Leadership in the Age of AI

#LeadershipInTheAgeOfAI

AI is becoming a transformative technology for new products, services, and enterprise value. These rewards are available to organizations in every industry, but the challenge isn’t just a matter of hardware and software. Accessing the most transformative outcomes from AI takes transformative human leadership.

AI is shaking up traditional industries by opening new revenue opportunities as enterprises evolve from simply offering products or services to delivering outcomes for customers. For example, using a suite of AI applications, a manufacturer of wind turbines might find new revenue by shifting from just selling hardware to instead providing reliable energy provision , a potentially greater customer value. Meanwhile, automation and predictive AI can help transform business functions (like HR or marketing) by optimizing workflows, improving efficiency, and lowering costs. And AI can also permit new business models and even disrupt entire industries, such as what is occurring in automotive and transportation.

Seizing the enormous potential from this transformative technology means rethinking what it means to be an effective leader in the age of AI. It is not enough to simply approve a technology acquisition, dictate the anticipated value to subordinates, and then take a hands-off approach, assuming employees will intuit the best ways to use of tools. Instead, leadership now is a multi-dimensional, whole-of-enterprise endeavor, where decisions, actions, and engagement at every level of business authority and across nearly every business function impact ROI.

To promote that end, consider these tactics for successful leadership in the era of AI.

1. Become AI literate

Like any business topic, leaders need a firm understanding of the types of AI, how they are used, and the enabling hardware and software that make them possible. This means diving deeper than high-level concepts of algorithms and neural networks. Leaders need to understand what things like computer vision, natural language processing, process automation, and more can (and can’t) achieve, as well as how machine learning reveals patterns and correlations and why the outputs are valid. This AI literacy is what allows leaders to better understand the risks that AI presents and what that means for ROI. With this level of fluency, business leaders are equipped to support the many AI stakeholders in ways that promote business value.

2. Strategize an AI approach

AI is not just one thing. It is the result of a constellation of hardware, software, data, and other enabling technologies. A strategic consideration for business leaders is to identify what the organization should buy, what it should build internally, and what it should outsource. There are hardware solutions for on-premise computing, but some AI applications can be hosted and cheaply scaled in the cloud. Third-parties may have software or services that can help validate AI outputs and monitor performance. And when it comes to the data science itself, the business leader should weigh whether it makes sense to develop a novel solution in-house via its data science team, purchase something off-the-shelf that can be tailored to the business needs, or rely on a partner to develop and manage the AI for the company.

3. Invest in the right talent

Data scientists and AI engineers are vital for AI programs, but scaling AI for enduring value takes more than just a technical workforce. Businesses require bilingual professionals who speak the language of science and the language of business and can shape how novel and innovative solutions make a competitive impact in the marketplace. Given the volume of business investment in AI, these kinds of professionals are in high demand, which is why leaders may look to upskilling the existing workforce. The data scientists can be educated to look through a business lens and the company executives and managers can be trained for their particular role as a stakeholder in the AI lifecycle. To be sure, everyone has a role to play, from marketing to HR to finance and legal and beyond, and an effective business leader invests in the workforce such that they are equipped to participate.

4. Cultivate an ecosystem of AI partnerships

Participating in the community of stakeholders developing and deploying AI allows the enterprise to contribute to the proliferation of leading practices and new knowledge, as well as learn from and improve via the insights from stakeholders in other sectors. Engage with practitioners and researchers in academia, third party organizations, outside advisors, and others through conferences, collaborative research, and meetings that offer regular opportunities to connect and grow.

5. Embrace the future of work with machines

Some might assume that using AI always means replacing a human with a machine, but that is a misunderstanding of where the global business community is headed. When weighing AI programs and the value they can offer, understand that the central decisions are not just what to automate. It is bigger than that. Leaders need to identify the compound value that results from delegating repetitive tasks to AI so as to liberate the workforce for more valuable labor. When changing the way things have always been done, leaders need to lay out a clear vision for the transformed business that applies its human workforce in new ways.

6. Be an ambassador for positive change with AI

The workforce may have pre-existing concerns or fears over AI. There may be an expectation that workers will be replaced and fired or that the use of AI may negatively disrupt ongoing projects and progress. The business leader needs to engender buy-in from the workforce, ensuring team members are excited for the potential in AI. This means providing and soliciting feedback. AI stakeholders at all levels of the business should have confidence that the tools the organization uses benefit not just overarching enterprise value but also how the workforce performs its duties. This has the added benefit of promoting AI fluency across the business.

7. Align AI programs and outputs with corporate social responsibility (CSR) goals

How AI is used can have a big impact on the broader society. For example, training and testing AI can result in significant energy consumption and corresponding carbon footprints. Business leaders are also called to uphold the organization’s CSR goals as they relate to things like promoting diversity, equity, and equal access to opportunity, as well as promoting eco-friendly tactics for technology use. The qualities of the AI lifecycle need to meet the ethical and safety standards that the enterprise sets for itself or are otherwise mandated through industry standards and government rules. Some of the AI qualities that impact the degree of trust stakeholders have in the AI lifecycle include fairness, transparency, reliability, privacy, and safety and security, all within a culture and technology framework that promotes responsibility and accountability. Collectively, these are the component qualities of Trustworthy AI.

8. Develop an organizational structure that permits continuous monitoring and improvement

Unlike traditional software, AI evolves as it is used, and for AI to deliver value over time, what’s needed is a business-wide structure that allows for continuous monitoring and improvement across the AI lifecycle. From the inception of the program through its development and then after deployment, the business needs documented processes and clear roles and responsibilities for assessing whether the AI application is on a path to deliver expected value while also remaining in line with timelines, business strategy, CSR, and other priorities. Doing so successfully takes clear waypoints in the AI lifecycle for assessment and decision making, enterprise-wide channels for AI stakeholder feedback and concerns, and potentially investment in complimentary tools that can monitor and validate AI outcomes.

9. Promote ongoing learning in AI throughout the enterprise

The field of AI is still developing at a rapid pace, and new capabilities create new vulnerabilities and opportunities. Thus, business leaders and the broader workforce are challenged to understand how AI is changing and what that means for the organization. As well as tracking new AI research, best practices, industry case studies, and novel applications, business leaders and others should remain abreast of where AI intersects with other emerging technologies, such as AR/VR and digital ledger technologies. This broad, up-to-date awareness positions leadership to make informed, strategic decisions on everything from technology and human capital investments to insurance, cybersecurity, public relations, and more.

10. Stay abreast of changing laws and regulations covering AI

AI maturity and deployments have increased so significantly in recent years that the impact of AI has dramatically outpaced government law and rule making. Yet, this is changing, as governments around the world craft and implement regulations and legal structures dictating how AI can be used. These emerging laws and regulations are not always consistent between countries, or even within the same country. Business leaders need to identify and empower the stakeholders (e.g., legal counsel) who are involved in helping the company comply with government mandates.

To be sure, we are still in the early days of an era where AI will be used at massive scale and impact nearly every aspect of business, government, and society at large. There is no universal prescription for how enterprises should use and manage AI. Instead, every business leader and their workforce needs to carefully weigh priorities, ROI, risks, and all the factors that impact enterprise value, both now and in the future.