Blending people with machines
To reap the benefits of AI, businesses need to build new workflows that enable predictable, rote and repetitive activities to be done by machines, while humans specialize in applying judgment, creativity and empathy.
The executives interviewed for this report recognize this need and what it means for their workforce. “We especially need skilled people who are capable of running automated systems. We will be hiring much more such talent in the future,” said a CEO from a consumer goods company in Europe. A U.S. healthcare COO remarked that “the coming five years will see an increase in demand for AI, ethics and data governance, and data science in multiple divisions across our business units.” The growth in demand for such roles requires the workforce to increase its range of skills to make themselves relevant to where the market is clearly going. Big data specialists, process automation experts, security analysts, human-machine interaction designers, robotics engineers and machine learning experts will all be highly valued for the foreseeable future.
How well organizations blend and extend the strengths of their people with the capabilities of intelligent machines will determine their digital maturity and their success in fundamentally changing – and improving – how work gets done. Organizations will need to rethink their workforce resourcing model by applying AI to specific processes, separating out tasks and activities from within jobs as they are currently configured, and parsing them anew between people and machines. The result will likely mean more gig work and micro-outsourcing of tasks as work becomes more specialized. Success for many organizations will depend on how they blend and extend the strengths of people with the capabilities of machines. Management will need to be focused on explaining this objective in a way that takes people along on the journey; preparing the workforce for the profound changes in how they work is an important element of living up to the mantra of being an organization of purpose, and of being clearly regarded in talent markets as an employer of choice.
Quick Take: How to match people with machines
Succeeding with AI requires an acute focus on the relationship between humans and machines, how the two will collaborate, and how the current workforce and the business will adapt to AI. We offer a framework to help organizations build workflows to match smart people with smart machines by aligning five elements (5Ts) – tasks, teams, talent, technology and trust – to transition into the new machine age successfully. At the heart of this framework are business processes that need restructuring and reengineering to support human-machine collaboration:
Tasks: deconstruct jobs into tasks. Companies will need to identify which tasks within any given job are best performed by humans vs. machines to achieve an optimal balance of human-machine collaboration. In most cases, portions of a job will be impacted or replaced by a bot, while other portions will be untouched or even enhanced.
Talent: fuse human and technical skills. People skills will need to be tweaked for optimal human machine collaboration. Workers will need to think in terms of the systems, tools and processes required to make the best use of AI-driven insights and capabilities.
Technology: IT matters more than ever. Whether your organization is recreating a business process from scratch or injecting AI into front-, middle- or back-office processes, success will depend on how well the IT infrastructure is integrated with AI systems. The IT infrastructure needs to become agile, responsive, flexible, secure, scalable and simple to manage the transition.
Teams: small, flexible and fluid. We will witness a shift from larger hierarchical team structures to smaller teams in the future. These changes will allow individuals and teams to become more fluid and flexible across roles and functions. Businesses will require new roles, such as human-machine teaming managers, to identify tasks, processes, systems and experiences to be upgraded by newly available technologies, as well as imagine new approaches, skills, interactions and constructs.
Trust: instilling trust in machines. From unexpected or biased results to dangerous errors, we now face the moral dilemma of determining who’s responsible for any wrongdoing by an AI-driven machine. Businesses will need to increase transparency into AI mechanisms and decisions.