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Unlocking AI’s potential in Estonia’s industrial landscape: a tale of challenges and opportunities

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In our fast-evolving industrial world, integrating AI has become a pivotal strategy for companies striving to maintain a competitive edge. When implemented effectively, AI ushers in a new era of efficiency and productivity across industrial processes. The advantages of AI adoption are diverse – from elevating productivity and enhancing quality control to enabling predictive maintenance and data-driven decision-making. Early AI adopters gain a competitive advantage and position themselves as industry trailblazers, poised to navigate the challenges and seize the opportunities of the modern industrial landscape.

The industrial sector is the largest segment of Estonia’s economy and plays a vital role in propelling economic growth. Industrial enterprises create many job opportunities and pave the way for various other forms of businesses to thrive.

Estonia is widely acclaimed for its digital innovations and flourishing startup ecosystem. Moreover, Estonia boasts the highest density of AI startups per million people . Despite this, somewhat surprisingly, Estonia has witnessed limited AI adoption within its industrial sector, with just 4% of companies incorporating AI last year.

Although, it has not been a problem-free year for industrial companies in Estonia. Statistics Estonia, noted that the decrease in industrial production has been a prevailing trend throughout the year. Notably, shale oil production was a rare exception, showing a significant increase of 22.0%. Among larger industries, output decreased in wood manufacturing (15.8%), electrical equipment (7.2%), fabricated metal products (8.1%), electronic products (18.9%), and food products (7.1%).

Given these concerning statistics, I came across a master’s thesis titled “Challenges Faced in Implementing AI in Estonian Manufacturing and Retail Companies” by Andrei Hirvi. Intrigued by the topic, I had the opportunity to sit down with him for an insightful conversation. Our discussion aimed to unravel companies’ key hurdles and explore potential solutions.

Primary challenges to implementing AI in industrial processes

When looking into what were the primary challenges that companies face when trying to implement AI/ML solutions, I see a lot of similarities with digital transformation projects in the public sector

The primary roadblock that emerged consistently was the lack of upper management support. Despite the internal initiatives, progress remained stagnant without backing from top-level executives. During his research, Andrei also noticed a common misconception among companies: many believed they lacked the necessary data to commence their AI journey. In reality, this often isn’t the true impediment. Lastly, it’s common for companies to overlook the importance of an end-to-end solution. Integrating AI with existing IT systems and clearly defining the end users often falls by the wayside.

A notable challenge specific to industrial companies in Estonia has been the absence of a data-driven mindset or data-driven work culture. This entails questions such as: Do we effectively utilize data? Are we actively collecting it? And how do we shape our decision-making processes around it?

A company has overcome challenges and is ready to innovate. Where to start?

  1. Top-down commitment: It must begin with a top-down commitment from the company’s leadership, a collective decision to invest in the initiative.
  2. Financial preparedness: Determine the extent of your financial readiness. Clearly define how much capital you’re willing to invest in the endeavour.
  3. In-house expertise vs. partnership: While possessing the full spectrum of in-house expertise is unnecessary, decide whether to bring in a dedicated in-house leader to spearhead the project or explore partnering with an external expert.

However, it is important to note out with the right work culture in place, innovation has the potential to spring from every member of the workforce.

How can the public sector support industrial companies?

While most companies find satisfaction in their innovative solutions, they often grapple with fears that require mitigation. Implementing AI represents a high-risk investment. States can play a pivotal role in risk reduction through various grant programs and mentoring initiatives. Additionally, Andrei has suggested that the state focuses on formulating a comprehensive strategy for employees displaced by AI. This challenge extends beyond the industrial sector, as future technological advancements will impact all of society.

While AI flourishes in startups, the industrial sector faces persistent challenges. These challenges resemble those found in public sector digital transformations: the absence of upper management support, misconceptions about data availability, and the oversight of comprehensive AI solutions. Amid these challenges and promising opportunities, embracing AI is a clear pathway to progress within Estonia’s industrial landscape. With a well-defined strategy and ample resources, transformation becomes an attainable goal.

The European Union has established a formidable goal: By 2030, 75% of European enterprises are expected to integrate cloud computing services, big data, and artificial intelligence into their operations. While this aspiration is commendable, it presents distinct challenges, particularly for countries with more modest economic footprints, such as Estonia and other smaller nations. The majority of enterprises in Estonia are Small and Medium-sized Enterprises (SMEs); in contrast to countries with more larger corporations that often possess the internal expertise or financial capacity to acquire such expertise through strategic partnerships readily,  SMEs do not enjoy the same advantages in this regard.

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