In a world of increasing technology scope, where data monitoring and analysis play a crucial role in processes, many organizations are considering to employ digital enhancements.
Due to a growing skill gap, and a global shortage of IT professionals (3.5 million unfilled IT positions globally by 2021), and growing data (there is more than 2.7 Zettabytes of data exist today) companies strive for working hands.
While all this time, artificial intelligence methodologies can play a crucial role in replacing a broad range of IT operations processes and tasks, freeing the IT team to handle real IT issues.
So why do companies lag behind in the adoption of AI? Let’s look into some of the core reasons for the causes.
1. Limited Experience of the Team
This isn't to say that IT teams aren't qualified. They are! But with under-staffing issues mentioned above, and growing amounts of data which require professionals to deal with a lot of maintenance and monitoring, attention is shifted from strategy to day to day maintenance.
Despite AI advancement, there has been a slow increase in companies which adopt AI due to some challenges that arise despite a very ambitious digital transformation process. With new AI solutions, providing end to end hand holding during the AI adoption process, enterprises need to act now to bring their existing staff up to speed, so they leverage the possibilities of AI today.
The need to improve team skills is crucial incorrect AI adoption: The urgent need to train staff for new tools and managing new systems. Top employees are already quite skilled in running existing systems. Replacing those with new business tasks requires deep and long retraining of staff. Introducing AI systems requires extra time and effort to train and integrate.
2. Artificial intelligence implementation is not an overnight process
AI adoption is a not an overnight process as there is a difference between AI machines and tools. Any machine learning (ML) methodology requires human feedback and a learning curve and is by all means NOT automatic. Therefore, enterprises must consider a prolonged process to truly enjoy the vast benefits of AI-based solutions. Businesses interested in a real AI-backed digital transformation should seek to take advantage of AI to analyze and extract data for predictions/failures. However, challenges do arrive! For AIOps to succeed, there must be a higher effort and investment in employee skill set and enterprise structures.
Successful AI integration requires enterprises to overcome the barriers between IT and businesses teams. For both the departments to perform optimally, with the right mindset that AI implementation is a prolonged process, businesses should provide them with guidance and training, allow a reasonable time span for machine learning and conduct periodical training, exposure to professional conferences and materials, set the right KPIs and rethink compensation and performance bonuses.
3. The legal and social barrier
Even though AI is not a new concept, the groundwork for legal, ethical and social issues in AI adoption- has not yet been fully established. Reality is that technologies are booming before actual legislation has been commenced (as in the case of civilian drone usage). Both executives and consumers must look upon the adoption as a positive aspect instead of some infiltrating mechanism. New legal and regulatory guidelines need to be drawn up to cover liability and privacy in AI systems.
Acknowledging the complexity of the whole process
AI is not a plug-and-play solution. It requires a holistic organizational preparation and rearrangement, syncing in the right stakeholders, from decision-makers to hands-on workers, thus prepating the ground for a true AIOps revolution.
A lot of intensive work, which is worth enhancing customer experience, driving innovation into the organization, and maintaining a functioning, well-orchestrated operation.
Then, and only then, businesses can achieve IT Operational Excellence.
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