Picking from part 1, part 2 will continue to point out the challenges of AI deployment in businesses.
It’s crucial that a company’s leaders believe in the potential of AI for business and are willing to learn new methods and facilitate changes. However, AI-related decisions require conversations and cooperation on all levels. A corporate culture that promotes experimentation and more dynamic and scalable environments are the best for AI implementation.
Many organizations charge the IT department with the task of AI implementation. That is a mistake. Every employee should be aware of and look for the benefits of AI to be realized in their job. Otherwise, companies risk underutilizing them. For instance, if the marketing team doesn’t know what machine intelligence can do for them, they may never approach the AI team. Successful deployment of an AI solution requires that people running daily business processes should contribute and actively participate throughout the process. In data-driven companies, data scientists should act as links between all departments.
The adoption of AI into an organization’s environment may take significant research and development effort. A lot of initial training and work with data is required before an AI solution can work with the existing system and deliver tangible results. Afterward, monitoring by subject matter experts will be required to ensure the machine is interpreting the change in the business context correctly.
Lack of Technical Expertise
According to Gartner’s 2018 survey, when CIOs surveyed ranked AI as the most problematic technology to implement, the most common complaint was that it demands new skills. Data scientists and professionals with particular technical abilities may be hard to find, and there’s almost always someone willing to pay them more. It’s even harder to hire people with a firm understanding of business strategy and digital technology who can generate insights from corporate data.
Companies may respond to this challenge by training their own AI workforce. According to the 2019 MIT Sloan Management Review and Boston Consulting Group AI Global Executive Study and Research Report, companies that support their existing workforces in gaining AI skills are by 40% more likely to have generated value from AI compared with companies that don’t. Unfortunately, investment in such training doesn’t seem to match the jump in AI usage. Only 62% of executives surveyed by the 2019 RELX Report said their company offers AI training. However, it’s a jump up from 46% in 2018. Of those whose companies don’t offer training, 53% say they plan to do so in the future. 93% believe that U.S. companies should invest in the future AI workforce through university partnerships and other educational initiatives.
After the initial planning, research, and development, time will be required to tailor and configure it to your business and knowledge domain. If your machine learning application manipulates language, it can be even more difficult to put in operation.
The integration of the built AI system into your business processes and IT architecture will require additional time for adaptation. The transition from prototypes to production systems can be time-consuming. You will also have to redesign the business processes around the AI solution.
Fully autonomous AI systems are rare. That will typically mean new roles for the employees who work alongside them. Retraining workers on the new process and system may require considerable time again.
Even a fully autonomous AI system is likely to require some augmentation. During this period, the interaction between the system and the users and observers should occur. The collection of new data sets and baking them into machine learning algorithms may take months on end.
Uncertain Value of AI Applications in Business
According to the same 2019 MIT SMR-BCG AI Report:
- 65% of executives worldwide report that they are not yet seeing value from the AI investments they have made;
- 40% of organizations making “significant investments” in AI do not report business gains from AI;
- only 50% of organizations across maturity groups that have invested in high-risk projects have seen value to date.
It’s difficult to measure and predict the ROI because most companies are only trying AI applications. Moreover, AI and machine learning bring about improvement in quality and efficiency which may be visible not immediately but in the long run.
The goal and the most significant outcome of AI deployment should be the improvement of people’s lives. For example, AI-powered voice user interfaces will facilitate personalized and emotional user experiences, and digital assistants may soon be recognizing customers by face and voice across channels and partners.
Why You Need to Employ AI Applications in Business, after All
Deployment of artificial intelligence for business use can be a long and expensive affair, especially when driven by big data and sophisticated technologies. A costly but limited AI talent pool, ROI uncertainty, and insufficient data are the most common obstacles on this path. However, organizations that overcome these barriers can rely on AI’s power to improve processes, increase employee satisfaction and productivity, and develop a competitive edge.
Before investing in any AI applications, it’s essential to determine whether it’s the best way to achieve your business goals and strategic objectives. The C-suite should shape the company’s AI strategy and support the culture of experimentation and relevant education and training.
The need for progress should be their motivation. Slowly for innovators and too fast for laggards, but the artificial intelligence impact on business will be growing. A range of existing AI solutions already has the potential to transform marketing, customer services, IT, human resources, decision-making, administration, finance, and cybersecurity.
If you anticipate a threat from AI-driven competitors or new entrants, it’s best to start exploring AI’s abilities now. Although the full potential of AI in business is unlikely to be adequately realized for another three to five years, don’t wait till it’s too late. By the time a late adopter is poised to leverage an AI solution, early adopters will be operating at lower costs with better performance. Companies that lag in deploying AI may never be able to catch up.
Source: Becominghuman.aiRelated posts: