How to Evaluate Potential AI and Automation Investments
Technical Complexity of AI Deployment and Potential Benefits
A holistic summary of the company’s business goals and pain points would help optimize the benefits of AI initiatives. Consider the competitive landscape and determine where AI or automation can bring the company closer to achieving its strategic priorities. For example, will AI and Automation help the company accelerate time-to-market for its new products and services?
Similarly, companies should determine which ‘bread points,’ such as inefficient supply chains or poor after-sales service, stand in the way of high performance. Companies need to diagnose the root causes of these problems and should be able to identify which parts of their operations need improvement.
As companies identify areas for improvement, they can consider which areas they lend themselves to automation and digitization. Is the process predictable and repeatable? Is the IT environment stable or changing? If the company is in the midst of a multi-stage IT transformation, it may be very difficult to attempt to introduce automation or AI technology into that precarious context.
Companies should, of course, perform a cost-benefit analysis before embarking on any AI or automation project. Based on our experience of helping companies implement such projects, we know that ROI typically requires balancing the expected benefits against the technical complexities of implementation In order to calculate the business benefits of an AI or automation program, companies should consider how many human transactions the new system will replace and the average time it takes to complete the process.
On the cost side, companies must consider the upfront investment in the implementation of the AI program, maintenance costs, and whether automation will result in lower costs per transaction or other savings.
The more judgment required, the higher the AI capability must be. For example, a life sciences company that is trying to automate the process of reviewing doctor’s reports, evaluating a drug that goes through clinical trials, would need a deep understanding of the industry.
- Pulling the Right Lever at Right Time
Artificial intelligence represents the highest level of business automation. True AI can feel, think, and act. This type of AI is needed to power self-driving cars in complex, unpredictable environments. A medical robot may use AI to analyze human vital signs and advise physicians on diagnosis and possible treatments.
- Cognitive computing systems
can also make sense and think. In a corporate context, such capabilities can make it possible for people to do their jobs better, improve business processes and increase profitability.
Cognitive computing systems may automate actions that people have taken once they have done or make suggestions to people about their next action. Typically, such systems have natural language processing capabilities to collect and analyze large amounts of text, documents, and other forms of unstructured data.
For example, a life sciences company could use such a system to scan records of millions of clinical cases involving its products, detect adverse events, and report them to regulators. Cognitive computing systems generally have machine learning elements that use algorithms to find patterns in data.
For example, a marketing firm could analyze the patterns of consumption of its many consumers. Predictive analytical elements of cognitive computing systems enable companies to analyze data from business processes and find recommended ways to improve results.
- Robotic process automation
gives companies a way to apply automation to smaller projects. Chatbots are still new, but they can already answer customer queries about simple billing questions. In the same way, virtual private assistants make it easier for employees in professional service firms to plan travel.
Assess Your Situation—Then make the Move
- AI and automation are state-of-the-art technology. Worldwide businesses are keen to take advantage of them.
Managers at a leading provider of networking services did not vary when they viewed an opportunity to develop their network. Superior connectivity relies on the effective operation of telecommunications networks. The industry depends on manual detection and network incident resolution — which can be triggered by technological or environmental issues or human errors. It’s an inefficient and time-consuming operation.
This service provider was aimed at improving the network uptime and reducing costs while saving time and improving service. See four series phases:
- By deploying a cognitive smart platform that dynamically combines machine learning, analytics, and robotic automation to predict network failure, the company was able to automate parts of the network maintenance process.
- As the experience of the service provider shows, AI is customized to the operations of a client. Evaluating these opportunities requires careful and detailed assessment of the competitive environment of a organization, its digital maturity, the efficacy of current market practices against industry standards, the potential for changes and the scope of the technical background in which AI and automation are to be implemented.