Having made the suggestion the organization you work for needs artificial intelligence (AI) to support future growth, you are now required to make a presentation to the board of directors and convince senior leadership to invest in AI. Below are a few key considerations to consider while making the business case for AI.
In terms of profits, an AI program should not be implemented simply because a few individuals are fascinated with it. There must be specific reasons for implementing AI, and it must be able to pay for itself. Providing honest, accurate information that supports the use of AI will help the board of directors make an objective, well-informed decision.
It will be important to discuss the specific business opportunities and problems the AI program will address, and how the program will be implemented.
The meaning of the terms “artificial intelligence” and “machine learning” have recently been merged, causing some confusion. Consider the possibility you may be promoting machine learning, rather than artificial intelligence. There are significant differences. While the two terms may be interchangeable during casual conversations, anyone promoting the use of AI should understand their differences.
Machine Learning vs. Artificial Intelligence
Machine learning algorithms started out as a method for developing artificial intelligence. An algorithm provides limited responses for a variety of tasks. For example, a machine learning algorithm can answer the phone and provide a limited number of responses, and it can be used to detect anomalies in electronic banking transactions.
Theoretically, a large number of machine learning algorithms could be combined to form artificial intelligence – but this hasn’t been done so far. Machine learning is still used in developing artificial intelligence, but has also become a separate industry that performs specific tasks.
In the 1950s, Alan Turing suggested that if a machine could engage in a conversation through the use of a teleprinter, and imitate a human-to-human conversation with no apparent differences, then the machine can be described as thinking. Using this description, we have achieved artificial intelligence. (We now expect our AIs to talk, and we can usually tell when we’re talking to an AI.) Developments such as visual perception, speech recognition, decision-making, and word translation are all developments supporting AI.
Artificial intelligence programs and machine learning algorithms are often expensive, but they do not have to be. Finding experienced staff to build or maintain the programs, on the other hand, can be difficult (and expensive). But if an organization can afford the staff for AI, it can improve efficiency and promote profits when used wisely.
Considerations in Making the Business Case for AI and Machine Learning
Building a business case that supports the purchase, installation, maintenance, and use of an AI program or machine learning algorithms should include an analysis of the expected costs and benefits. Unfortunately, AI projects can become quite expensive and may not provide immediate gains – profits and efficiency are expressed over the long term.
If an organization is considering AI, it should be safe to assume a Data Governance (DG) program is in place. (If there’s not, it might be reasonable to invest in a DG program prior to an AI program.) The Data Governance program can be used as evidence of how long-term efficiency and profits can increase with a significant and successful investment in software.
Also, if the competition is currently using artificial intelligence, a little research could provide additional supporting evidence to make the case for your organization to invest in ML or AI.
An important question to answer is: How long before there is a return on the investment (ROI)? This may be difficult to predict, but an effort can nonetheless be made.
What’s more, not all ROIs are difficult to predict. For example, an electricity producer using artificial intelligence with predictive maintenance tools can track their equipment’s up and down time, and maintenance costs, to accurately predict and measure returns on their investment. This example can be used as a base for your ROI, though your prediction will probably be based on much more flexible information.
The majority of AI projects generally take time to realize an ROI, and this makes it difficult to predict the costs and benefits.
The scarcity of experienced AI talent makes them expensive to hire or contract. A primary reason businesses hesitate to move forward with AI solutions is the lack of talent availability. In essence, the plan requires finding staff, so having a handful of applications or a list of potential contractors would certainly help in making your case.
If the AI works with a standardized data format, preparing the data for it might be a nuisance. Theoretically, the Data Governance program should be providing uniform data, but if not, it can be done through the use of appropriate software or manually (which is more time-consuming). This concern should be included in the predicted expenses.
Be as honest and accurate as possible about the expected costs and communicate the actual benefits of using AI. It is important to approach AI projects with a long-term view.
Popular AI Use Cases
Artificial Intelligence as a Service can be used to solve data processing problems. Rented AI eliminates concerns such as experienced staff and maintenance. Artificial Intelligence as a Service and Machine Learning as a Service can (depending on the service) be moderately priced, or very expensive.
Virtual assistants assist in conducting business. They are also one of the cheapest forms of AI available (they are often a free service, offered in the hope of increasing sales for their providers). They use artificial emotional intelligence and advanced Natural Language Understanding (NLU) and to better understand natural language commands while learning from the situations.
Virtual assistants can conduct web searches and find answers. They can respond to a variety of voice commands, schedule appointments, and send text messages. Virtual Assistants can also play music and control devices, such as door locks, lights, thermostats, and smart home devices.
Chatbots are used for customer support. Chatbots rely on a combination of artificial intelligence, natural language, and machine learning processing algorithms. This combination allows the chatbot to learn and use context, which helps to personalize conversations.
They can be available when the organization is closed, responding to online questions 24/7, in real time, allowing human staff to focus on other projects. Chatbots can also be used to collect information from new customers.
Cognitive automation is a combination of artificial intelligence and automation. It uses AI to work with business process automation (BPA) and robotic process automation (RPA). Cognitive automation expands and broadens the range of actions automated processes can perform. It allows “robots” and software “bots” to perform tasks, such as:
- Streamlining workflows: Cognitive automation can be used to automate workflows for greater efficiency
- Using natural language understanding, machine vision, optical character recognition (OCR), or speech recognition for intelligent data capturing
- Using AI decision engines (includes analytics) to replace traditional management systems
- Automatically retrieving customer data as it responds to a service call, while using natural language understanding and speech recognition. For example, the user’s location, demographics, and purchase history can be analyzed very quickly using AI, which will then present the most relevant offers for that customer.
Cognitive automation covers a broad range of ways to combine artificial intelligence with automation to improve business outcomes. Basic cognitive services are often customized, rather than designed from scratch.
Health care technology uses artificial intelligence to help in diagnosing disease, developing medicine, monitoring patients, and more. Because artificial intelligence continues to learn as it is used, it also learns about the patient, the medicine, and the disease.
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