AI is transforming more aspects of life than anyone can imagine. Every industry, from finance, banking, and healthcare to automotive and manufacturing, recognizes the potential of AI implementation and is beginning to invest. Will those investments, however, pay off? Is your organization truly AI-ready?
Why some AI investments fall short
Stories of data scientists being hired by an organization to ease the AI incorporation into the business through the organization are far from prepared to fully leverage Artificial Intelligence is not uncommon. Considering the immense benefits AI has brought to multiple businesses, notably large tech giants such as Spotify, Netflix, and Amazon who already utilize recommendation engines and machine learning capabilities to elevate customer experience, it is understandable that companies desire to accelerate their AI development.
However, AI integration is not easy and is not for those seeking instant gratification. There are several reasons why some AI investments have failed to meet your business objectives:
No clear vision for data and AI in your organization
Set out for large-scale projects requiring huge investments before testing the water with smaller cases
Unmatched expectations for efficiency at the experimenting stage
Lack of constant feedback loops
“If you think about this as just a technology challenge, you’re missing out on the best opportunities,” says George Westerman, a principal research scientist with the MIT Sloan Initiative, “What you want to do is rethink how you can do business in this digital age. Then AI becomes a tool to get you there. So don’t be the hammer looking for nails. Look for the completed project and find the right hammers for it.”
To guarantee a successful AI deployment, you must first assess how ready your business is.
How AI-ready is your business?
Based on Intel
’s AI Readiness Model and Intellico
’s Data-To-Impact framework, we have divided AI readiness into four different stages:
Stage 1 - Starting point: Organizations who are looking to integrate AI tools into their businesses are in this phase. Companies should then increase their investment in technology and skills to ensure that the necessary technical infrastructure is in place to implement an AI-enabled tool. Launching AI pilot projects to investigate opportunities and familiarize decision-makers with the technology is also critical. Companies that lack in-house technical infrastructure (for example, computing power and storage) can turn to private sector cloud services, which are advantageous for model training and rapid testing due to low entry barriers and pay-per-use service.
Stage 2 - Test the water: Organizations have already invested in IT assets and skills at this point, but have yet to reap the full benefits of these investments. This is the time for leaders to emphasize metrics and invest time and money in running AI pilot projects to demonstrate the importance of data and AI to the organization. These projects will not only provide invaluable insight into the entire AI lifecycle but will also serve as examples of what AI can do for an organization. Implementing enterprise-wide data dashboards at this point can also be advantageous because it demonstrates the value of data and aligns the organization's key performance indicators.
Stage 3 - Change management: This is a critical stage for your company. You must accept changes in government operations brought about by the use of AI-enabled tools in this case. These changes should address two issues: how tasks are performed and workplace awareness and acceptance of AI-enabled tools. It is especially important to focus on reskilling your employees and being open and honest with them about how AI implementation can improve their role.
Stage 4 - AI-ready: At this point, organizations are ready to reap the full benefits of AI. You've got the right technologies and people in place, as well as a clear strategy for how AI can help your business. Your company's culture is data-driven, and information flows freely throughout the organization. It is time to move beyond a few pilot projects and begin deploying AI solutions at scale across the entire value chain.
It’s not enough to only assess if your business already has the suitable technology for the AI transformation. The human factor is just as important.
According to Gianni Giacomelli, senior vice president and business leader of Digital Solutions at Genpact, “AI will impact the HR function more than any other company function. Redeployment is going to be a huge factor that the better companies will learn how to handle.”
Have you prepared your people?
AI is becoming better known but it is far from becoming basic knowledge.
Resistance to change is one of the most difficult challenges of digital transformation. A recent survey
found that upper management is the group most strongly opposed to AI implementation.
According to Kay Firth-Butterfield, executive director of AI-Global, an organization dedicated to the practical and responsible use of artificial intelligence, there is such a lack of understanding about the benefits of the technology that board members are unwilling to invest in it. She also stated that regulatory uncertainty about AI, negative experiences with previous technological innovation, and a refusal to adapt as a defense mechanism to protect shareholders and stakeholders could all be factored in this case.
The question of whether AI will put people out of work is another sensitive issue. It has made many news headlines and driven organizations to carry out research to give the public an answer. According to a 2020 World Economic Forum report
, robots, automation, and artificial intelligence could replace 85 million jobs globally by 2025.
However, it will also create 97 million new jobs in the future. Rather than replacing human workers altogether, AI has an enormous potential to complement human intelligence. Therefore, it is vital for executives to be educated about reskilling possibilities.
Jody Kochansky, head of the Aladdin Product Group at financial services firm BlackRock, supports this hypothesis: “In many respects, with all the data that’s out there, you could argue we’re using machine learning to accelerate human learning. It’s not so much that it takes away the role of the researcher, but that it makes the researcher’s job easier. Ultimately, the combination of humans plus computers is more powerful than humans alone, and certainly more powerful than computers alone.”
It is also critical to train and reskill your employees. To ensure successful reskilling, organizations must invest in training and constantly monitor where the market is, what the competition is doing, and the new skills and technologies available. Great training produces highly skilled analytical personnel who can analyze and interpret data with insight, providing a significant advantage in an AI-dominated environment.
Change is difficult. However, successful AI implementation can be accomplished by balancing new technology and processes, as well as effective change management and reskilling to enable successful human/machine collaboration.
AI-readiness for organizations is a tall order. It requires a cultural shift, a change in the technological paradigm, and most importantly, an establishment of a data-driven value chain that is people-focused–be it the internal employees or the external stakeholders. A tangible, intentional, and change-focused AI strategy will always emerge as the winning combination, regardless of the tools, platforms, skills, or methodologies that organizations use.
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