Shifts that AI brings to your organization

When Steven Jobs returned to Apple in 1997 he faced a company on the brink of bankruptcy. That story has become a business legend by now. It is not always mentioned how he approached his task coming back to Apple. Initially, Jobs set his sight on cleaning the house (inventory reduction, consolidation etc) and then was asked what he was going to do next. Jobs sat down and smiled: “I am going to wait for the next big thing.”

Figuring out the next big thing and putting everything in place to take advantage of the shift is a clever strategy. Currently, AI is hiding in plain sight as the next big thing. It is time to get up and put your pieces in order to make the most of it.

A short recap of shifts to organizations

When most people think of AI they think of data terminals, algorithms, mathematics and business processes. While the AI revolution is driven by the convergence of data, processing power and algorithms there is something more going on. AI introduces a shift in not only value creation and delivery but to the core of organizations themselves. And it is not the first technology shift that affected the way people work. Let us take a look at three world-altering shifts in the past and how they changed organizations.

Henry Ford rode the wave of the second industrial revolution powered by revolutionary technologies such as mass-produced steel, electricity and availability of machine tools. His Model T was built on the back of these technologies and changed society by ushering in personal mobility for everyone. All of this happened because one more ingredient was mixed in: Frederik Taylor’s ideas on management. In 1911, Tayler published his landmark book “The Principles of Scientific Management” and introduced concepts of breaking work down into small repeatable tasks, in control of the system and standardizing for quality. This framework of standardization and scale added social innovation to technological powers. Soon, most people could afford and drive a car.

50 years later, Peter Drucker saw that value creation needed a new approach to fully utilize its potential. “The most valuable asset of a 21st-century institution will be its knowledge workers and their productivity,” he wrote. In Taylor’s thought, the intelligence was located in the setup of the system and its protocols; for Drucker it shifted to the knowledge worker. His ideas of managing with autonomy and empowerment led to MBOs (management by objectives), decentralization and a focus on talent management. Good people were extremely important and to be sought, groomed and retained. Social innovation once again made tech innovations with high-tech knowledge requirements impactful. Soon, high tech entered offices, living rooms and finally our pockets.

Over the last 40 years, the IT revolution is changing industries. The software-driven change in many businesses comes with a new set of management ideas, often around the key idea of agility: decentralized decision making, early customer involvement, and a short-cycled priority setting. Social innovations again make tech innovations show their power. Today, software changes many industries and everyday experiences across the world.

Now, with the advent of the data-driven business we see some new demands emerging. From Taylor to Drucker to Agility — management behaviors need to change to meet the needs of a changing technological drivers. While many successful companies are based on Tayloritic principles, they realize the need to adapt. Often, they operate on standards and scale, try to fit in some talent management and agile thinking. This mish-mash of management ideas is now even further stretched by entering the age of AI. To use the power of AI, another set of shifts is needed — and while it is an evolving field most of the core changes are well within our view.

Three minor shifts when bringing AI to your organization

As companies engage in their AI journey there are a number of questions they face early on. These are minor in the sense that they are part of the early learning curve and will be overcome with practice. Once properly addressed they will fade in the background and not be as relevant anymore. They are still real challenges and need to be solved as soon as possible. These are the questions facing today’s organization when embarking on the AI journey:

• Fear of employees: AI threatens to replace or monitor employees. These scenarios are on top of many people’s minds. Without the buy-in of the organization, any transition will be slower and divert attention from progress. What are the change approaches to the questions of your employees?
• Lack of skills: AI requires a range of technical and management skills. The field is evolving and many roles are developing as we speak. Many organizations cannot afford to hire highly technical experts or motivate them to move there. Many management teams do not have experience in directing AI teams or making decisions on AI matters. What is the approach for this?
• Developing AI vision and strategy: AI holds great promise but putting AI systems into production is challenging and error-prone. Some figure that 85% of systems never make it to production. Where do companies focus their effort? How do you build a plan if you are not familiar with AI? What is the long-term plan to avoid costly detours and burning goodwill of all involved?

Five major shifts when embracing AI in your organization

While the three barriers above can be addressed with good planning, learning and outside help, these following shifts are adjustments of the ongoing nature of operations. And any successful company will have consolidated processes, habits and beliefs that make these shifts tricky and face strong resistance internally. Also, these shifts will be ongoing development work that leads to a multi-year transformation. They need to be considered early to be addressed. The AI journey necessitates a new way of management at the company. Companies need to answer these questions to harness the power of AI for their use:

• Becoming data-strategic: AI runs on data. Getting data, accessing data and ensuring proper use are tricky organizational questions. Often people close to data generation (think: website owners, or shopfloor managers) don’t have data science awareness. Often legacy systems make it complicated or impossible to get to data or make them useful. Governing data access to ensure proper use and avoid malpractice is another real challenge for corporates. What is the data strategy across all these questions?
• Overcoming silos: AI needs a combination of business owners, data and ML experts, software developers and operators. Often these play in different organizational units. How to set up an organization to ensure alignment, communication and ownership is not trivial — and many have suffered delay, turnover or project failure because of it. What is the playbook to bring teams together that become effective fast and do not play politics?.
• Exploration attitude: AI deals in probabilities, not certainties. Therefore, AI projects are much more exploratory in nature — which runs counter to longer-term plans and defined time frames. AI systems in production continue to be dynamic based on the data provided. How do you set KPIs for exploration? How good is your organization to deal with the inevitable failure that is part of exploration? How do you manage ongoing AI systems in production that require thinking in probabilities?
• Speeding up decisions: Since AI projects build on real-time data and do not have a clear answer, organizations need to find a way to empower decisions close to action. Central decision making is the default in many organizations and AI brings the added demand of data-literacy to management teams. How do you orchestrate your decision processes to move fast?
• Maintaining momentum: Introducing new ideas to an organization goes through waves. After 1–2 years, the attention shifts in organizations to new things. Embracing AI as an organization takes longer than 2 years. What is the plan for sustaining momentum in the organization? How do you move people through these required shifts? What is the communication plan and when is success celebrated?

We echo Jeff Bezos that AI has the power to transform every industry and every organization. This enormous potential needs action today. Speaking of Bezos, Amazon did not just run a bookstore with an ordering process on the internet — he built a whole different approach to discovery, order, fulfillment and review powered around internet technologies. So, AI will not just be your business with a few steps data-empowered. AI ushers in a shift in a new way business can be done. The benefits will be enormous. Probably, it will be a winner-takes-most in many industries. The shifts to your operating model, organization and management are not trivial. But the journey will be worth it to reap the benefits that AI will bring to your business, your customers and, ultimately, society.

Marlin Watling

Owner at 129

Former HR Director at SAP, Bilfinger and Roche, Marlin holds a degree in organizational psychology and has run over 200 change programs through his career. At 129 he helps executives figure out their AI journey and what it takes to ensure that the management of AI is set up for success.

Originally published at https://peltarion.com.