AI and GenAI: Pioneering the Next Era of Business

Last month, we successfully modeled a forecast on oil conditioning using a time series AI model. This engine analyzed historical maintenance and sensor data from hydraulic machines, gearboxes, compressors, turbines, and other equipment to predict potential oil degeneration. These early warnings help minimize downtime, reduce maintenance costs, and optimize technician resources.

While research breakthroughs can be exciting and successful, they don’t always meet business expectations. AI or GenAI has generated significant excitement within IT, but it must be approached with caution and a clear understanding of its practical limitations and security issues. Not every use case is suitable for AI tools. AI deployments require substantial time and budget, and while many have invested in pilots and plans, specifics on productivity gains and ROI remain guarded. Only a few have the patience to endure the development period, during which money is continuously invested without immediate returns, as much time is spent training the models to achieve the best outcomes.

Tools like ChatGPT, Gemini, and applications such as Soundful have fueled public excitement, making it seem like AI is revolutionizing everything. However, this revolution comes with its own challenges and limitations.

When building an AI-based solution, companies face unique challenges, including:

·        Models trained on specific data may not generalize well to new examples.

·        Inadequate testing and validation can lead to unexpected issues in production.

·        Deploying ML models at scale can be challenging due to infrastructure, latency, and reliability concerns.

On the positive side, despite challenges related to outcomes, costs, and reliability, AI is booming and deeply integrated into every aspect of business. It guides decision-making, product development, customer interactions, and overall operational processes, representing a transformative shift from traditional business models to those fundamentally driven by AI capabilities. AI influences every decision, predicts trends, automates operations, and creates new opportunities.

Organizations that have already integrated AI into their operations or products, particularly mainstream MNCs and SMEs, have been the first to explore GenAI’s potential. These companies have the capacity to incorporate the technology into their business and operations. A classic example of an AI-first approach is a retail company using AI for customer service chatbots or a financial institution employing AI for fraud detection. This trend is not limited to large companies and is expected to expand to small-cap companies and startups.

In the future, AI will not just be a part of business but will become the business. As this shift occurs, companies are becoming more agile in adapting AI and GenAI technology to stay competitive.