David BECK analyzes technological issues from a political, economic and management perspective.

Industrializing AI-based solutions in their company

David BECK Academic - Society, Politics & Techology

McKinsey and EY-Parthenon surveys confirm that the future will belong to companies that put technology at the center of their outlook, capabilities, and leadership mandate. Globally, 10% of companies have succeeded in industrializing AI-based solutions in their company and 30% have carried out experiments.

McKinsey’s latest survey confirms that the future will belong to companies that put technology at the center of their outlook, capabilities, and leadership mandate.

Faster adoption: the McKinsey previous survey showed that across key areas of the business model, companies’ overall adoption of digital technologies had sped up by three to seven years in a span of months. The newest results show that this acceleration is also happening at the level of core business practices.

Obsolete or viable model: according to the survey, many respondents recognize that their companies’ business models are becoming obsolete. Only 11% believe their current business models will be economically viable through 2023, while another 64% say their companies need to build new digital businesses to help them get there.

Investment is the key: bolder, at-scale investments in technology are significantly more likely to support a successful transformation than those that are smaller in scope. To achieve their ambitions, it’s critical that organizations understand what it really means to differentiate from others on their technology — especially since “technology” and “digital” are such broad terms and mean different things at different organizations.

Talent poses a perennial challenge to companies that are transforming their business through digital and technology. Top economic performers report a greater reliance in hiring new employees. At other companies, respondents report an equal focus on hiring and retraining their current people, and the two groups rely equally on partnering or contracting.

Differentiate with technology: The top-decile performers are making more aggressive plans to differentiate themselves with technology, with some preparing to reinvent their value proposition altogether. Performers have also taken a bolder approach to innovation and now obtain a much larger share of their sales from products or services that didn’t exist one year ago. Top performers are nearly twice as likely to have technology leaders who actively shape overall strategy.

Leadership: despite the importance of involving technology leaders in business decisions, it isn’t sufficient for companies to have a single technology leader responsible for driving a top-performing and digitally enabled business strategy. The importance of digital poses a challenge for company leaders: few are used to engaging with technology, even as it is transforming the requirements of nearly every role and becoming part of everyone’s job. Leaders need to become technology “leaders” — rather than “enablers” or “obstructors” — at their respective organizations.

Commissioned by the General Secretariat for Investment in France, EY-Parthenon conducted a Trusted AI market study in 7 industrial sectors in Europe, North America and Asia-Pacific.

An EY-Parthenon study highlights that industry remains cautious about integrating AI components into processes, products or services on a larger scale.

10% of companies have succeeded in industrializing AI-based solutions

While on average manufacturers invest between 0.4% and 1% of their turnover in projects involving Artificial Intelligence, the industry, led by players in the Technology, Banking, Healthcare and Automotive industries, remains cautious about integrating AI components into industrial processes, products or services on a larger scale.

There are several reasons for this:

  • Difficulties in obtaining quality data to train, maintain the algorithms and be able to deploy the applications at scale;
  • Uncertainty of executives to capture ROI, slowing down decision making;
  • A legal framework that does not address the issue of liability, limiting trust in AI-based systems.

What about an AI granting credit based on biases?

In this regulatory context under construction, developing products and services that meet societal, ethical or technical requirements is a major socio-economic challenge, as is ensuring the right balance between regulation and innovation, taking into account the specifications and existing situation of each industrial sector.

The European Commission published on April 21, 2021 are project “AI Act”, aiming to make Europe the global pole of trustworthy AI. The main objective is to promote the development of AI throughout the European Union, while addressing the potential risks to the safety and fundamental rights of citizens in some of its applications.

AI use cases considered critical

Nine priority industrial sectors were studied: Aeronautics, Insurance, Automotive, Banking, Electricity and Networks, Railways, Minerals and Metals, Oil and Gas, Pharmaceuticals.

AI budget: if in the Automotive or Pharmaceutical Industry, the main players of the sector invest between 0.8 and 0.9% of their turnover, this ratio appears lower in Insurance or Railways (0.5 and 0.7%). Conversely, the Banking Sector, a pioneer in the field, stands out with nearly 1% of the revenues of players dedicated to AI.

Needs: The AI solutions segment (applications, systems and development platforms), represents 85% of the market. Manufacturers need infrastructure (storage space, servers, computing power) as well as support to deploy and audit their AI-based systems.

By geographical areas: EY-Parthenon has estimated the amount of investments of 420 m€ for the purchase of AI solutions, divided between Europe (180 m€ including 40 m€ in France), Asia (130 m€) and North America (110 m€).

The time is now for companies to make bold investments in technology and capabilities that will equip their businesses to outperform others.

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