Artificial intelligence (AI) is leading organizational change, offering new opportunities to redefine the future of work and workforce, not just optimizing what has been done in the past.
This requires us to think in a whole new way about how organizations deal with strategy, leadership, talent, culture, organizational design, etc., all the elements of organizational health.
Amazon, Google, and Facebook are examples of AI-first companies that have rapidly scaled and successfully managed fast-paced growth. They reduce reliance on "hard assets" such as machines or facilities, and instead focus on key intangible assets for organizational health, and leverage AI to unleash its full potential impactAs AI has rapidly become more widely available over the past few years, these companies have reinvented their talent and organizational practices to adapt to the AI world.
For example, applying AI learning algorithms to match potential employees with open positions enables recruiters to screen applications and fill vacancies faster.
In March 2017, Sundar Pichai, CEO of Alphabet and Google, announced that the company was transforming into an AI-first organization. Subsequently, the company released a range of scenarios for AI applications, including the development of specialized chips for optimizing machine learning, the wider use of artificial neural network-based machine learning methods (deep learning), such as cancer research, and the installation of Google's AI-powered assistant on as many devices as possible. Pichai said the company is moving from "searching and organizing the world's information to AI and machine learning." The announcement is a strategic shift in the company's vision. In the same month, Microsoft also announced its intention to shift from "mobile-first" and "cloud-first" to "AI-first."
In the future, we will see machines outperform humans in a more cost-effective way in multiple tasks. Using AI to optimize organizational structure is an emerging approach that can help companies better adapt to market changes, improve operational efficiency, promote employee development, and improve the overall competitiveness of the business.
Organizing to Win: The Engine of Profitable Growth in the AI Era
Claudie Jules, People's Posts and Telecommunications Press.
The case of Saxo Bank – AI-based information retrieval, analysis and personalized marketing
In 2016, Patrickhunger, then CEO of Saxo Bank, led the bank's technological overhaul. Saxo Bank has not only embarked on robotic process automation (RPA) to improve efficiency and free up humans to create greater added value, but has also developed various AI and machine learning projects to create new value. This work focuses on three key areas: AI-based information retrieval, analytics, and personalized marketing. Overall, the bank refers to these initiatives as "robotics."
But Henger also realizes that the success of a bank's digitalization initiatives will depend on a few key principles
1. Top-down
Leaders need to be proficient in robotics so they can create a compelling robotics vision and lead the journey, and articulate its importance to business strategy. In other words, leaders must empower robotics leaders to be change agents in their day-to-day work.
2. Break down organizational barriers
Many companies have created a cultural gap between their own business and their IT teams. However, robotics requires these teams to work as closely as possible to ensure they keep up and accelerate the pace of business development. As Hanger thinks,"It doesn't matter how smart the company's organization is designed. It is interpersonal transactions that create organizational thinking consensus and value; All of this is guided by a collective business goal that is rooted in culture. Prabhu Venkatesh, then Head of Data at Saxo Bank, further emphasized this point and explained:
We have a two-way collaboration model where technical and business teams can freely exchange ideas and information. The technical team knows what's possible, and the business team knows what's useful—and that's where the magic product is born. "Ensuring continuous dialogue and clear alignment between the IT team and the business team as two equal teams in the company is a critical effort to break down departmental silos, so machine learning and AI development teams are designed as an integral part of the business organization to bridge the gap. Christian Busk Hededal, Head of Big Data and AI at Saxo Bank, explains: "Our goal is to be a data-driven organization that brings technology and business together. ”
3. Present data to a wide range of stakeholders
Prabhu Venkaters noted that an important capability of robotics is to present data and KPIs to a wider stakeholder community. In other words, keeping the big picture in mind while helping every employee become more data-driven in their decision-making.
This is not just a set of manifestos, but a practical principle. It may sound inconsequential, butShowing execution data transparently creates a common understanding across teams and gives everyone an idea of what's happening in the company and how everyone's work impacts the company's performance.
The positive impact of this is the creation of a results-oriented culture in which people take the initiative to take action on visible problems rather than relying on processes to solve them. Build an engine of change in robotics. By building a strong governance system, Saxo Bank's leaders can more effectively drive robotics solutions based on the expected business value and are constantly researching new ways to enable businesses to benefit from robotics. This requires challenging the status quo and overcoming organizational and process hurdles that have become obsolete due to robotics. Finally, Saxo Bank needed to provide operations managers with practical methods and tools to manage a hybrid workforce of people and machines on a day-to-day basis. Saxo Bank has developed a plan to alleviate the anxiety of managers and employees when dealing with any changes: work closely with the HR department at an early stage to advise on the redeployment of human resources.
4. Ensure human-machine integration
The role of a leader is to consciously design the organization as an organism that reaches its full potential through inclusion rather than isolation. For Saxo Bank, human-robot collaboration is of great significance, not just as a buzzword, but also as a logical system. Under this system, innovation and performance flourish best in the human-machine ecosystem. "When we say we're essentially a tech company, we mean that technology is the primary tool for putting human skills into practice," Hunger points out. Technology has strengthened our organization's ability to move away from business size. "While some see digital means, especially AI, as a contradiction between machines and humans," others see technology as a way to help us show humanity like never before. It's a tool to improve the health of your organization. This organization, with an intelligent operating model at its core, will be the backbone of improving individual and team performance within the company, allowing new approaches to human-robot collaboration to take root. As a result, the biggest opportunity for organizations to evolve is not just to redesign work or deploy automation tools, but also to fundamentally rethink the "architecture of the intelligent operating model" to create new value for enterprise teams and individuals**.
In addition, during the pandemic, as the digital customer experience deepened, the full value of Saxo Bank's AI-based tools became clearer, especially when it came to unlocking human potential. As consumers stay at home, or at least far from brick-and-mortar bank branches, the bank's AI-based tools help employees meet rapidly changing customer needs and preferences in ways they never thought possible. Ultimately, the bank's employees become good at learning and working hard, which means the bank is able to respond in a faster, more agile and more accurate way. It can continuously learn, expand, and operate all year round, around the clock.
What does AI-first really mean?
AI First isn't just about harnessing the power of analytics (or decision-making) to enhance human-machine collaboration, it's about reinventing the organization of the future based on AI to prevent AI from being used only as a tool to optimize the organization. In other words, it's not just about doing the same thing better, cheaper, or faster, it's about launching new activities and creating more value.
Today's global digital economy (sometimes referred to as the "Internet economy") requires organizational consistency, agility, and intelligence more than at any other time in history. Organizations need to embed AI into every aspect of their operations, making it part of the organization's DNA in order to effectively unlock the full potential of the company. In the early years or in a less volatile business environment, the CEO or top management team may be solely responsible for developing the full potential of the organization. Nowadays, however, the essential elements of an organization must be coordinated and act in sync as a whole.
According to one study, nearly half of the major barriers to AI adoption were related to tissue health (see Figure 9-1). The study, published in the MIT Sloan Management Review, categorized respondents into four types: pioneers (organizations that understand and adopt AI), investigators (organizations that understand AI but don't complete the pilot), experimenters (organizations that experiment with AI without in-depth knowledge of the technology), and passives (organizations that don't know and don't adopt AI).
How can companies remove barriers and successfully unlock the value of AI?
Based on the results of MIT's research, I think it can be summed up as a high focus on four specific elements of organizational health. Specifically, companies that have successfully adopted AI and other digital technologies have excelled in the following four dimensions, which I see as four investments.
1. Investment in strategy and decision-making
A McKinsey study showed that the majority of respondents said their companies were already deriving value from AI, with larger scale, higher revenues, and lower costs compared to other companies. It's not just a matter of luck, but a company's ability to develop a business strategy, implement it, and change management in the process of applying AI all contribute to the extent to which change is achieved. Companies that have been more successful in democratizing AI are more likely to have a set of key actions, including aligning AI and business strategy, and five out of every six key actions are related to organizational health. Companies that are more inclined to use AI effectively are more likely to democratize AI across the company and realize business value. In another study, 36% of respondents from high-performing companies said their frontline workers use real-time feedback from AI to make day-to-day decisions, compared to only 8% of respondents from other companies.
2. Investment in organization and work design
McKinsey research found that nearly 90% of companies that have successfully adopted AI spend more than half of their analytics budgets on driving AI adoption, such as workflow redesign, communication, and training; Only 23% of other companies poured similar resources. 1 Companies that do the best at democratizing AI spend as much money or budget on projects to change and drive AI adoption (workflow redesign, communication, training) as they do on the technology itself.
3. Investment in talents
McKinsey research found that companies that successfully adopt AI within their organizations invest as much in people and processes as they do in technology. A survey of 1,000 companies found that only 8% of the companies surveyed were engaged in such practices, enabling AI adoption to become a reality. 13 Numerous studies echo these findings. MIT Sloan Management Review and Deloitte Digital in The Technology Fallacy: How People Are the Real Key to Digital Transformation The joint study published in the book provides compelling evidence that digital maturity has more to do with people and organizational change than with the specific technology it uses.
4. Invest in risk mitigation and awareness-raising
The way a company deals with risk (i.e., quality assurance audits or compliance training) largely determines the rights and responsibilities. Similarly, the way organizational learning is done (through the use of knowledge and collaboration platforms) often defines company culture. There are significant risks associated with adopting AI, as AI is often based on large amounts of data, such as search habits or call log hours, and misuse of this data is illegal. As a result, no company wants to risk collecting and using data without permission, without fully explaining to employees what it is going to do. According to the EU General Data Protection Regulation (GDPR), fines for non-compliance can be up to 20 million euros, or 4% of the company's global turnover. The potential judicial costs of non-compliance or abuse can exceed closing**, and if a problem is identified, the company's valuation will be lower due to the high cost of dealing with GDPR regulation. As a result, deal teams must carefully review whether the adoption of AI technology is compliant or whether there is a significant liability risk. Similarly, CEOs and boards of directors must establish governance and oversight structures to ensure that companies adopt AI responsibly.
In short, the time has come for artificial intelligence (AI) to lead organizational change, and the power of generative AI and large models is changing the rules of the game and accelerating disruption of old models.
In the future, AI still faces various challenges, which are worth exploring by large model companies, data analysis companies, and users. Hotspot Engine Program
This article is an excerpt from "Organizing to Win: The Engine of Profitable Growth in the Age of AI" by Claudy Jules. In December 2023, he was appointed and published by the People's Posts and Telecommunications Publishing House. With a focus on high-growth companies, including those in the midst of rapid expansion, seeking rapid growth and scale-up, many of the lessons are equally applicable to large organizations seeking to adapt and compete in a volatile digital economy.