5 reasons businesses are struggling with large-scale AI integration
Artificial intelligence is an important vehicle for companies looking to automate processes, reduce the cost of operation, or fuel innovation. Despite the positive influence AI-supported activities have on business, a successful implementation won’t happen overnight. First you need a complete understanding of your business goals, technology needs, and the impact AI will have on customers and employees. The majority of employees face challenges or concerns relating to AI adoption, and that needs addressing.
The implication of successful AI adoption is far reaching for businesses undertaking full-cycle digital transformation, which places equal emphasis on automation, innovation, and learning. While employees may experience trepidation at the prospect of AI reshaping or eliminating day-to-day tasks, their productivity could actually increase because more of their time can be directed toward activities that produce value-driven business outcomes. No matter the role or the business unit, AI, automation, and machine learning are changing how work is performed.
As AI becomes pervasive, companies must face challenges head-on. Executives will need to consider the following five areas as they progress with digital transformation and move to invest more heavily in AI.
The adage “out with the old and in with the new” rings true for decision makers who are assessing whether their current infrastructures are intelligent enough to support today’s technology. AI-supported activities require ingestion of vast amounts of data; thus, infrastructure must be agile and scalable. Traditional structures like software-defined infrastructures (SDIs) aren’t necessarily the best option. While SDIs provide flexibility, the structure is limited by the source fixed source code and the administrator who is writing the scripts. More sophisticated AI algorithms and intelligence systems require smarter structures like AI-defined infrastructure (ADIs) and cloud-based networks that can quickly expand based on business needs.
Moreover, while neural networks have existed for decades, only now is massive computing power available at a reasonable cost, which in turn has helped increase the number of layers in these networks. Each layer adds more intelligence but also consumes enormous computing power, which used to be prohibitively expensive. More layers mean better outcomes.
AI is generating a demand for new skill sets in the workplace. However, currently, there is a widespread shortage of talent that possess the knowledge and capabilities to properly build, fuel, and maintain these technologies within their organizations. The lack of well-trained professionals who can build and direct a company’s AI and digital transformation journeys noticeably hinders progress and continues to be a major hurdle for businesses.
To mitigate this, businesses should look inward and enforce on-the-job training and reskilling. For example, LinkedIn just announced it plans to teach all its engineers the basics of using AI. With the proper staff powering AI, employees are able to focus on other critical activities and boost productivity creating a large ROI. If an enterprise’s digital transformation goal is for AI to become a business accelerator, it needs to be an amplifier of its people.
