Scaling AI entails moving beyond proof-of-concepts to implementing and enabling AI in operational processes across a firm
By Balakrishna DR
The best use cases for AI in the last one year were found in combating the effects of the pandemic. The healthcare industry built numerous applications of AI for faster diagnosis, forecasting the spread and pattern of the disease, tracking people and their recovery, developing drugs, vaccinations, and managing the logistics.
Supply chain and retail companies have used AI and cognitive automation to overcome the threats of Covid-19, as consumers faced significant challenges in shifting from physical to online mode. Besides healthcare and retail, businesses of all shapes and sizes across several sectors have adopted AI to pursue higher productivity and enhanced customer experiences. However, managing the entire charter of an organisation gets tough as AI permeates through 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 to implementing and enabling AI in operational processes across an organisation. It is also about accessibility—anyone within an organisation can use and access intelligence to improve the work process. Studies show that scaling AI effectively can increase ROI up to 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 must conduct an AI and automation maturity assessment across multiple dimensions to arrive at a well-defined AI strategy. They must outline a structured approach to discovering, developing, and democratising the technology across the enterprise aligned with their business goals and vision.
Data strategy: A robust data strategy that defines the vision for identifying, capturing, storing, managing, sharing, and using the data is crucial to scaling AI. For AI to deliver the desired result, availability, relevance, and accuracy of data is essential.
Co-innovate with partners: Organisations can explore new opportunities through external collaboration aligned with 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 standardised cloud platforms that allow developers to access AI hardware and software stacks quickly and easily. This would help them build intelligent solutions that deliver AI-first business processes for enterprises.
Ethical framework for scaling AI responsibly: Without ethical and responsible use, solutions built with AI may work technically but may not deliver trust. An AI governance strategy to design, develop and deploy AI responsibly is a must.
While the ability to leverage and operationalise AI is increasingly essential to growth and differentiation in today’s fast-paced market, understanding emerging 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.
The writer is SVP – Practice head, AI and Automation and Energy, Utilities, Services, Communication and Media Segments, Infosys