One of the processes that are radically transforming the world of work as we know it is the use of Artificial Intelligence technologies for the business. The revolution is already underway and is pushing the top management of many companies to revisit their management models to ride the wave of automation.
Successful examples are numerous, from Amazon, using efficient Kiva robots in its warehouses, reducing operating costs, to General Electric, which uses robots for preventive maintenance of its industrial plants.
It is clear that CEOs should also consider the implications of Introducing Artificial Intelligence within their own companies.
A survey conducted by McKinsey on a 3,073 executive and 160 study cases in 10 countries has identified 10 key factors that CEOs have to consider to undertake a successful implementation.
1. Artificial intelligence numbers in the business world
Although investments are increasing, corporate adoption of AI technologies is still limited. Total investments range from $26 to $39 billion in 2016, with tripled values over 2013.
In spite of the investment levels, the spread is still limited: only 20% of the interviewed sample uses one or more technologies (autonomous vehicles, computer vision, language, virtual agents, automatic learning).
We have come to the point of self-sufficiency. Technologies such as neural-based automatic learning and natural language processing are reaching a stage of maturity, so their value is so clear that they can define the AI for the business.
The telecommunications and financial services leading the game, with players in the sector who plan to increase spending on innovation related to Artificial Intelligence by 15% annually over the next three years.
2. More AI more sales (more data)
The 30% of the sample that has adopted the technology said it had increased its turnover, having gained market share or that it has expanded its products and services.
In addition, the first to adopt Artificial Intelligence say they expect their profit margins to grow up to five points higher than their competitors.
A separate analysis reveals that it is already demonstrated how innovation is boosting profits in the same range of associated digital technologies, such as big data and advanced analytics.
3. The weight of a good example
The poll sample declaring a positive experience with AI for business considers it essential to support leadership for a strategy for applying new technologies.
Support not only from CEOs and IT executives but also from the Board of Directors and top management of the so-called C-suite.
4. Automation and partnership
Partnerships need to be developed to increase skills and competencies. Digital giants like Amazon and Google also sought companies and talents beyond their borders to strengthen their skills. Just think about acquiring DeepMind by Google, who is using automatic learning to improve a top business such as SEO.
The study demonstrates how the first to adopt AI has also acquired the right technology solutions; only a few have developed and implemented in-house solutions.
5. The importance of testing
Building a team that deals only with automation activities is not the best idea. Taking responsibility solely to IT technicians may be unproductive, the risk is to launch a technology without being properly tested.
To be sure of its proper functioning, it must be evaluated both by industry leaders and by digital innovation experts.
6. Set up a strategy
A portfolio strategy can accelerate success. The tools offered by Artificial Intelligence are very varied, from those that can solve problems (for example, detection of preventive maintenance schemes) to that less well-offed but equally useful (e.g., a tool to develop a competitive strategy). This approach allows you to work with short, medium or long-term goals.
7. The machine learning boom
Automatic learning is a remarkable tool, attracts a lot of attention from the media and gets significant gains. It is not suitable for everything.
Although there are many fields of application, machine learning is just one of the innovations that can solve business problems. For example, technologies implemented to improve call center performance can be very different from those used to identify credit card fraud.
It is crucial to look for the right tool to solve the problem of a particular business segment.
8. Digital Transformation
An adequate digital transformation must precede the application of the automation systems. The leaders in the adoption of Artificial Intelligence – such as high tech, telecommunications and automotive – are also the most digitized.
Within individual industries, companies that first adopted AI for the business had already invested in digital skills, including big data and cloud infrastructures.
Referring to the statistics, chances of generating profits using technology are 50% higher for companies that have strong digitization experience.
9. The best defense is the attack
Studies on digital delay show how adopting an offensive strategy is one of the most important factors that enable companies to turn a threat into an opportunity.
An organization with an offensive strategy that develops new business models can build more robust paths than those that preceded digitization.
The same seems to happen for AI: the first to adopt it have a greater profit than those who adopted it later.
10. People and processes, the big challenge
In many cases, incorporating Artificial Intelligence into decision-making processes is more important than the technical implementation itself.
The development of the AI is based on principles such as advanced vision, collaboration, and thought building. The business will have to focus on decision-making, on a culture based on continuous improvement and learning.
Expectations related to the use of Artificial Intelligence within companies are remarkably high, due to the innumerable fields of application of the same, as well as the new technologies with the enormous potential that arise every day.