ENSURING RELIABLE AI ADOPTION FOR YOUR BUSINESS WITH ‘CONTROL’

Why are Generative AI and Large Language Models (LLMs) making waves across industries? While they’re reshaping processes and customer interactions, they also come with their own set of challenges – particularly when it comes to keeping their outputs in check. Without proper governance, AI responses can veer off course, potentially damaging your brand. Consider what happens when AI provides inaccurate or even harmful information – everything unravels like a domino effect. This is where controlling the output becomes critical. Our Govern Guide and Control (GGC) framework addresses this issue, serving as the cornerstone for responsible and reliable AI deployment.

What does ‘Control Your AI’ mean?

The Control element of our GGC framework goes beyond simply monitoring AI actions; it ensures that organizations can update responses on the go and send notifications via chat or email to correct any discrepancies. This means that even if your AI autonomously generates an inaccurate answer, domain-experts can step in to ensure that the customer receives the correct information. By monitoring it stringently, the risk of inaccuracy is greatly reduced, building trust in generative AI and turning it into an asset from a liability.

COBOL’s Problem Isn’t Just Technical – It’s Strategic

In the finance sector, several investment banks use generative AI to analyze market trends and provide daily trading recommendations. Unchecked AI can misinterpret a minor market fluctuation as the start of a major tech stock bull run and advise traders to buy heavily into those stocks. Traders following the flawed advice, could cause an artificial stock surge. When the market corrects, the bank suffers financial losses, clients lose trust, and regulators investigate the reliance on AI, potentially leading to fines and policy changes. To mitigate legal risks tied to misleading responses, the control aspect of our GGC framework notifies the experts, ensuring customers receive accurate information.

The Role of ‘Control’ in Reducing Liability in Healthcare

Let’s now examine this from the point of view of the highly regulated healthcare sector. If AI were to provide a patient with incorrect medical advice without the ability to correct it in real time, the consequences could be severe for both the patient and the healthcare provider. By integrating a robust control system, healthcare organizations can immediately update AI outputs, ensuring the information is accurate and meets regulatory standards.

A great example is Google’s DeepMind, which uses AI to predict patient outcomes based on medical records. While the AI system can make real-time predictions, human experts maintain control over its actions, ensuring that every prediction is reviewed and verified before being shared with the patient. This collaboration between human experts and AI ensures patient safety and regulatory compliance, while maximizing the AI’s capabilities.

Real-Time AI Coach – The Key to Control

The Real-Time AI Coach, a feature of our GGC framework, is a perfect example of how control is applied in practice. This ‘coach’ allows your business to control AI responses in near real time, and if AI generates an output that is off-brand or inaccurate, the AI Coach can help correct it, ensuring consistency and reliability.

For instance, Tesla’s Autopilot AI faced issues when its system misinterpreted road signs, leading to accidents. Having a real-time control system could correct such errors promptly, preventing incidents and ensuring the safety of drivers. Such a system would notify the user of any changes, providing a critical layer of human oversight.

Another well-known example is ChatGPT, which, while capable of producing convincing responses, occasionally hallucinates – providing plausible but inaccurate information. Without a robust control mechanism, businesses using such tools could face significant risks. The control feature of our GGC framework can help mitigate these risks by allowing your organization to correct errors in near real time and notify users with the correct information.

Control Builds Customer Trust

A key advantage of maintaining control over AI systems is the trust it can foster with your customers. In an era where massive data contains proprietary and private information and security is paramount, customers need assurance that the information they receive from your AI systems (which uses their data) is trustworthy. By ensuring control over AI outputs and making prompt updates when necessary, your organization can demonstrate your commitment to providing accurate and reliable information.

The Future of AI Control

As AI continues to integrate into industries like healthcare, finance, and retail, the ability to control its outputs in real time will be vital for long-term success. By leveraging the control aspect of the GGC framework, your business can ensure that AI systems remain aligned with your goals, values, and legal obligations. This not only reduces potential liability but also enhances AI's ability to address real-world challenges effectively.

In conclusion, ‘control’ is the foundation of responsible AI deployment. With our GGC framework’s control feature, businesses can fully harness the power of Generative AI and LLMs. In today’s fast-paced digital landscape, this level of monitoring is not just beneficial – it’s essential for any organization looking to effectively leverage AI.