AI for executives: how to reduce noise and get results

AI has dominated discussions, not just on the global tech scene, but in business in general. The impact of apps such as ChatGPT and DALL·E has been such that consumers are now fully aware of the wealth of possibilities large language models (LLM) And Generative AI offer. Indeed, according to research from AppRadar, new AI apps have been downloaded 23.6 million times by Android users since November. Over 700 AI startups have received a combined $7.1 billion in funding in the last three months alone. Very few technological innovations have managed to simultaneously capture the imagination of the worlds of technology, investors, businesses and consumers.

Given this varied interest and appetite, there are unprecedented opportunities for businesses to experiment with and adopt new AI-based solutions. However, the breadth of potential applications available – from customer service to supply chain finance – is such that policymakers and investors face the challenge of deciding which horses to support and when. After all, those who have recently committed resources to metaverse-adjacent technology or block chain only to find that real business value is far down the road may be reluctant to follow the latest hype.

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

I imagine that for many people, insurance companies would not be the first legacy business vertical that you would associate with the adoption of advanced technologies. However, the key for these institutions is that they can immediately see the logic and business value of AI solutions. For relatively little outlay and minimal risk, they can quickly and meaningfully transform large aspects of their business. And that’s the fundamental rule when we consider the best opportunities for LLMs to have a serious business impact: what can they use to give them a good return on investment with minimal risk?

Tried and tested against bleeding edge

For decision makers in large companies, LLMs (and AI in general) present an impressive number of options. Every business function can benefit from AI processing. The first thing to consider is the different maturity and development levels of each solution. It can be interesting to experiment with the latest innovations or create your own unique use cases, but this naturally comes with some risks. Often, off-the-shelf generation AI solutions (e.g., ChatGPT) have risks that render them unsuitable for certain enterprise use cases. Decision makers should view these capabilities as a toolkit available to accelerate their vision while ensuring the right technology is used based on the nature of each application.

For example, fintech startups have a long history of using data science to create sophisticated solutions that reduce the burden on financial departments and provide business leaders with real-time insights. Some of the latest advances have focused on AI-powered 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 the AI ​​running amok.

What are the main problems and inefficiencies in your business?

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

Businesses can always increase efficiency, improve customer experience and reduce pain points. Identifying where they are most needed will allow you to get the most ROI from your new AI solution. To do this, you need to look at your internal data as well as team and customer feedback. From there, you can narrow your search for AI solutions.

Start small and get the right AI infrastructure for your business

Any new technology comes with question marks as to exactly how it will fit into your existing business processes and infrastructure. The rush to jump on the AI ​​bandwagon will inevitably lead some companies to be derailed because they simply don’t have the tech stack or in-house expertise to effectively use their new solution.

AI systems will only work effectively if the data they use is smooth, complete and clean. In many organizations, this is simply not the case. Data management infrastructure is too often overlooked. Often, information is siloed within departments, platforms are unable to easily share or analyze data, and data collection and management policies are inconsistent. Bad data will lead to bad AI.

Starting small by using AI in a confined environment or use case will ensure that your infrastructure, policies, and processes are capable of being adopted at scale. It also has the benefit of enabling team and management buy-in more easily by reducing upfront expenses and potential disruptions. There are many specialized third parties that you can use in a targeted way to quickly launch these initiatives.

Don’t forget about human supervision

There is a severe data skills shortage that will impact the ability of businesses to effectively adopt AI tools. Master data training across an enterprise is needed to identify the most applicable solutions, properly monitor and verify their results, and use those systems most effectively. Businesses shouldn’t blindly trust what AI tells them; they need trained human supervision. This expertise cannot be held solely within the data team – it needs to be top-down and across all departments.

This model is what is often called the “human model”. on the loop model”, where systems do not depend on human input to perform their activity (such as the In looped systems did), but instead pushes human control further from the center of automated decision-making, playing a reviewing role to ensure the outcome is accurate and reliable.

What solutions should I bet on?

Currently, the most talked about new use cases for generative AI are in marketing, specifically copy and image generation. It is natural that many companies consider applying generation AI first here.

However, as we’ve discussed, any new technology attracts companies that dream up new use cases, which often results in existing use cases not advancing significantly. Our recommendation is to think about how AI can accelerate progress in resolving existing pain points, which often do not require the generative component (with its challenges of hallucination) but instead rely on the fundamental understanding of unstructured data.

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

Juan de Castro is COO of Cytore.