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AI Insights in Tech

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Mihail Guguvčevski IT Architect

The questions below have been designed to explore the current and future strategies and use of AI, while allowing for discussion of concrete examples from NIL.

How is the current use of artificial intelligence (AI) affecting the operations of technology companies and what are the biggest challenges they face?

AI is a key part of the digital transformation of companies. Companies aim to use new technologies to boost revenues and decrease costs. AI is not just one, but a bundle of different advanced technologies that are not merely an accelerator of digitalization but have the capacity to transform entire industries. It enables us operational efficiency, continuous learning, and numerous innovations. Companies use AI to quickly obtain data-based analyses and discover patterns that would take humans weeks or months—if they were able to discover them at all.

Many companies see AI as the technology of the future that will enable companies to differentiate and gain competitive edge. At present, companies are entertaining the idea of AI from the wrong angle – they are mainly focusing on implementing the technology or experimenting with certain models, algorithms, or built software, most often offered by large public cloud providers. This approach is faulty because in the vast majority of cases it does not take into account the actual business challenge of the companies, which they could implement with the help of AI. Technology alone is not sufficient. What is necessary are new skills, internal development, and building of new competences of employees, as well as redesign of internal processes, change of culture, and setting the business challenge. In addition to the shortage of talent and highly skilled professionals, companies are faced with ethical challenges of AI use, such as algorithm bias and data privacy. Due to the huge amount of data required for the successful implementation of AI technology, it is the latter that increases the risk of potential security and privacy breach in companies. An additional perspective of challenges is that of regulation and compliance, which may vary from country to country or may not yet be fully developed.

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What strategies should be developed by technology companies to remain competitive in a future increasingly focused on AI and automation?

As with the introduction of any new technology, the introduction of AI requires thorough planning, strategy adjustment, and redefining of the company’s vision, which will from now on be further supported by a new technology pillar – AI.

The introduction of AI into business should be strategic, taking into account the implementation of the following short-term steps:

  • Identifying the gaps in AI readiness
  • Finding the AI initiative with the greatest impact
  • Short-term AI strategy
Mihail Guguvčevski, Head of Innovation, NIL, part of Conscia
Mihail Guguvčevski, Head of Innovation, NIL, part of Conscia

The first and most important step is to examine the AI-readiness level of the company. Any answers to the questions about identification will provide management or owners with a clear picture of the current knowledge and status of the company’s AI readiness.

Questions such as whether data are kept in-house, whether you store the data you generate, where the data are stored, whether the data can be accessed quickly and easily, what your technological knowledge is about AI, and so on, are examples of the first iteration. Since not all relevant questions can be defined in the first iteration, additional questions will be formulated in subsequent iterations. The approach is therefore flexible and repetitive and based on strategic experimentation.

In looking for AI initiatives with the greatest impact, we aim to identify the AI initiatives with the greatest impact for the company, while developing AI skills for leaders and teams. Ideas can arise from existing domain knowledge or known inefficiencies in the organization.

Another approach is to examine the company’s longer-term goals and determine if there are problems that AI can solve. Managers of different business units can list key problems and you can assess whether these problems can be eliminated or mitigated using AI. From ideas to initiatives; for each idea, we determine its impact and naturally the input – technological, financial, human resources, etc. Initially, at least one good initiative with a high impact on the organization is sufficient.

The short-term AI strategy must serve and be aligned with long-term objectives. The long-term vision provides the purpose and motivation to promote initiatives in the future. The initiatives in the previous step are used to develop a concrete strategy that is relevant, rather than a generic one that is usually too general. There are two approaches to implementing the short-term strategy: developing use cases and proactively filling gaps.

The development of use cases is the most important step because it involves planning and executing initiatives. After selecting one or two AI use cases with the highest potential, you can start activities such as determining model development, existing infrastructure possibilities, data location, etc.

In the end, it is most important to monitor and track progress during the actual implementation of the strategy. We only know if the strategy is on the right track once value added starts being created. In AI iteration implementation and monitoring the impact of AI on our business, it is advisable to ask yourself about the price you will pay if the company is not ready for the challenges that AI brings in the future.

What areas of the IT industry expect the biggest technological progress in the next ten years and how can NIL and other technology companies prepare for this?

Over the next decade, we expect technological progress in several key areas of the IT industry. Artificial intelligence (AI) and machine learning will continue to transform the industry with advanced applications such as generative models, predictive analytics, and autonomous systems. In cloud computing we expect more flexible, secure, and energy efficient solutions to be developed, with a particular focus on hybrid and edge cloud architectures that make data processing more user-friendly. Quantum computing will transform areas such as cybersecurity, simulation of complex systems, and optimization, although many breakthroughs are still needed for widespread use. In cybersecurity, technology will be even more integrated with automated threat detection and response solutions, taking into account the growth of IoT and the increasing number of cyberattacks. Rapid progress will also be seen in networking technologies that enable ultra-fast and reliable communications, which will accelerate IoT and smart cities. Together, these technologies will transform the IT industry and many other areas including health, logistics, and education.

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NIL and other technology companies can prepare for these changes with some key actions, such as:

  • investing in research and development
  • continuous training of employees
  • infrastructure flexibility strategies
  • focus on cybersecurity
  • developing partnerships and ecosystems
  • adaptive organizational culture
  • automation and use of AI

Can you describe how our SOC (Security Operations Center) is already using AI to improve security operations and what upgrades are planned in the future?

Constant and continuous improvements of our products and services with the latest technical upgrades should be a constant for any successful IT company, especially because technology is changing rapidly and growing exponentially.

In introducing AI technologies, the first steps are often focused on developing or improving the implementation of certain processes. Excellent security operations centers are based on precise processes, strict SLAs, and procedural instructions on service delivery. AI in SOC environments allows for automatic threat detection, alert fatigue reduction, automated response actions, predictive analytics, and faster incident analysis and elimination.

In the future, AI will contribute even more, especially through deeper integration of AI and machine learning, quantum cybersecurity, better integration with cloud services, and extended automation.

Source: This interview was originally published in AmCham’s Dialogue.