Incorporating artificial intelligence into your business systems and processes is a journey unlike any other digital technology implementation. The path to AI is long and winding and requires a fresh look at existing approaches.
Rather than applying their learnings from other digital initiatives, businesses need to get ready for a whole new way of thinking to reap the full success that AI can offer. Perhaps even more than the right technology, AI requires the optimal blend of business case and corporate culture to succeed. In many ways, it reshapes the companies that adopt it.
The following ingredients are essential to creating an effective AI culture:
- Small, multi-skilled teams are critical. AI success depends on combining knowledge from business functions, processes, data and technology. It takes an organizational village.
- Speed is of the essence. AI’s rhythm is to pilot, learn and scale. To make it happen, you need to assemble the relevant skills and teams to work quickly and iteratively.
- Closing the learning loop is essential. Because learning happens on multiple fronts, it unlocks new capabilities and approaches that can be applied to other parts of the business. It’s important to have an organizational construct that can oversee multiple AI experiments in parallel and still ladder up to a centralized approach to learning that advances core business capabilities.
- Never forget humans are at the center of all key business initiatives. Balancing human ambition with machine resilience enables AI to grow. It can’t be an afterthought. Focus on finding balance from the get-go by emphasizing continuous AI literacy, skill retraining and role retooling.
- Communicate, communicate, communicate. Sharing knowledge and experience is at the heart of corporate AI efforts. Apply it to success and failure. Use constructive words that convey and reinforce business value, benefits and outcomes through the use of technology, superior techniques and differentiated data.
There is no utility AI, every company must chart its own path to success. Every application requires different tools and algorithms, and unlike other scalable technologies such as cloud and analytics, AI requires a fresh look at every use case.