Integrating AI into an organization’s technical infrastructure is challenging. It requires precision and suitable embedding to incorporate artificial intelligence into the system. Time and resource management are equally important to make it work. In this article at Harvard Business Review, Terence Tse and his fellow authors explain why organizations fail to integrate AI. Even after working closely with a reliable vendor, artificial intelligence does not respond to business improvement.
Mission: Active AI Operations
It is a practice that involves the consolidation, testing, releasing, deployment, and management of the AI systems to withdraw desired insights of the end-users. To integrate successful AI operations (AIOps), you need not only the right tools, but also a team of skilled developers and engineers. They know the trick to integrate AI into existing processes and systems. Only an eligible organization can fuel the AI engines with real business transactions to achieve scalable results.
Unlock the Real AI Value
A fraction of codes allocated to AI functionality in many businesses would help you restart. In a well-planned environment, where the coders meet the end-users, unlocking the real value of AI is crucial. Think about managing projects, right from scratch, first, analyze whether the proposed AI solution could help you develop and integrate the client’s operating system. Also, aim to meet these three product environment standards:
Dependability: Laden with technical glitches, the AI tools can stop functioning once you add wrong data. So, avoiding data bottlenecks is essential to developing a dependable environment. Adjust processing and storage architectures to overcome throughput and latency issues. An ideal AIOps team develops contingency plans to overcome roadblocks.
Flexibility: Business objectives and process of workflow keep evolving. Enable AI models to deliver promising results like data imports at regular intervals. Thus, a flexible production environment is critical for smooth system reconfiguration and data synchronization.
Scalability: As your business expands, establishing an improved infrastructure to upscale your capabilities and develop new competencies becomes crucial. Nonetheless, different IT systems carry different problems as they cross system boundaries. By embedding upgraded AI models, increase the chance of business expansion by continually adjusting to the transformational solutions.
Click on the following link to read the original article: https://hbr.org/2020/06/the-dumb-reason-your-ai-project-will-fail