ChatGPT, its GPT-4 iteration and the broader advancement of generative AI are raising the stakes for companies that are expected to incorporate this technology. CEOs, boards and top executives in such a spot should step back and meet the challenge with a big picture approach that systematizes a set of proven strategies.
AI as the next competitive weapon for business relevancy, and profitability means companies must transform from digital enterprises to intelligent enterprises. The latter successfully integrates AI, machine learning, data analytics and the Internet of Things (IoT) to drive innovation, optimize processes and create new revenue streams.
Begin by adopting an “AI first” mindset. Adapt your organizational structure to the reality that “intelligence” increasingly resides in many parts of the company. It further means nurturing and investing in talent accordingly and being cognizant of the regulatory and compliance pressures.
Also, think of Intelligent Enterprise as an augmented organization where AI and humans are complementary while adapting to the surroundings and competitive atmosphere. It’s forward-looking. It’s able to both exploit its current competitive advantages and explore and experiment with new innovations and ideas that emerge from its workforce. Good examples include the Ocado Smart Platform, an “end-to-end e-commerce, fulfillment and logistics platform” for grocers from the British online supermarket and tech company, and Netflix’s leveraging AI to analyze data on viewer preferences and behavior. This has informed decisions about what types of content to produce and how to market it and has yielded hit series like “House of Cards.”
AI systems enable “decision decentralization” — empowering employees with intelligence at their fingertips and allowing companies to shift responsibility to individuals closest to the outcomes of the decisions. Toyota pioneered decision decentralization in manufacturing, via Jidoka. Decentralization further enables different parts of the organization to fix technology flaws and hire their own experts. For example, the materials science company W.L. Gore & Associates is organized into self-managing teams under its “lattice” model with employees encouraged to take on different roles and responsibilities based on their interests and skills.
A vision for nurturing and enhancing talent is critical. A good model is Coca-Cola’s Data Science and Analytics Academy for employees across departments and job roles, which has helped optimize operations, improve supply chain management, and enhance customer experiences using AI-powered solutions.
Confidence also must be elicited in stakeholders and regulators. Decentralized intelligence and decision-making will behave in sometimes unexpected ways, as will government regulations of AI-based systems and decisions. Furthermore, different countries may have different perspectives on societal impacts of AI-based systems (privacy, autonomy, etc.). Microsoft has a model for such mitigation with its AETHER (AI and Ethics in Engineering and Research) committee of experts in computer science, philosophy and law to review and advise on legal and ethical implications.
Humancentric — not artificial — intelligence is most essential to success and survival in this emerging era. As a recent study involving 1,500 firms in a range of industries indicates, indicates, the largest rate-of-return will be made with “Human 2.0″ — humans and AI-based machines working together to yield intelligent decisions, i.e., true intelligence.
To position your company for maximum value from integrating the gains from technology, especially AI with human ingenuity and capabilities, refer to the checklist below as an “AI Manifesto for the Intelligent Enterprise CEO”:
-- Become an “AI first” organization: Embed AI into your purpose and vision;
-- Adopt an “AI-everywhere” premise: Embed AI into every change, transformation and innovation program;
-- Augment every business process with AI: Explore and implement new use cases for AI across the business;
-- Increase your organization’s AI IQ: Foster a culture of innovation and learning, and invest in the education and training of your employees;
-- Create high-performing AI augmented teams: Remove “artificial” from AI so that your teams effectively use AI enablers and tools;
-- Build diverse and inclusive AI teams: Partner with organizations that promote diversity and inclusion in AI;
-- Promote ethical AI: Use data responsibly, and ensure your AI systems are transparent, explainable bias-free and fair;
-- Embrace progressive innovation in AI: Regularly assess the impact of your AI initiatives and make continuous adjustments to ensure they align with your purpose, values and goals.
AI is “human augmenting” rather than “human replacing.” AI-based automation may be tempting as a panacea for cutting costs, and this may be of value in the short run. But humans connect with customers better. As future technology and markets change constantly and rapidly, customers will increasingly make decisions based on trust and empathy.
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ABOUT THE WRITERS
G. Anand Anandalingam is professor of management science at the University of Maryland’s Robert H. Smith School of Business, and Alwin Magimay is global head of AI for PA Consulting, London, U.K.
