Winning the Decision-making Game with GenAI: Preparing Next Generation of Business Leaders

“With great power comes great responsibility.” Popularized by your friendly neighborhood Spider-Man, this phrase isn’t limited to just superheroes. Enterprises around the world must, too, acknowledge the power of technology and act responsibly. 

GenAI is set to become a $1.3 trillion market by
2032, growing at 42% over the next 10 years.

Source: June 2023, Bloomberg Intelligence

The emergence of generative artificial intelligence (GenAI) saw numerous organizations joining the tech craze. With advancements in large language models that power OpenAI’s ChatGPT, Google’s Gemini, and Meta AI, GenAI helped organizations achieve quicker results. Modern technology performed even better when equipped with additional domain expertise available. It is interesting to see how GenAI is making its way into the foundation of various organizations and unlocking exceptional value. But where does it stand on the trust ratio for the new-age business leaders?   

A report by Deloitte’s State of Generative AI in the Enterprise found that most global enterprises (72%) have trusted all types of AI since late 2022. However, many leaders continue to be apprehensive about the GenAI tools. Around 33% of the business leaders “lack confidence” in GenAI results. The survey was conducted between January and February 2024 among 1,982 director- and C-suite-level executives in six industries and six countries. But GenAI isn’t just a fad. The same report also stated that 60% of the surveyed leaders had found a balance between integrating GenAI within the organization and implementing processes to mitigate potential risks.

One reason for the underlying mistrust is that innovation leaders focus on being ‘the first’ rather than taking the ‘right, the first time’ approach. GenAI models created without proper training can result in incorrect information and misleading outcomes, forcing users to perform manual checks. These inconsistent outputs, termed AI hallucinations, occur when irrelevant or incorrect data is incorporated into the AI model, leading to inaccurate predictions or decisions.

Wouldn’t it be disheartening if you ask for some facts, and the AI tool confidently delivers a factually incorrect answer?  

AI hallucinations can manifest in various ways. AI models can forecast an event that’s unlikely to happen. For instance, AI makes an inaccurate weather forecast or predicts torrential rains when the skies are clear.

Another form of false positive is when an AI model mistakenly identifies something as a threat when it isn’t. For example, AI flags a legitimate transaction as fraudulent, causing unnecessary distress to the users.

All the outcomes don’t necessarily have to result in AI hallucinations. When training an AI model, using relevant and specific data aligned to the intended task is crucial. This knowledge empowers organizations to train their AI tools effectively, enhancing their ability to make accurate predictions.

Making GenAI More Reliable with Newgen

The right technology partner can ensure a trustworthy AI and GenAI model to scale opportunities and business growth. Newgen, with its domain experts, is here to enhance business decision-making and provide reassurance in GenAI’s reliability.

Robust Training Data

• Relevant Datasets: Ensures datasets are comprehensive, up-to-date, and relevant to the application
• Quality Assurance: Assures accuracy and relevance, reducing the likelihood of incorrect outputs
• Domain-specific Data: Incorporates domain-specific knowledge to enhance the model’s contextual understanding and accuracy

Scenario Testing and Validation

• Extensive Testing: Conducts extensive scenario testing before deployment to simulate various conditions and edge cases
• Edge Case Analysis: Includes unlikely but possible scenarios to test the model’s robustness
• Benchmarking: Benchmarks against industry standards to ensure competitive performance
• Validation Processes: Implements thorough validation processes to identify potential issues early and ensure model accuracy

Monitoring and Feedback Loops:

• Ongoing Monitoring: Monitors GenAI applications Continuously post-deployment to track performance and detect anomalies
• Feedback Mechanisms: Implements feedback loops for users to report inaccuracies or unexpected results
• Performance Metrics: Reviews performance metrics regularly to identify areas for enhancement

Regular Updates and Improvements

• Frequent Updates: Updates GenAI applications regularly to incorporate the latest advancements and improvements
• Incorporate New Data: Learns from new data and scenarios continuously to improve model accuracy
• Advanced Tools: Leverages advanced tools for sophisticated benchmarking and optimization
• Iterative Improvements: Follows an iterative process to ensure models remain cutting-edge and deliver high accuracy and reliability

Explainability and Transparency

• Transparent Decision-making: Ensures transparency in AI decision-making to build user trust
• Explainability Functions: Includes explainability functions in GenAI applications to allow users to understand the rationale behind AI outputs

User Training and Education

• Awareness of Limitations: Ensures users are well-equipped to leverage GenAI capabilities while understanding its limitations
• Ongoing Support: Offers ongoing support and resources to keep users informed about updates and best practices

With the right approach, GenAI’s potential can be harnessed to drive innovation, efficiency, and growth. Together, we can pave the way for a trustworthy future.