverse-adjacent technology or blockchain only to find that true business value is still a long way off may be reluctant to follow the latest hype.

The reality, of course, is that while ChatGPT may have brought AI into the mainstream, generative AI is really just the latest advancement in a plethora of data science-driven applications. For example, the insurtech industry has been transformed over the last decade by data solutions that have automated processes, digitally processed risk, increased volumes, and ultimately improved the customer experience.

I imagine that for many people, insurance is not the first traditional industry you would associate with embracing advanced technology. However, the key for these institutions is that they can immediately see the logic and business value of AI solutions. For a relatively small outlay and minimal risk, they can quickly and tangibly transform major aspects of their business. And that’s the fundamental rule when looking at the best opportunities for LLMs to make a serious impact on businesses: what can they use that will give them good ROI with minimal risk?

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Tried and tested vs. blood curdling

For enterprise decision makers, LLMs (and AI in general) present a dizzying array of options. Every single business function can get the AI ​​treatment. The first thing to consider is the different maturity and development levels of each solution. It can be attractive to experiment with the latest innovation or create your own unique use cases, but of course this also entails risks. Often, out-of-the-box gen AI solutions (e.g. ChatGPT) carry risks that make them useless for certain business applications. Decision makers should view these capabilities as a toolkit available to accelerate their vision while ensuring the right technology is used depending on the nature of each application.

Fintech startups, for example, have a long track record of using data science to create cutting-edge solutions that offload finance departments and provide business leaders with real-time insights. Some of the latest developments have focused on AI-assisted cash flow analysis and forecasting. Given the experience of many of these service providers, their products are likely to be more tried and tested, further reducing the risk of AI going haywire.

Where are your key business pain points and inefficiencies?

Ultimately, the best approach is to start with the problem rather than the exciting new AI solution. We recommend taking new technologies as building blocks to create enterprise-ready solutions that address real tangible pain points.

Companies can always further increase efficiency, improve customer experiences and reduce pain points. Identifying where they are most needed will help you get the best ROI on your new AI solution. To do that, you need to look at your internal data as well as feedback from teams and customers. From there you can narrow down your search for AI solutions.

Start small and get the right AI infrastructure for your business

Every new technology raises questions about exactly how it will integrate with your existing business processes and infrastructure. The rush to get on the AI ​​bandwagon will inevitably derail some companies because they simply don’t have the technical stack or in-house expertise to effectively use their new solution.

AI systems only work effectively if the data they use is free-flowing, complete and clean. In many organizations this is simply not the case. Data management infrastructure is too often overlooked. Information is often isolated within departments, platforms are unable to easily share or analyze data, and data collection and management policies are inconsistent. Bad data leads to bad AI.

By starting small with AI in a closed setting or use case, you can be confident that your infrastructure, policies and processes can be applied at scale. It also has the benefit of facilitating team and management buy-in by reducing initial costs and potential disruptions. There are many specialized third parties that you can use to get these initiatives up and running quickly.

Don’t forget about human supervision

There is a serious data skills shortage that will impact companies’ ability to use AI tools effectively. Basic understanding of data across an enterprise is required to identify the most applicable solutions, properly monitor and verify their output, and use these systems most effectively. Companies should not blindly trust what AI tells them; they need expert human supervision. This expertise can’t just be kept in the data team – it needs to be present from the top down and in every department.

This model is what is often referred to as the “human.” on the loop” model, where systems do not rely on human input to perform their activity (such as traditional “human in the loop” systems did), but instead pushed human control further away from the center of automated decision-making and played a controlling role in ensuring that the output is accurate and reliable.

Which solutions should I bet on?

Currently, the hottest new use cases for generative AI are within marketing, particularly copy and image generation. It makes sense that many companies here first look at applying gen-AI.

However, as we discussed, any new technology attracts companies that dream of new use cases, often resulting in existing use cases failing to make significant progress. Our recommendation is to think about how AI can accelerate progress in solving existing pain points, which often don’t require the generative component (with its challenges of hallucination), relying instead on the fundamental understanding of unstructured data.

Remember, identifying the best AI solution for your business is just the first step. You need the infrastructure, buy-in, in-house expertise and checks and balances to get the most out of it.

Juan de Castro is COO of Cytora.

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