Competing in the Age of Artificial Intelligence
Competing in the Age of Artificial Intelligence
As AI finally begins to realize its long-heralded potential and change the business environment drastically in the process, companies need to develop an understanding of these fundamental changes and develop well thought out, but flexible, strategies to harness the strongest capabilities of both men and machines.
After decades of unfulfilled promise, artificial intelligence has finally begun to realize its potential, driven by massive gains in processing power and better data collection methods. Developments in natural language processing and computer vision have played particularly important roles in helping machines perform tasks traditionally reserved for men. AI has also caught the public imagination over the years thanks to well-publicized events like Deep Blue’s chess victory over Kasparov, AlphaGo’s recent victory over Lee Sedol, IBM Watson’s victory in Jeopardy or even Google’s demonstration of self-driving cars. All this has prompted massive investments in AI-related areas in industries like finance, retail, and healthcare.
Differences between human and machine thinking
Because AI systems ‘think’ and interact, they are often compared with people. Humans are fast at parallel processing (pattern recognition) and slow at sequential processing (logical reasoning); exactly opposed to this, while computers have mastered parallel processing in some narrow fields, their strength lies in superfast logical reasoning.
Human intelligence allows for different types of problem-solving capabilities. Compared to this, at current processing power growth rate, artificial general intelligence is a long-term possibility at best. AI excels at performing specific tasks, fast and thoroughly.
So while investment in AI is critical, it is useful to ask this question: How can business leaders harness AI to take advantage of the specific strengths of man and machine?
AI’s effects on traditional ways of doing business
On notions of competitive advantage
In the 1980s, a technology tool in itself (such as Wal-Mart’s logistics tracking system) could serve as a source of advantage. But, now, because of open source software, algorithms in and of themselves will not provide an edge. Other traditional sources of advantage, like positional advantage and capability, are also being reframed by AI. Companies need a more fluid and dynamic view of their strengths, instead of a focus on static aspects. These three examples show how traditional notions of competitive advantage are changing:
Data – This is the raw material for AI systems, where large and early-moving companies like Google, Facebook, and Uber have an apparent advantage. They have created massive data repositories, which helps them collect more data, and also leverage it for better ad-targeting or in self-driving cars. But other companies without these resources can still do well through collaborations, even with competitors, to create their own privileged zones. They need to figure out areas where sharing works, and where it may not.
Customer Access – Traditional notions of customer access, like well-placed physical stores, are being replaced by AI-driven customer insights that help with personalized marketing and appetite prediction, generating higher revenues at negligible extra cost.
Capabilities – AI-driven automation is encouraging the replacement of traditional segmented and discrete areas of knowledge by cross-functional capabilities and agile ways of working.
On decision making
The speed of decision making is changing rapidly.
Predictive analytics and objective data are replacing decisions based on gut feel and experience (as can be seen in stock trading, online ads, supply chain management and retail pricing).
On the role of human employees
Humans will not become obsolete but will see rapid and major dislocations into new areas of work.
They will be needed in large numbers to build the AI systems.
They will be needed to help machines with common sense, social skills, and intuition as well as for quality checks.
In such an AI-inspired world, strategic issues are enmeshed with organizational, technological and knowledge issues. Agility, flexibility and continuous education are important for winning strategies.
Getting started towards winning strategies
Companies need to identify the jobs that machines or men are better at, develop complementary roles and redesign processes accordingly. Plus, they should be willing to change and adapt strategies at short notice. This is true in general for all areas in today’s business world, but especially so for AI-enabled processes.
Instead of a brute force implementation of AI everywhere, companies could evaluate through four lenses whether AI can create a significant and durable advantage:
Customer needs – Define the fundamental needs of your customers and see if AI can better address them
Technological advances – Study if the right technology exists to address your requirements and if you can make use of work already done instead of building a system from scratch
Data sources – Create a holistic architecture that combines existing data with novel sources, even if they come from outside
Decomposition of processes – Break down processes into relatively routinized and isolated elements that can be automated, taking advantage of tech advances and data sources identified above
These steps can be challenging for most companies. Setting up a center of excellence can help keep track of current and emerging capabilities, incubate technical and business acumen and disseminate expertise through the organization. It can then collaborate with the functions that would eventually put AI to use.
Thus, the full potential of AI can be achieved only if humans and machines solve problems together and learn from each other.
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