Amid growing interest in AI, some misunderstandings about AI technology did not appear. CIOs should identify and eliminate such misunderstandings. Only then can a better strategy be created or the existing strategy improved while implementing the AI project. Understanding how AI works and its limitations can help CIOs increase the use of AI to increase business value.
Misconception: AI was a luxury during coronavirus outbreaks
Actually: In the midst of the Corona 19 crisis, interest and investment in AI continued to grow. Recently implemented Gartner SurveyAccording to the report, 24% of companies have increased their AI investment since the onset of the coronavirus outbreak, and 42% have remained unchanged.
During coronavirus outbreaks, AI played a key role in predicting the spread of the virus to medical and government CIOs and in optimizing emergency resources. In addition, all types of companies have played an important role in accelerating recovery operations and AI has also played an important role in initiating cost optimization and business continuity. In the midst of business disruption, it has improved customer interaction and helped boost sales.
While AI is not a disaster, it has the potential to counteract the immediate and long-term effects of corona outbreaks and many organizations cannot ignore it. CIOs must emphasize that AI is not a luxury, but a very versatile and powerful technology. For example, it can be used for practical purposes such as rapidly analyzing more data during and after coronavirus outbreaks and strengthening decision-making.
Misconception: We do not need an AI strategy
Practical: AI can be applied to a variety of business issues, but innovative work value is only realized when there is an AI strategy.
Combining short-term opportunities, especially with the power of AI to increase the work that people do, with work priorities, can help CIOs increase the value of AI. The first step in this direction is to identify the most promising AI usage cases. Among them, we identified those that are consistent with strategic planning and critical business tasks.
For example, automating administrative tasks to increase the time available for innovation. Periodically, your organization’s approach to AI should be re-evaluated and decisions regarding AI implementation (or not using AI) should be investigated and deliberate.
Misconception: AI only replaces simple and repetitive tasks
Reality: Over time, many skills have influenced the way people work, as well as the skills needed to obtain higher income opportunities. So, when new jobs are created repeatedly, some jobs disappear. For example, 10 years ago it was very difficult to find social media marketing managers, today we rarely see paid typists.
AI technology is expected to have a significant impact on work content as well as work and learning practices. AI not only has the ability to automate tasks that are supposed to be routine or repetitive, but can also enhance or change the rest of the tasks through high value tasks. For example, AI can read thousands of legal agreements in a matter of minutes and can gather all the useful information faster and with less error than lawyers.
Identifying activities that can be enhanced or automated with AI, such as project management or customer service, is one way for CI to look at the potential impact of AI on existing tasks. Then, employees will be able to perform their duties more efficiently and quickly with the help of AI without losing their jobs.
The important thing is to communicate frequently and transparently with employees and stakeholders to eliminate concerns about the use of AI. That way, you can reduce negative emotions and help each team prepare for the changes to come.
Misconception: AI and ML are the same thing
Reality: AI is a general term for a wide variety of computer engineering technologies. Inside the AI, there is a large subfield called ML. ML refers to the ability of a machine to learn without special programming. ML can be used to identify patterns from data and is generally good at a specific task. For example, ML can be used to classify emails as spam.
Just as AI and ML are different, so are ML and DL. DL technology, or Deep Neural Networks (DNNs), are making great strides as a type of ML. This does not mean that DL is the best technology to solve all the problems in the AI category.
This does not mean that DNN has always been the most successful AI technology for solving specific challenges. In fact, current AI problems can be effectively solved using governance-based systems or traditional ML.
State of the art AI options are not always the most effective solutions to business problems. Data technology as a whole should be encouraged to look at AI technology and implement the ones that work best with business models and goals. It is advisable to combine DL with other AI technologies (eg physical models or graphs), especially for complex issues that require human insight.
When CIOs talk to their shareholders, it is important to clarify these terms, which are generally subject to change. The whole conversation about AI should be broken down into conversations about personal technologies like ML, showing how each technology can solve real world problems.
Misconception: AI is about algorithms and models
Reality: models Developing and applying ML algorithms to create attendance models is often the easiest part of an AI project. Some difficult areas have well-defined issues with AI and ensures that adequate data is adequately collected, tested, and maintained.
The most difficult part of the AI project is deployment. In fact, by 2023, at least 50% of IT leaders will have difficulty maturing AI prediction projects through proof-of-concept and into production.
CIOs need to focus on defining business issues that AI solves and in order to do so, they must consult with key stakeholders. In addition, individuals, processes, and equipment, such as testing and deployment required for AI operation operations, must be prepared and maintained in a timely manner.
Misconception: All black box AIs must comply with the rules
Reality: Black box AI is an AI system in which input values and processes are hidden so that the user cannot see it. The description level of each AI application depends on the customer as well as the regulatory requirements for privacy, security, algorithmic transparency and digital ethics.
AIs that create insights for internal use do not require much explanation. AIs, on the other hand, which make decisions about individuals (e.g. decisions regarding credit eligibility or credit) need explanation. AIs that make decisions in ‘closed loops’ with significant consequences (such as when driving autonomously) have higher requirements for detail. There may be ethical and legal reasons.
CIOs must ensure that AI applications comply with existing ethical and legal directives. In addition, it should support the testing team and the certification team. This is because the data collected by these teams will determine whether or not the AI application used is detailed. email@example.com