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Every day we hear more about how self-driving vehicles will change our lives. Automotive innovators such as Tesla and Waymo have been working to advance this capability for years while legacy companies, including GM and Ford, have more recently joined the chase. Self-driving cars are now shuttling around riders — although still with human overseers — in Las Vegas, Boston, and Detroit, among other cities.
But artificial intelligence and automation go far beyond cars. When it comes to “self-driving” capabilities, how long until we can achieve this at a company level?
The concept of a self-driving company is that many routine, data-intensive decisions would be made by machines in partnership with humans. As our ability to quantify and capture organizational information grows, the opportunity to apply machine learning, and even automate decision-making, grows as well.
Self-driving company capabilities are closer than many leaders realize. Software companies and established venture capitalists are investing in new products to help serve self-driving enterprises in the near future. Here, we highlight some of the companies that are already making strides toward autonomous futures and suggest three steps for leaders to take to prepare for the coming changes.
The Metaphor of the Self-Driving Car
To put the concept of autonomy in context, autonomous vehicle offerings are ranked from zero (no automation) to five (fully self-driving) by SAE International (see “Understanding the Self-Driving Car Metaphor”). Tesla’s Autopilot, which requires drivers to keep at least one hand on the steering wheel, is level 2, which provides “steering or brake/acceleration support” to drivers but requires drivers to “constantly supervise” the support features.
To borrow this scale for business decision-making, many enterprise software solutions rank somewhere around zero. That’s akin to the invention of the automotive rear-view camera: It provides information, but it’s up to the driver to figure out what that information implies and how to use it, and it certainly won’t stop you from backing up into a fence.
Over the past couple of decades, minor advances in business software have flirted up to the equivalent of autonomous level 2, often referred to as “driver assistance.” This type of enterprise software is process based and automates routine tasks to make them faster and easier.
Salesforce, for example, routinized customer relationship management (CRM) for salespeople. SAP did the same for operations, as did ServiceNow for employee services. Microsoft routinized the documents, presentations, and analysis of knowledge workers. These types of applications assist workers but do little, if any, unsupervised work on their own. Humans still make the important decisions with them.
But this is just the tip of the self-driving iceberg. Machine learning has created vastly greater potential for software and applications to make more decisions and even take direct action. These new tools will be the equivalent of driving automation capabilities at level 3 — that is, the worker will still be in the “driver’s seat,” but the machine will do most of the driving.
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Industries Where ‘Self-Driving’ Capabilities Are Emerging
Automation is already changing a number of industries. In financial services, some experts expect robo-advisers to see assets under management increase to nearly $800 billion in the next five years. In digital advertising, companies now can use automated systems to place ads, and in travel and hospitality, automation is also important for pricing things like airline seats and hotel rooms.
Most company leaders are doing little if anything to create autonomous decision-making capabilities for their organizations, but one industry that is not lagging is enterprise software. Here are some examples by business function of software companies that are already creating autonomous offerings:
IT: A variety of “autonomic” software offerings from companies including IBM, CA Technologies, IPsoft, and ServiceNow are powering many different aspects of IT, from infrastructure configuration to software testing and data management. Oracle, for example, is positioning its Oracle Autonomous Database Cloud product as “the world’s first ‘self-driving’ database,” intended to manage client databases with “no human error” and “no human labor.” It automatically updates data sets without taking them offline, identifies and mitigates security threats, and uses machine learning to manage capacity and performance.
Operational planning and supply chain management: Aera Technology, a venture-backed startup, calls itself “the cognitive technology for the Self-Driving Enterprise.” It says its Cognitive Demand Management system figures out how a business works, makes real-time recommendations, and takes actions on a leader’s behalf in the areas of demand management, manufacturing performance, and supply chain visibility. Another company, o9 Solutions, offers automated capabilities for demand and operations planning.
Marketing: Many companies offering AI-powered tools have gotten into the business of automated marketing, lead generation, customer support, and SEO solutions. They include Marketo, which positions marketing automation as a self-driving car with Marketo’s customer engagement tools as the engine, and BounceX, a startup that turns anonymous web traffic into known traffic in order to create personalized next steps for engagement.
Sales: 6sense uses artificial intelligence to track potential buyers before they even enter a company’s sales funnel. By gathering and mining “early intent signals,” such as researching competitor products, 6sense says it can gather and qualify previously invisible leads and make recommendations on how to engage them. Another company, Conversica, autonomously creates personalized emails with the goal of moving customers down the sales conversion funnel. CRM leader Salesforce has developed its own “Einstein” automated capabilities which allow users to rank sales leads in their likelihood of yielding a sale.
Preparing for Autonomous Decision-Making
Just as auto manufacturers are rethinking the meaning of driving, software companies creating AI-driven business applications are forcing business leaders to rethink an equivalent question: What does it means to manage an enterprise once some of the work can be done autonomously?
All this may be a scary prospect for board members and corporate leaders — particularly middle managers who make repetitive decisions. But the goal of these automated offerings isn’t to eliminate human managers, but to create leverage for them. Even when systems are operating at the equivalent of automation level 3, a human manager will still be necessary for decisions that aren’t made frequently, or that require a high degree of human engagement, or that rely on information that isn’t digitized.
With the growth in machine learning and its capabilities, the expansion of digitized networks, and the explosion of data companies now have available, the direction is clear. Step by step, autonomous solutions will be integrated into the day-to-day management and operations of organizations.
Unfortunately, few leaders and boards are ready to make the leap to managing and leading an autonomous enterprise. As such, here are three intermediate steps leaders can take to help prepare themselves and their organizations for success:
Categorize your decisions. Not all decisions and processes can be automated. For managers, a good place to start is to complete an assessment of which decisions and processes are candidates for automation. Create an automation scoring system that is based on degrees of repetition, reducibility to rules, and levels of data generated. In addition, think about which skills create costs savings versus insights and actions. It can be helpful to start with the skills that are easy to automate and work from there.
Upskill yourself and your people. All leaders need to understand today’s data-driven world and how data models and machine learning will affect their jobs. To ensure success, AI upskilling needs to start with the C-suite: Top leaders need to appreciate how machine learning works at a high level, how data drives algorithms, and how to use the results across their teams.
Adopt AI pervasively. Many companies are falling behind competitors in their data and AI initiatives. Given the trend toward autonomous management, the risks of not catching up can be serious. Given this, organizations need to build data science departments that are as powerful as their IT operations, and charge them with collecting, managing, and growing the unique data of the enterprise.
Senior leaders still have plenty of time to prepare their teams and boards: We are just at the beginning of our transformation toward self-driving enterprises. However, given the pace of change and the many recent announcements by major software companies and venture capitalists that they are investing in autonomous solutions for business, it’s time for leaders to prepare for the inevitable. We are inextricably moving toward self-driving companies that set and maintain their own courses by crunching and acting on data, leaving humans to focus on more value-added activities.