Many people consider artificial intelligence (AI) as the technology industry’s future, but with the latest advancements in digital transformation development, the future is already here. Today, many organizations across various industries are applying AI to boost their business operations and maintain a competitive edge in the market.
However, not all cases of digital transformation have been implemented without failures. A good example is IBM Watson’s failure to provide results in personalized health care, Microsoft’s social-learning chatbox, Tay that became anti-social after encountering hostile Twitter followers. The difference between the AI projects that fail and those that succeed mainly lies within the business strategies that organizations take.
In this article, we look at the five strategies that put AI at the center of digital transformation.
1. Take AI as a tool rather than a goal
Usually, many companies fail in the quest to achieve digital transformation as they view AI as an independent goal rather than taking it as a tool that helps improve business operations. The executives should not focus much on developing a unique strategy for AI but on the role that the technology plays in promoting the operations in the company.
In this case, the best strategy would be to analyze the existing challenges and goals keenly and identify the issues that AI can solve. Taking this approach can potentially help the companies find their areas of weakness where applying artificial intelligence is possible and could disproportionately favor business growth. For instance, machine learning algorithms provide unique strengths due to their predictive power.
An example of the application of AI is the use of machine learning algorithms in detecting credit card fraud, which makes the technology a valuable tool in solving a longstanding security issue. The AI enables banking institutions to detect and prevent fraud in time, unlike in the past when fraudulent transactions were only noticed after they have taken place. With the knowledge of past fraudulent transactions, the banks have the necessary raw material to use in machine learning algorithms.
2. Take a portfolio approach to AI projects
Companies should view AI as a tool and also invest in finding AI applications that match their business strategy well. But it is also important to avoid pooling all their AI resources into one project, especially in the initial stages of digital transformation. Instead, we would recommend that companies take a portfolio approach that includes long-term AI projects and quick wins.
Taking a portfolio approach enables companies to acquire the necessary experience with artificial intelligence and also build consensus from within, which can later help implement larger and more transformative AI projects. In this case, the organization can start with thinking about the challenges that their employees face at work and come up with ways that artificial intelligence can help clear the pain points. For instance, introducing voice-based tools that help manage or schedule internal meetings or develop voice interfaces to be used for search purposes.
The quick win projects might not entirely transform the business, but they can expose the employees who might initially be skeptics of the impact of artificial intelligence to the benefits of AI. Moreover, such projects offer organizations with a great opportunity to build skills that would come in handy when dealing with large volumes of data without exposing the business to excessive pressure or risk.
3. Take a Structured Approach to Develop and Reskill your Talent Base
Companies should take a structured approach while adopting AI technologies to help grow their talent base by focusing on hiring external experts and reskilling internal employees. Focusing on developing skills and growing talent is important in an organization since most of the engineers would have graduated long before the latest interest in artificial intelligence and machine learning.
With this in mind, the skills required to implement AI projects might be insufficient in most organizations, making it important to focus on reskilling. For example, Google had started an internal training program in the early days of artificial intelligence that required employees to work in a machine learning team for six months with a mentor. After reskilling, the company distributed the experts into product teams to ensure that the entire company benefits from the technology.
Apart from producing AI technologies, the other important part that companies should also focus on is the field of consuming AI tools. Consequently, managers must have the skills that would enable them to consult these tools and act on insights from the AI tools or recommendations. The executives must have the knowledge that enables them to understand the capabilities and limitations of modern machine learning and decide the right time to lean on such technology models.
4. Address the biases and the specific risks in artificial intelligence
The other important strategy in putting AI at the center of the latest digital trends is understanding the new risks associated with AI and focus on managing these risks proactively from the start. Starting AI projects without knowing the specific risks may result in an unintended adverse impact on society and potentially expose the company to additional legal, reputational, and regulatory risks.
In the recent past, there have been cases of AI technologies discriminating against historically disadvantaged groups. For instance, an Amazon algorithm developed to help in hiring was later found by the company to have a gender bias, while a mortgage algorithm was also found to be racially biased.
The type of biasness exhibited in algorithms occurs since algorithms are products of nurture and nature, just like humans. Moreover, the datasets consist of compilations of human judgments, choices, and other human behaviors and the developers’ decisions on the topic in question.
Therefore, it is important to understand that the algorithms do not create entirely new biases but pick ideas from the human historical biases and apply them on a larger and more damaging scale.
5. Focus on the long term
Artificial intelligence is a relatively new field in technology, which means that it is more likely that many companies will fall into early failures. Therefore, it is important to focus on the long term and continue investing in the technology without being discouraged by the failures.
Besides, early failures are common when companies are trying to adopt new technologies, such as when mobile and cloud computing came into the market. Many companies retreated after the early failures in these technologies and scaled back their efforts before achieving great results in the transformation.
We also anticipate that a similar thing will happen with AI and other new technologies before companies are ready to walk the journey. With the latest advancement and AI applications, the technology is here to stay, so companies should focus more on the long term as the new trend appears to be the future.
Conclusion
In conclusion, many companies across various industries are currently applying AI to improve their business, but with mixed results. However, the difference between the AI projects that fail and the ones that succeed mainly lies in the strategies that businesses follow in achieving digital transformation.
