June 15, 2024 in Artificial Intelligence

3 Hurdles to AI Adoption and How to Overcome Them

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Artificial Intelligence (AI) is no longer an optional advantage, but a competitive imperative. Recent advances in AI have made AI technology more accessible, rapidly speeding adoption. In fact, 73% of organizations are now prioritizing AI projects over other digital initiatives due to the accessibility and potential transformative change AI can provide

Nonetheless, significant hurdles still stand in the way. According to Gartner research, only 15% of AI projects will be successful. Often, organizations embark on these projects without defining or addressing critical requirements and struggle to retroactively correct their oversights. Namely, organizations must carefully consider:

  • How will their programs ensure consistent quality data? 
  • Which deployment environment will suit the solution over its lifetime?
  • How will the solution meet evolving cybersecurity needs?

Addressing these conditions from the outset helps ensure the success and longevity of the AI program. In the following sections, we will review key considerations and recommendations for each topic.

Ensuring Consistent, Quality Data

AI will only ever be as good as the data it is built on. In fact, industry reports suggest data quality issues are the leading cause of failure for AI initiatives. The key to capturing high-quality data is effective data synchronization. Data synchronization is the process of establishing consistency within data sets so they can be successfully used by machine learning models. This is far from a trivial process. Developers must account for a litany of potential challenges like network inconsistencies, data changes, asynchronous syncs, server errors and more.

Eurotech ensures effective data synchronization by leveraging digital twins. Captured data is used to create the digital twin, a real time and accurate representation of the physical asset or system from which the AI can be powered. This “twin” then acts as a sole source of truth to which incoming data can be easily compared and synchronized.

Edge, Cloud or Hybrid?

Where companies choose to deploy their AI depends on a host of factors including the type of AI they want to develop, the data and talent they will use to develop it, and where they are in their AI journey. Each solution offers advantages that may be more or less important depending on these factors.

  • Development Costs: Depending on the use case, the cloud often offers tangible benefits during product development. Developers can pull data from distributed sources to train their models, use existing data sets and tools, and even leverage pre-existing models. 
  • Lifetime Costs: While cloud only applications have an advantage during the project development phase, deploying applications at the edge often provides a better lifetime value. Data transfer costs can add up quickly, particularly for solutions that require more data or are globally deployed. These costs can be minimized with an initial investment in edge infrastructure.
  • Latency: Edge AI can enable faster and more accurate inference, data analysis and decision making by processing data locally. In turn, this processing speed can enable more automation and flexibility in the end solution.
  • Reliability: Edge AI provides strong benefits in terms of reliability as well. By running programs locally, companies can minimize downtime related to connectivity — an important consideration especially in mission critical applications.

The impact of each benefit is dependent on the application, and in the end, the best solution is often a mix of both edge and cloud. Eurotech offers a full suite of scalable edge solutions paired with edge software that provides seamless, agnostic cloud connectivity, empowering companies to choose the best option to meet their unique needs.

Security by Design

With all the promise of AI, cybersecurity risks cannot be underestimated. To implement AI-based solutions, companies must open OT systems to new, potentially significant risks. To mitigate these risks, companies must approach their AI projects with a security-first mindset — by building safeguards for each step of their solution from day one. This includes everything from physical hardening of edge devices to developing a secure “chain of trust” for data communication. Thankfully, standards exist that can guide companies to adopting cybersecurity best practices. Foremost among these is ISA/IEC62443, which covers every aspect of industrial cybersecurity from risk assessment to operations. At Eurotech, we provide a full suite of IEC62443 certified integrated hardware & software solutions, helping our customers ensure compliance and security from the start.

There is no doubt that the rapid rise in AI adoption is making it a competitive imperative. Yet despite its widespread popularity, companies still struggle to launch their AI solutions. Overcoming these hurdles requires careful consideration of data quality, solution architecture, and cyber security. Organizations like Eurotech provide the tools to address each of these needs and enable the development of successful, long-lived solutions. 

Want to learn more? Click here to view Eurotech’s whitepaper.

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