“Artificial intelligence is reshaping business – though not at the blistering pace many assume,” notes Building the AI-Powered Organization, an article in Harvard Business Review by McKinsey partners Tim Fountaine, Brian McCarthy, and Tamim Saleh in its opening sentence. A number of studies agree that AI is the biggest commercial opportunity for companies and industries over the next 10-15 years. Two recent reports by McKinsey and PwC respectively concluded that AI has the potential to incrementally increase global economic output between now and 2030 by an additional $13 to $15 trillion.
“Yet, despite the promise of AI, many organizations’ efforts with it are falling short,” adds the HBR article. Surveys with thousands of executives, showed that most firms are only using AI in ad hoc pilots or applying it to a single business process. Only 8% of firms are engaged in practices that support widespread adoption.
Why the slow progress? “At the highest level, it’s a reflection of a failure to rewire the organization. In our surveys and our work with hundreds of clients, we’ve seen that AI initiatives face formidable cultural and organizational barriers… at most businesses that aren’t born digital, traditional mindsets and ways of working run counter to those needed for AI.”
To support the widespread adoption of AI, companies must make three fundamental shifts.
From siloed work to interdisciplinary collaboration. As Yogi Berra might have said, this first shift feels like déjà vu all over again, as IT had to go through something similar over its first few decades.
Companies first embraced IT in the 1960s and 1970s to automate their existing back-end processes in applications like inventory management, financial transactions and airline reservations. The advent of PCs in the 1980s then made it possible to apply IT to front-office processes and applications, such as word processing in office systems, spreadsheets in data analysis, and customer support. The use of IT grew rapidly with the automation of an increasing number of individual processes, while the underlying structure of organizations remained in place.
But during this period of rapid IT growth, US labor productivity grew at only 1.5% between 1973 and 1995. “You can see the computer age everywhere but in the productivity statistics,” said MIT Nobel Prize laureate economist Robert Solow in 1987, in what’s become known as the Solow productivity paradox.
It wasn’t until the 1990s, with the emergence of business process reengineering and of sophisticated business management applications like enterprise resource planning (ERP), that companies realized that using IT to automate existing processes wasn’t enough. Rather, it was time for organizations to fundamentally rethink their operations, redesign the flow of work in their companies, and integrate the various processes and functions in the enterprise.
In their 2009 book Wired for Innovation: How Information Technology is Rewiring the Economy, Erik Brynjolfsson and Adam Saunders introduced the concept of organizational capital as the necessary critical ingredient that enabled a company to take full advantage of major IT advances.
“The companies with the highest returns on their technology investments did more than just buy technology; they invested in organizational capital to become digital organizations. Productivity studies at both the firm level and the establishment (or plant) level during the period 1995-2008 reveal that the firms that saw high returns on their technology investments were the same firms that adopted certain productivity-enhancing business practices.”
Similarly, says the HBR article, “AI has the biggest impact when it’s developed by cross-functional teams with a mix of skills and perspectives. Having business and operational people work side by side with analytics experts will ensure that initiatives address broad organizational priorities, not just isolated business issues. Diverse teams can also think through the operational changes new applications may require – they’re likelier to recognize, say, that the introduction of an algorithm that predicts maintenance needs should be accompanied by an overhaul of maintenance workflows.”
From experience-based, leader-driven decision making to data-driven decision making at the front line. Will there be enough work in the future?
Over the past few years, a number of papers, reports and books have addressed this very important question. They generally conclude that AI will have a major impact on jobs and the very nature of work. For the most part, they view AI as mostly augmenting rather than replacing human capabilities, automating the more routine parts of a job and increasing the productivity and quality of workers, so they can focus on those aspect of the job that most require human attention. Overall, few jobs will be entirely automated, but automation will likely transform the vast majority of occupations.
To many, AI feels different from previous technologies, as it forces us to explore the very boundaries between machines and human. Learning to create a new smart workplace, where humans and machine collaborate to attain major increases in business performance will take time and considerable reskilling. In such a hybrid space of human-machines activities, humans work closely with their smart machines, each doing what they do best.
“When AI is adopted broadly, employees up and down the hierarchy will augment their own judgment and intuition with algorithms’ recommendations to arrive at better answers than either humans or machines could reach on their own. But for this approach to work, people at all levels have to trust the algorithms’ suggestions and feel empowered to make decisions – and that means abandoning the traditional top-down approach. If employees have to consult a higher-up before taking action, that will inhibit the use of AI.”
From rigid and risk-averse to agile, experimental, and adaptable. Like electricity, computers and the Internet, AI is a General-Purpose Technology. Such technologies are the most transformative due to their broad potential, but realizing their benefits requires time-consuming complementary innovations and investments, including additional technologies, applications, processes, business models, and regulatory policies.
Despite the recent hype, we’re still in the early stages of AI’s deployment. It’s only been in the last few years that complementary innovations like machine learning have taken AI from the lab to early adopters in the marketplace. Beyond the relatively small number of early adopters, AI is still in the experimental stage. To achieve a wider deployment, considerable experimentation is still required with a variety of pilots, use cases and marketplace applications.
“Organizations must shed the mindset that an idea needs to be fully baked or a business tool must have every bell and whistle before it’s deployed… Such fundamental shifts don’t come easily. They require leaders to prepare, motivate, and equip the workforce to make a change. But leaders must first be prepared themselves. We’ve seen failure after failure caused by the lack of a foundational understanding of AI among senior executives."
“The ways AI can be used to augment decision making keep expanding,” write the authors in conclusion. “New applications will create fundamental and sometimes difficult changes in workflows, roles, and culture, which leaders will need to shepherd their organizations through carefully. Companies that excel at implementing AI throughout the organization will find themselves at a great advantage in a world where humans and machines working together outperform either humans or machines working on their own.”