3 Strategies to Leverage AI in Business
Source – td.org
Leaders help organizations maneuver transformational changes. Latest advances in artificial intelligence have triggered large scale changes in every industry. Interestingly, a recent Harvard Business Review article has predicted that “the first wave of corporate AI is doomed to fail.” The authors, Kartik Hosanagar and Apoorv Saxena, have argued that most of the companies are not approaching the AI-focused initiatives in the right way.
Undoubtedly, organizations must persist in their initiatives, experiment continuously, and learn from mistakes. However, the initiatives may still fail due to lack of preparation or simply because of not following a systematic approach. Senior leaders in every organization must rise and face this challenge by creating robust strategies that may help the company thrive in the new age of AI. Let us explore three such critical strategies that leaders must have to harness the powers of artificial intelligence.
Strategy#1: Cut Through the Clutter
Everyone is talking about AI. In this year’s Consumer Electronic Show (CES 2017), intelligent devices took center stage. Companies are desperately trying to ride the wave of AI. Products and services are regularly getting tagged with this breakthrough technology. There is a lot of hype surrounding machine learning, deep learning, neural networks, and related topics.
Consequently, leaders must have a strategy to cut through the clutter and identify how their organizations can use artificial intelligence to create positive outcomes for the business and customers. While it may be too much to expect that an executive would have hands-on experience in technology, but a reasonable level of AI-literacy would be a must. Leaders should:
- have a broad level understanding of AI and how the machine learning approach is superior to the traditional (hard-coded logic driven) computing approach
- realize AI’s potential and limitations (what it can do and what it can’t do) to ensure that there are no exaggerated expectations created within the organization about its capability
- identify how this technology can be leveraged to create value for the customers.
Strategy#2: Creatively Apply the “New Electricity”
When asked about the factors that might be hindering progress of AI in business Eric Brynjolfsson, author and professor at MIT Sloan School, has remarked, “What’s not holding us back is the technology, what is holding us back is the imagination of business executives to use these new tools in their businesses. You know, with every general-purpose technology, whether it’s electricity or the internal combustion engine the real power comes from thinking of new ways of organizing your factory, new ways of connecting to your customers, new business models. That’s where the real value comes.”
Sooner or later, most of the organizations would acquire expertise in AI by upskilling the existing workforce or by hiring new talents. At that time, the primary differentiator would be the way one organization applies this technology to solve customers’ problems vis-à-vis another.
Leaders must spot and identify opportunities to apply these powerful tools by encouraging their teams to tap creative ideas. Such ideas may come from within as well as outside the organization. Moreover, the leaders must foster an ecosystem of innovation that helps the teams to ask the right questions around customer’s pain areas, effectively use the machine learning and AI to solve those issues, and create extraordinary customer experiences.
Strategy#3: Grow and Mine a Reservoir of “New Oil”
This is perhaps the most underrated and yet critical strategy a leader must have in the age of artificial intelligence. Leaders must strategize, invest in, and systematically help in building reservoirs of quality data that can harvested in the future. The businesses that would be able to do this better would differentiate themselves from the rest.
Peter Norvig, director of research at Google and a leading authority on AI once said, “More data beats clever algorithms, but better data beats more data.” One of the most common class of machine learning algorithms are based on supervised learning, which needs millions of data sets for training purposes. The efficacy of these algorithms depends not only on large amounts of data but on data that is labeled or tagged.
Everyone is talking about data, but hardly anyone is emphasizing the urgency to capture data in a systematic way so that it can be used by machine learning algorithms in the future. In any business, there are numerous data sources coming from internal and external stakeholders as well as data generated through human-machine interactions. For example, whenever a company plans to use Robotic Process Automation (RPA) to improve the accuracy and cycle time of operations, it must capture transactional data from various process steps to record how humans are currently performing these tasks. At the time of storing such data, it may complete the necessary tagging and run quality checks to prune human errors or biases. Without such disciplined approach of gathering and labeling data, AI-initiatives may not be effective.
Bottom Line on Strategies Needed to Leverage AI
While AI is destined to transform almost all industries, not every organization will unlock its maximum potential. Executive leaders of every organization must create effective strategies to harness the powers of AI. Specifically, they must have a strategy to cut through the clutter, to creatively apply AI in business, and to create reservoirs of quality data that can be appropriately mined by AI tools in the future.