AI is becoming embedded in many of the things we interact with daily. The emergence of some remarkable capabilities over the last decade made this possible. AI is now not only in lab environments but also in real-world examples and commercial practices. Yet, there are still questions around AI: Is it overhyped, or is it achievable and accessible?
The skepticism about AI has some grounds based on organizations' unsuccessful uses and experience. On the one hand, we hear favorable predictions. McKinsey says by 2030, 70 percent of companies will have adopted at least one type of AI technology. Then there are other examples like "Why 85% of AI projects fail."
To avoid worrying about all these bold statements we need to think about how to inspect AI. AI models are not self-sufficient beings, they work in tandem with much bigger objects and technologies to decide and act. This means that the need for the right environment for AI is a must. Even so, setting the right environment can be a difficult and expensive task. The systems in which AI operates should be reliable, flexible, and scalable. Otherwise, it is not possible to cope with changing data structures, business objectives, and demands. Organizations that use AI will be successful in setting the right environment for one specific case but not for all.
Another challenge to making AI thrive is to feed it with the right data. Not any data or even more data but strategically and purposefully chosen data. Organizations often tend to lift and shift their data into cloud environments, as well as expect a better return on investment from sophisticated AI algorithms. Yet, without any change in variety, velocity, or quality of the data being used by these algorithms-- which are intended for machine learning -- they barely move the dial. In particular, today’s ever-increasing customer digital footprints state that we have to manage the complexity of data through well-integrated and federated layers and make real-time data available for AI algorithms.
Organizations might be lucky enough to overcome environmental and data challenges to deploy artificial intelligence solutions. Yet, there is still one big hurdle to jump and that is the lack of AI talent. This issue has been going on for the past couple of years. The supply has not caught up with the demand despite all training and upgrading efforts being made. Hope to have people who only have theoretical knowledge also were proven to be wrong to guarantee success in a commercial environment. The skills gaps are prevalent across all three talent segments, data scientists, data engineers, and developers. It is unlikely to find the right people in key aspects of building AI succeed. Different talents are needed to build the right data pipeline and put scalable infrastructure in place.
All of this might paint a grim picture but there is good news too. AI is indeed achievable and accessible. For organizations that can't or are unenthusiastic to build their end-to-end AI systems, an artificial intelligence service known as AI-as-a-Service is what they need. It allows businesses to experiment with artificial intelligence in a low-risk environment. It brings many benefits for these organizations across the board:
- Provide advanced infrastructure at a fraction of the cost - Bring greater flexibility and scalability - Bridge the gap between science and execution - Overcome the AI skills gap
Organizations agree that the adoption of AI results in better revenue, cost, and profitability. This is valid across industries and regions. Either you are a big enterprise or a small to mid-size business you have to figure out how to embed AI into your business. In particular, SMBs around the world need AI right now to compete against tech giants before it becomes too late.
Now we are ready to ask that initial question again, Is AI overhyped? The simple answer is "No".
Is it achievable and accessible? "Yes, if you reach out for the right AI as a Service product.