For the past few years, McKinsey has conducted a global survey to better understand the AI initiatives pursued by different industry sectors and job functions. This year’s online survey garnered responses from 2,395 participants representing a full range of regions, industries, company sizes, functional specialties, and tenures. Below is a high-level summary of the results. Link: An executive’s guide to AI | McKinsey
AI adoption and impact
By industry, respondents in the high-tech and telecom sectors are the most likely to report AI adoption, with the automotive and assembly sector falling just behind. The business functions in which organizations adopt AI remain largely unchanged from the 2019 survey, with service operations, product or service development, and marketing and sales again taking the top spots. The largest shares of respondents report revenue increases for inventory and parts optimization, pricing and promotion, customer-service analytics, and sales and demand forecasting.
Notably, more than two-thirds of respondents who report adopting each of those use cases say its adoption increased revenue. Of those, the use cases that most commonly led to cost decreases are optimization of talent management, contact-center automation, and warehouse automation. Over half of respondents who report adopting each of those say the use of AI in those areas reduced costs.
The 2020 survey was the first time McKinsey asked participants about their adoption of deep learning. Of those surveyed, only 16 percent say their companies have taken deep learning beyond the piloting stage. Unsurprisingly it is the industries which have led AI adoption where deep learning is finding an early home, with high-tech and telecom companies leading the charge. A full 30 percent of respondents from those sectors saying their companies have embedded deep-learning capabilities. These are early signals that deep learning is finally solidifying a pathway to practical, regular application.
Separating the best from the rest
The companies seeing the highest bottom-line impact from AI demonstrate an overall organizational strength and engage in a clear set of core best practices. This means top-down support, where AI initiatives have an engaged and knowledgeable champion in the c-suite. Resources have also proved vital, with AI high performers investing more of their digital budgets in AI than their counterparts. Having witness the early benefits from robust internal support, these AI leaders are more likely to increase their AI investments in the next three years.
The organizations with the highest EBIT attributable to AI were more likely to engage in nearly every practice than those seeing less value from AI. These practices generally align to six categories: strategy; talent and leadership; ways of working; models, tools, and technology; data; and adoption.
- Strategy
- Have a road map clearly prioritizing AI initiatives linked to business value across organization
- Have a clearly defined AI vision and strategy
- Senior management is fully aligned and committed to organization’s AI strategy
- Have an active program to develop and manage an extensive range of AI ecosystem partnerships (eg, with companies, academia)
- AI strategy that aligns with the broader corporate strategy
- Talent and Leadership
- Tech professionals develop AI skills through tailored curriculums by role and progress along defined career trajectories
- An appointed, credible leader is empowered to move AI initiatives forward in collaboration with peers across business units and functions
- Strong, centralized coordination of AI initiatives is balanced with close connectivity to end users in the business
- AI talent is effectively recruited and onboarded
- Type of AI talent needed (e.g., by role and skill level) to support AI initiatives is understood
- Ways of working
- Feel comfortable taking risks with AI-related investment decisions
- Use advanced processes (e.g., data operations, microservices) to deploy AI
- Have a clear framework for AI governance that covers all steps of the model-development process and manages AI-related risks
- Use design thinking, involving the end user in development of AI tools
- AI-development teams across the organization follow a standard protocol to build and deliver AI tools
- Models, tools, and technology
- Have standard tool frameworks and development processes in place for developing AI models
- Understand how frequently AI models need to be updated, and refresh them based on clearly defined criteria
- Use automated tools to produce and test AI models
- Track AI-model performance and explanations to ensure that outcomes and/or models improve over time
- Use a standardized tool set to create production-ready data pipelines
- Own a high-performance computing cluster for AI workloads
- Use a standardized end-to-end platform for AI-related data science, data engineering, and application development
- Data
- Generate synthetic data to train AI models when there are insufficient natural data sets
- Rapidly integrate internal structured data to use in AI initiatives
- Have well-defined governance processes in place for key data-related decisions
- Have scalable internal processes for labeling AI training data
- Protocols are in place to ensure appropriate levels of data quality
- A data dictionary (ie, a metadata repository) describes the features of data that are accessible across the enterprise
- A clear data strategy supports and enables AI
- Adoption
- Entire organization consistently adheres to the execution processes identified as essential to capturing value from AI
- Systematically track a comprehensive set of key performance indicators to measure the impact of AI initiatives
- Capabilities are designed for scalability, and AI initiatives are fully scaled within business units and/or company-wide
- Have a comprehensive process for moving AI solutions from pilot to production
- Enact effective change management to ensure AI adoption (eg, by having leaders model behaviors)
There really is a “playbook” for success. Some of the biggest gaps between AI high performers and others are not only in technical areas, but also in the human aspects of AI, such as the alignment of senior executives around AI strategy and adoption of standard execution processes to scale AI across an organization. This includes board support for AI investment, the upskilling of existing talent, and bringing employees along for the journey to ensure talent feels like it’s part of the process.
However, challenges remain around a lack of model explainability, which presents a level of risk in nearly every industry. Some risks are more obvious than others, such as those in highly regulated industries such as financial services and health care. However, some lurk under the shadows, like a lack of AI adoption. Losing support for these initiatives through poor planning can lead to wasted investment and the risk of falling behind the competition.
