AI, Analysis
16 December 2019

Is every industry ready to implement and truly benefit from AI? Are all industries equally poised to succeed? What are the core use cases? Such considerations are common even among those who have already started their journey towards AI-enabled enterprise. Today’s blog post delves into the futuristic world of Artificial Intelligence.

AI and Industries

According to American consultant company McKinsey, the top 5 industries that stand to benefit by AI value creation include: retail, travel, high tech, transport, and the automotive trades. Why is this the case? Simply because they can automate easier. However, revered venture capital firm – MMC Ventures – lists 31 core AI use cases in these eight sectors:
  • Asset management
  • Healthcare
  • Insurance
  • Law & Compliance
  • Manufacturing
  • Retail
  • Transport
  • Utilities


These two reports show that there is no current common consensus as to who is going to benefit most from the implementation of AI. This could be because research data is lagging behind due to Artificial Intelligence constantly evolving.
McKinsey also claims that 60% of occupations and at least 30% of constituent activities are technically automatable by adapting proven AI technologies. While there are some industries which could benefit more than others, AI can be used everywhere. BRAINHINT customers recruit from across industries, going beyond just the aforementioned ones. Trades like construction, electronics, food packaging, industrial, and many more are included in BRAINHINT’s portfolio of work. All of them have since enjoyed tangible benefits from implementing AI, such as decreased costs and higher productivity.

The Digital Silk Road

China is well ahead of the game in AI adoption, which to some may seem surprising, as it is tempting to think that Silicon Valley and in general the United States have dominated the field. Through their Artificial Intelligence Development Plan (which has included the so-called Social Credit system) they are ahead of most economic powerhouses in both developing core technologies and adoption rate by general economy, especially within Europe. Some reasons for this rapid development may include:
  • Strong government support, including less-stringent data protection policies (especially compared to Europe).
  • Advanced approaches to corporate data (MIT Sloan claims 78% of leading Chinese companies maintain their data in centralised data lake, compared with 43% in the US and 37% in Europe).
  • They have fewer legacy systems.
The European AI ecosystem has to catch-up, with policymakers not making it easier (on the other hand, as citizens we should probably be happy about the privacy level we can enjoy compared with our Chinese counterparts). But does this mean we have lost the game already? We don’t think so.

Industry, Location, or Leadership?

Thanks to huge developments in technology, typical human tasks like understanding, reasoning, and planning can nowadays be performed by AI quite efficiently and at reasonable expense. Naturally, this makes sense that AI would be implemented in industries all-over the world. We at BRAINHINT believe that it is about leaders’ attitude in whether or not their organisations will be among frontrunners or laggards in the AI implementation race. Organisations considering “if” are quickly outpaced by those implementing the “how” and the gap between the latter and the former then expands rapidly. Once organisations start investing in AI, we see a rapid increase in dedicated AI budgets as they see improvements in productivity, revenues, and earnings.

AI Maturity Curve

The AI Maturity Curve, which attempts to measure where a company is in terms of AI implantation, can assist in discovering where your company can improve. This model, proposed by EY (commissioned by Microsoft), includes 5 levels:
  • None – Not yet thinking about AI
  • Planned – AI is being planned but not yet put to active use, not even in early stage
  • Piloting – AI is put to active use, but still only in early stage pilots
  • Released – AI is put to active use in one or a few processes in the company but still quite selectively, and/or not enabling very advanced tasks
  • Advanced – AI is actively contributing to many processes in the company and is enabling quite advanced tasks
The majority of companies across European markets are in the Piloting (41%) or Released (27%) phase. Wherever your organisation might be on this curve (hopefully at least in the middle), it is crucial not only to push forward but to work hard on the clarity of each business case – implementation of AI for the sake of AI can lead to a path of failure or at least dissuade organisations from future implementation when it may be beneficial.

Measuring Success

At BRAINHINT our focus, as with all our customers, is the specific case for your business.  We take time to talk about potential application of AI in your organisation. That’s why we believe there is no single right methodology of measuring success other than time saved or money earned. Be it decreasing cost per click (, minus 7%), operational
effectiveness of debt portfolios (Statima, +36%), automation of a process (Siemens, 100% automation of RFQ verification), and more. Analysis, goal, measures and feedback – the shell where AI feels best – are a must. This is why we put so much pressure on recognising what lies beneath the needs of our customers, assigning them dedicated enterprise advisor instead of just a development team. The perfect AI needs a smart human to get the job done.

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