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September 05, 2024

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Five AI takeaways for your business

Following the release of ChatGPT in late 2022, AI (artificial intelligence) is dominating conversations in every sector. Rapid growth in AI technology has some observers of business and technology likening its impact to the commercialization of the internet in the 1990s. At the Women’s Business Enterprise National Council annual convention in March, members of the Financial Services Roundtable for Supplier Diversity (FSRSD) presented Amplify Through AI, a panel discussion about AI trends, predictions and risks.

As an attendee of the panel, here are my five main takeaways from the discussion.

AI and Its Results: AI specializes in three things — content generation, content augmentation and content summarization. AI has trained to prioritize returning an answer to achieve these results. This creates vulnerabilities when utilizing it. For example, AI pulls information from across the internet, but it’s not intelligent enough to validate the information. This creates a huge risk for users, as seen in several recent lawsuits. AI may misunderstand info. It also may “hallucinate”, an even more concerning phenomenon in which AI generates false information rather than returning a “no” answer. It can be nearly impossible to differentiate between fiction and reality in its answers. AI seems invincible, but businesses must work around its weaknesses. 

Adoption Policies and Cybersecurity: Panelists cautioned that AI adoption must be slow and mindful if businesses want to stay secure. Its development is changing day-to-day, even minute-to-minute. As government regulations are established, everyone must be ready to implement new plans and rules, especially around data privacy. Be proactive: Look at existing third-party solutions and consider adapting the data privacy and use policies for AI usage. Panelists also recommended that WBEs prioritize implementing Cybersecurity Model Level 1, the lowest level of security controls required to obtain a Cybersecurity Maturity Model Certification (CMMC).

It was also recommended that companies watch and study use cases. Learn from the implementation of others to avoid their mistakes.

Team Involvement with AI: Expect AI tools to become part of the team, whether officially or unofficially. Employees will likely use AI, so putting policies and training into place is key even if the company does not have an official AI implementation. Treat AI as a new employee - test its strengths and acknowledge its weaknesses.

One strategic approach to gaining momentum with AI is called “Human-in-the-Loop”. It’s a powerful combination of effort, wherein AI and employees collaborate. This synergistic approach leverages AI’s strengths in automation and data processing to execute tasks efficiently while ensuring human oversight to maintain quality and address nuanced complexities.

Job Changes: As AI technology advances, jobs are rapidly evolving to adapt to AI capabilities and efficiencies. New jobs will be created, while some existing jobs will become obsolete. One such new opportunity is the creation of the Prompt Engineer, a vital new role. A Prompt Engineer specializes in formulating precise queries to get the desired answers from AI. It allows companies to extract optimal responses and results from AI systems. AI programs speak their own language, and it’s important for employees to learn how to speak it as well.

AI tools are being developed to take over repetitive work, so jobs related to such might transform into new roles. For example, AI can streamline risk assessments without companies having to fill out hundreds of questions that may or may not yield results.

Customer Service: One business function that’s rapidly being transformed by AI is customer service. The customer experience has changed dramatically and is anticipated to be much more organized with the help of AI. According to a report from Servion, it’s estimated that AI will power 95% of all customer interactions by 2025. This includes customer support, marketing and sales processes. AI has the advantage of never sleeping or taking vacations. It can provide personalized conversation and recommendations 24/7.

The Financial Services Roundtable for Supplier Diversity (FSRSD) is an industry organization comprised of regional, national and global financial services companies with a formalized supplier diversity initiative. The FSRSD’s primary goal and purpose is to advance the inclusion of diverse firms in the financial services industry through Supplier Diversity. Learn more at fsrsd.org.

AI Terminology

Algorithm Bias:

Definition: Algorithm bias occurs when an AI system produces results that are systematically prejudiced due to erroneous assumptions in the machine learning process. It can lead to unfair or discriminatory outcomes.

Source: MIT Technology Review

Deep Learning:

Definition: Deep learning is a specialized field of machine learning inspired by the structure and function of the human brain, known as artificial neural networks.

Source: Microsoft Azure

Generative AI:

Definition: AI that can create original content—such as text, images, video, audio or software code—in response to a user’s prompt or request.

Source: IBM

Machine Learning (ML):

Definition: Machine learning is a subset of artificial intelligence where algorithms enable systems to learn from data and improve over time without explicit programming.

 Source: IBM

Reinforcement Learning:

Definition: Reinforcement learning is an area of machine learning where an agent learns to make decisions by interacting with its environment. It learns to achieve a goal through trial and error, receiving feedback in the form of rewards or penalties.

Source: OpenAI