3 Considerations For Leaders When Using Generative AI In Business

Artificial intelligence (AI) is transforming the business landscape, revolutionizing workflows, and changing how we analyze data and access information. According to a recent survey , the AI market is projected to reach a staggering $407 billion by 2027, experiencing substantial growth from its estimated $87 billion revenue in 2022. In addition, a recent Forbes Advisor survey notes that 64% of businesses believe AI will help increase overall productivity.

Yet when leveraging AI and machine learning (ML) to derive insights from data, these technologies are not the only solutions, and it’s important to remember results can be biased or skewed. While AI and ML offer tremendous potential for solving complex problems and making driven decisions, they come with inherent limitations and challenges that require consideration.

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Quality outputs come from quality inputs.

First and foremost, AI and ML algorithms are adept at processing large volumes of data and identifying patterns. For the primary language learning models, the datasets are vast, covering multiple points of view. There can be biases in the source data, but the preponderance of data trends toward a balanced view. For private AI, however, its effectiveness is directly proportional to the quality of training data. This is where human intervention becomes crucial, as AI cannot replicate the depth of human experience and judgment.

While AI, especially chat-based AI and LLMs, excel at processing vast datasets, it doesn’t always have the capacity for context-sensitive analysis and the nuanced understanding of domain-specific knowledge that human experts bring. Poor-quality or biased data can lead to skewed results and inaccurate conclusions, compromising the reliability of AI-driven insights. For example, decisions based on skewed data can result in unreliable products that do not meet customer and market needs, which can diminish trust and confidence in the business. Poor quality data can also cause operational delays and supply chain disruptions, wasting valuable time, effort and resources. This underscores the significance of data quality management, human intervention and validation processes in ensuring the integrity of the data used for training and inference.

Biases inherent in data and algorithms can perpetuate and exacerbate existing errors. Unchecked AI systems can inadvertently reinforce biases present in historical data or reflect the biases of their developers, leading to inaccuracies and mistakes. This can negatively impact businesses in a few ways. Customers who lose trust in the business may seek out competitors for more reliable solutions, leading to a decrease in market share. Businesses may also need to invest substantial resources to correct these mistakes, which can be costly and time-consuming.

Recognizing and addressing biases in AI models requires careful attention and deliberate efforts to promote fairness, equity and inclusivity in algorithmic decision-making processes. Without more expansive ethical guidelines and oversight, organizational policies are critical to minimizing or eliminating errors and biases. Designing AI to evolve continuously by integrating new, quality data and user feedback retains its agility and rapidly adapts to fluctuating market conditions and complex, changing project requirements.

Clarity commands confidence.

While AI models can provide predictions or recommendations, they often require more transparency and interpretability for stakeholders to fully understand and trust their outputs. Every piece of data holds a context for consideration. Lack of transparency can impede robust accountability and make identifying and rectifying errors or biases in AI-driven decisions challenging. Therefore, there is a pressing need for greater transparency and explainability in AI systems to foster trust and facilitate human oversight and intervention when necessary.

Businesses should provide documentation and be open about the type of AI algorithms used, how the systems process data, how they reach decisions and potential biases, and they should discuss what users can expect from their interactions. This helps to foster trust and facilitate human oversight and intervention when necessary.

In closing, while AI and ML have been around for years, generative AI has captured the public’s attention. A recent social media post by author Joanna Maciejewska underscores a glaring irony: People are now turning over the creation and creative expression process to a machine while spending time and attention on mundane tasks. On the business side, this might include analytics insights, measurement, conversion analysis, dashboard creation and maximization, advertising data insights, persona build-out, customer journey creation and optimization. The argument Maciejewska and others are making is that these should be reversed. AI should handle the hectic and manual tasks of business while we put our time and efforts into the creation, application and insight process.

AI shouldn’t take over the entire process but can be a valuable starting point. From there, the process requires human interaction for keen insights, unique and informed perspectives and experience-based decision-making. So, when looking into AI for your business, I encourage you to consider solutions that include human oversight and expertise. By approaching AI with this critical and nuanced perspective, organizations can mitigate risks and harness its potential for long-term success.