How can companies achieve AI transformation? After researching hundreds of companies, we found the answer

Source: Shidao

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**For Chinese enterprises, the necessity and urgency of digital intelligence are indisputable facts. **

However, whenever we talk about "digital intelligence transformation", it is like "a pony crossing the river". Most companies are like "ponies" eager to cross the river. Although they are not as rich as "Lao Niu" (large companies with large size and sufficient funds), they are still better than "squirrels" (small companies with poor risk resistance). In the original story, "pony" can decide independently "whether to cross the river or not". However, in the real world, the fourth wave of industrial revolution is sweeping the world, and many companies in the world have now been pushed to the river of digital intelligence. **

When it comes to survival, the river must be crossed.

Therefore, it is particularly important for enterprises to “find out the stones in the digital intelligence river”. This requires the experience of a "successful river crosser".

An article in the Harvard Business Review “What makes a company successful in using AI? "What Makes a Company Successful at Using AI?" gives a "Guide to Successfully Crossing the River."

The article adopts a study by McKinsey and MIT’s Machine Intelligence in Manufacturing and Operations Initiative (MIMO), which tracks the progress of 100 companies (involving various industries such as automobiles and mining) in digitalization, data analysis and machine intelligence (MI) technologies. Based on the performance of goals, actions and results, it can be concluded that the leading digital intelligence enterprises have certain commonalities in five aspects: governance, deployment, partners, personnel and data availability. **

The article has two authors, one is Vijay D'Silva, a senior partner at McKinsey & Company; the other, Bruce Lawler, is the managing director of MIMO at MIT. The author points out that the research results of this article can be used as a reference for all digital intelligence transformation enterprises. **

01 Four types of enterprises in the transition period

The first conclusion: If you want to stand out among the crowd in the digital intelligence competition, top companies must develop an overall plan for collaboration, and should not think about defeating the enemy with one move. **

By evaluating 21 performance indicators in 9 categories, the author divided 100 digital intelligence transformation companies into leaders, planners, executors and emerging companies.

The best are "leaders", accounting for about 15% of the sample. They achieved very significant improvements in 20 of 21 key performance indicators, ranking in the top 25% in all 9 performance categories. This type of enterprise can spend money wisely and is the biggest beneficiary of digital intelligence.

The second category is called "planners" and accounts for about 25% of the sample. This type of company is good at dealing with people and has solid knowledge of data execution. However, many companies are currently not reaping the rewards of transformation. Some companies are even struggling with the “pilot trap” proposed by McKinsey in 2018.

** Looking at it from the outside, the "pilot trap" is also a pain point for many Chinese companies in their digital and intelligent transformation. **

The large-scale transformation of an enterprise is a long-term and systematic process of accumulation of capabilities. Then, when promoting transformation, companies will choose some small-scale pilots to verify new change measures. However, these pilot cases are often difficult to replicate and expand, making it difficult for the entire transformation plan to achieve large-scale spillover effects.

**Digital intelligence, including digital transformation, often ends in failure. The reason lies in the lack of an overall plan. **For example, enterprises are too focused on deploying selected use cases rather than a holistic approach to transformation. According to a global survey by McKinsey, successful digital transformation is not “just another IT project” but a “business-led, ROI-focused transformation” supported by senior leaders (such as board chair, CxO, executive committee).

This conclusion is similar to that of this article.

Third, executors account for about 33% of the sample. These companies are results-driven, adept at leveraging a growing pool of expertise, working with partners, and able to seize opportunities to develop and implement concrete solutions. Although their infrastructure construction is less than that of the above two types of enterprises, they can still achieve significant results.

However, the pain point of "executors" still lies in the contradiction between the part and the whole. It is difficult for such companies to integrate the efforts of all parties into the company's performance and form a joint force.

The last category is "emerging companies", which accounts for about 25% of the sample. These businesses are the least mature and have the smallest benefits; many are just getting started. Many “emerging companies” find it difficult to find where to invest. Only a few companies with the strategies, skills or infrastructure can further develop digital intelligence.

02 Five Secrets of Smart Businesses

Compared with mediocre companies, "leaders" can achieve more than twice the results of other companies in half the time. Why are they so good?

The article summarizes what top players do in five key areas.

Governance

The article points out that for “leaders”, machine intelligence (MI) is a strategic priority. Many companies have also established dedicated centers of excellence (CoE) for this purpose.

This has to be mentioned. Although many companies have awareness of digital transformation, they have also purchased automation tools such as no-code, low-code, and RPA for digital transformation. However, they are limited by complex organizational structures and data between teams. In isolated islands, efficient cooperation within the enterprise cannot be achieved, which makes it difficult to advance the digital intelligence project and a large amount of enterprise resources are wasted.

The Center of Excellence (CoE) mentioned in the article can pool technology, talent, facilities and other resources to supervise the company to do the right things to accelerate the company's transformation goals. Many companies that have introduced RPA have already established CoEs. For example, Dongfeng Nissan CoE Director Chai Yicui once introduced that the corporate CoE plays an important role in seven aspects: positioning, promotion strategy, promotion system, drive and governance, training, information exchange, and seriousness and motivation. In addition, many consulting companies, including PricewaterhouseCoopers and Deloitte, have also established CoEs with different business directions. Among them, Ernst & Young and IBM have announced the establishment of a CoE in the form of a centralized virtual center to help financial institutions use hybrid cloud solutions to accelerate digital transformation.

Additionally, the authors point out that “leaders” prefer a clear process for evaluating and implementing digital innovations, and they also recognize that change is inevitable in this rapidly evolving field. As a result, most “leaders” are constantly evaluating and improving their processes, while “executors” and “planners” tend to get stuck, which limits their ability to successfully scale use cases.

Deployment

“Leaders” will use machine intelligence (MI) as widely as possible.

Each of the “leaders” in the study uses machine intelligence (MI) in areas such as prediction, maintenance optimization, logistics and transportation. Compared with the other three types of companies, "leaders" are also more inclined to adopt more advanced methods.

For example, biopharmaceutical company Amgen is working to develop a proven visual inspection system using AI that will increase particle detection rates by 70% and reduce false alarms by 60%.

The author gives an example at the beginning of the article: Vistra, the largest electricity producer in the United States. To keep factories running efficiently, workers constantly monitor and adjust hundreds of different indicators that even the most skilled operators can't guarantee accuracy. Later, the factory installed an AI-driven tool (heat rate optimizer), which ultimately increased the factory efficiency by 1%. It may not sound like much, but it actually saved millions of dollars and reduced carbon emissions.

Partner

Business-to-business partnerships often occur within academia, start-ups, existing technology suppliers, and external consultants. However, “leaders” prefer to establish connections with broader and more intensive partners in order to maximize their own development speed and learning efficiency.

For example, two consumer goods companies, Colgate-Palmolive and PepsiCo/Frito-Ray, partnered with system supplier Augury and deployed artificial intelligence-driven machine health diagnostic systems on their respective production lines. This decision helped the above-mentioned Both businesses avoided downtime of up to eight days.

Semiconductor company Analog Devices has teamed up with MIT to develop a new machine intelligence (MI) quality control system. The system is able to identify which production processes and tools may be faulty. This means that the company's engineers only need to review 5% of the previous process data, which greatly saves manpower.

It can be seen that although the "leaders" are very capable, they seem to know how to draw energy from external partners better than other companies.

Personnel

“Leaders” will not be stingy. They will enable as many employees as possible to master digital intelligence skills instead of leaving professional knowledge to a few data experts.

The study found that more than half of "leaders" provide basic training in machine intelligence (MI) to front-line employees, compared with only 4% in other companies.

For example, McDonald's restaurants use machine intelligence (MI) to predict customer reactions and real-time customer flow to improve a range of operational tasks.

Data experts in the Enterprise Center of Excellence (CoE) test and develop new methods, then package these developments into easy-to-use tools for field employees. With the help of the system, on-site employees understand the importance of data and develop their ability to identify problems, so they can also give back to the company.

Data availability

The article points out that all "leaders" allow frontline employees to access data, while only 62% of other companies allow it. Additionally, “leaders” obtain data from customers and suppliers. In return, 89% of Leaders share their own data with customers and suppliers.

All in all, this democratization of data stands in stark contrast to other businesses, where among “leaders” information is power and is vigorously guarded.

Building blocks of digital transformation

To sum up, the article points out: When the five aspects of governance, deployment, partnerships, people and data are interlocked and take into account each other, the digital transformation of enterprises will also become effective. However, under normal circumstances, companies will also organize a Center of Excellence (CoE) to coordinate the above five aspects.

All beginnings are hard. The author believes that enterprises determined to transform must first conduct an honest and comprehensive assessment of their current digital intelligence level. At this point, the business can begin to formulate a "transition plan." Even if this plan is rough, it can break down the obstacles that may be encountered in the transformation, such as skilled talent, investment capabilities and critical infrastructure; how to move data from legacy systems to the cloud, etc. In addition, the author believes that the pace of corporate transformation should not be too small. After all, most “leaders” start out using data and simple tools, but as their proficiency increases, they move to embrace more advanced technologies.

03 The gap may widen in the future‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍‍

Digital intelligence is a restructuring of all industries. At present, many large domestic enterprises have not only successfully completed their transformation, but have also extended their transformation experience and technology to all walks of life, constantly enriching the application scenarios for the integration of digital intelligence and industry.

Does this mean that companies that are unfavorable in transformation will be eliminated by the times?

The author believes that the answer is not optimistic. The "leaders" in the article have increased their spending on digital intelligence by 30% to 60% and are expected to increase their budgets by 10% to 15%, while other companies have almost stopped making progress. This means that the gap between the two sides is likely to widen in the future.

However, machine intelligence (MI) has made significant progress recently, and opportunities for comprehensive transformation are just beginning to emerge. Only those who brave the rapids can appreciate the wonders and scenery at the source of the river. Although the starting points of enterprises are different, the development paths are also different. **But at least, where the "number of stones in the river of intelligence" is, we have "leaders" who are pointing the way. **