While many companies are attempting to develop AI for various applications, there is a considerable gap between the goals that organizations want to achieve and the reality of the insights that data and programs provide.
The ambitions of an organization for using artificial intelligence (AI) and the reality of how such projects play out are vastly different. Emotional intelligence and mindfulness are two essential aspects. The pandemic highlighted this flaw – people’s capacity to stay focused and mindful can be compromised in a remote working environment. When AI is utilized in a cyber-attack, such as when someone tries to deploy a chatbot or another adversarial machine learning technology against organizations, this could be a significant issue.
Aligning AI with cloud computing
Only 20% of respondents in a 2017 Harvard Business Review survey of 3,000 executives from 14 industries indicated they have used AI as part of their core business.
It is extremely vital that enterprises link AI with their cloud computing and cybersecurity strategies in order to bridge the gap between ambition and reality in AI. When companies think about other ongoing digital transformation initiatives — such as cybersecurity and cloud computing — and combine them with AI initiatives, they create a force multiplier. These projects don’t all demand the same abilities, proceed at the same pace, or have the same objectives, but they do complement one other. Cloud computing – as a repository for large amounts of data – can act as a catalyst for AI. Another consideration is cybersecurity, as data, data models, and algorithms must be safeguarded.
Because the industry is rapidly evolving, business leaders must keep up with trends, however, some emerging trends are still years away from being implemented. Quantum computing and neuromorphic computing are both fascinating fields of research, but neither has commercial applicability yet.
Organizations must start thinking about mindfulness and emotional intelligence of AI. Individuals are forced to work on numerous tasks at the same time in the current stage of the COVID-19 pandemic, compromising their ability to stay focused and mindful.
While AI investments have risen significantly, many of them are motivated by a fear of missing out rather than success in AI development and deployment. Apart from the excitement, investment, and activity surrounding AI, businesses require a realistic strategy. One thing to keep in mind is that AI isn’t always the best solution in some cases. Before attempting to choose whether or not to run deep learning applications, it is critical to have a complete understanding of the problem that needs to be solved.
Having a data strategy
Another gap that businesses frequently overlook is the amount of data they possess and what they can do with it, but they don’t. Many businesses lack the ability to manage the data they require to succeed. A data scientist’s primary responsibility is to make data accessible, manageable, and governable so that it can be used. At the time of creation, data needs to be categorized, handled, and labeled.
An organization must first construct a data strategy before developing an AI strategy. It is crucial that scientists, researchers, and engineers approach data and Artificial Intelligence I systems with a human-centric mindset.