Scaling AI entails moving beyond proof-of-concepts to implementing and enabling AI in operational processes across a firm
The best AI use cases over the past year were found in tackling the effects of the pandemic. The healthcare industry has developed numerous AI applications to accelerate diagnosis, predict disease spread and patterns, track people and their recovery, develop drugs, vaccinations and manage logistics.
Retail and supply chain companies have used artificial intelligence and cognitive automation to overcome Covid19 threats as consumers have faced major challenges switching from physical to online mode. Besides healthcare and retail, businesses of all shapes and sizes across several sectors have adopted AI to pursue Increased productivity and improved customer experiences. However, managing the entire statute of an organization becomes difficult as AI infiltrates a multitude of functions. Most businesses adopting AI are only piloting it or using it for a few specific business processes. Scaling AI entails moving beyond Proof of concepts for the implementation and activation of AI in the operational processes of an organization. It’s also about accessibility: everyone within an organization can use and access information to improve the work process. Studies show that effectively scaling AI can increase ROI three times.
What can organisations do to expand AI enterprise-wide?
Achieving enterprise-wide AI involves more than just implementing technology.
Define your AI strategy: Companies need to conduct a multi-dimensional AI and automation maturity assessment in order to arrive at a clearly defined AI strategy. You need to outline a structured approach to the discovery, development, and democratization of technology across the enterprise that aligns with your business goals and vision.
Data strategy: A robust data strategy that defines the vision for identifying, collecting, storing, managing, sharing, and using data is critical to scaling AI. For AI to deliver the desired result, the availability, relevance and accuracy of the data is crucial.
Co-innovate with partners: Organizations can explore new opportunities through outside collaboration that is focused on business priorities.
Get people ready: To successfully integrate AI into the company culture, organisations need to reskill/upskill the workforce and spread awareness about AI among their customers.
Technology platforms for AI solutions: To scale AI, companies can successfully develop standardized cloud platforms that allow developers to quickly and easily access stacks of AI hardware and software and help them create intelligent solutions that AI-first business processes for enterprises.
Ethical framework for scaling AI responsibly: Without ethical and responsible use, solutions created with AI can work technically, but may not build trust. An AI governance strategy for the responsible design, development and implementation of AI is a must.
As the ability to use and operationalize AI becomes increasingly important for growth and differentiation in today’s fast-paced marketplace, understanding new AI techniques and technologies enables successful adoption. Companies must adopt a comprehensive approach and roadmap to scaling enterprise-grade AI to address the shift in business expectations amidst this global crisis.
