Currently, we are in the midst of the fourth industrial revolution, fondly called Industry 4.0. A revolution is marked by disruptive instances that change the perceptions of the norm. Industry 4.0 was ushered by innovations like the Internet of Things (IoT), neurotechnology, synthetic biology, artificial intelligence (AI), and so on that changed the way industries functioned. Each of these cutting-edge technologies was tried and tested at smaller scales first, and when found to be a game-changer, they were introduced to the masses.
Let us talk about how AI found its way into your workplace. You need not be a developer or an engineer to have interacted with an AI-based system for your work. While AI has made your walks enjoyable by giving you cool games like Pokemon Go, it has also forayed into your workplace. However, it seems to be another question whether you find it as enjoyable as catching projections in a park.
AI adoption in the enterprise - Still a hype?
In 2021, Data and AI Newsletters surveyed how AI has found use in the enterprise. The 3,574 respondents who belonged to diverse job functions helped gain better insight into how their practice had incorporated AI and their concerns. The survey had been taken by residents of different economies, with the majority from developed nations like the United States, Canada, Germany, the United Kingdom to the residents of developing nations of Asia Pacific, South America, and Africa. About 86% of the respondents had claimed to be working with AI-based systems in their workplace.
The survey results re-iterated the belief that Industry 4.0 has played a role in revolutionizing different sectors. While IT had comprised the most significant share at 17%, the respondents belonged to diverse industries as shown in this break up:
Each of these industries had used AI to enhance productivity, yet only 26% of the respondents were confident that it had a revenue-increasing effect on production. Of course, in sectors like agriculture, where AI is still fledgling, predictive models have been used to contain losses due to disease and parasites. In such cases, the use of AI had significant revenue gains, yet the acceptance is sparse. Therefore, the state of maturity in terms of AI acceptance is yet to increase among the masses since its benefits are still being evaluated. This was confirmed by 35% of the respondents.
The survey also showed how organizations have toyed with the idea of AI incorporation but yet to take the plunge. The hesitation was attributed to the lack of initiatives at the workplace and the inability to find the appropriate use-cases. However, the numbers were promising since they showed that people associate AI with increased productivity and are willing to try it.
The three levels of organizational AI maturity
The adoption of AI at work is primarily driven by the aim to increase productivity, but the demographic's mindset may drive acceptance. Various cultural factors influence how people accept change, especially in the workplace. This is also driven by the fact that not everyone is keen on learning a new skill after reaching a particular stage in their career. According to a report by Ericsson, in countries like India and China, where technical leanings are higher, there was a more enthusiastic approach towards the adoption of new technology. Due to stiff competition and a dynamic technological landscape, the culture thrives on constant improvement. On the other hand, western countries like the UK, US, and Germany are less experimental with tech upheavals at the workplace, although the enthusiasm is catching up.
An organization already using AI solutions can be at any of the three levels of maturity, namely AI beginners, AI followers, and AI leaders. The various aspects that can help categorize your organization are:
- Have there been any AI incorporation initiatives?
- Are AI or analytics-driven solutions used regularly?
- Do the AI or analytics projects have dedicated resources in terms of expertise and budget?
- Are you aware of laws that govern sharing and use of internal and external data?
- What is the number of AI or analytics projects in the organization
The AI leaders are the organizations that allocate dedicated resources to advanced analytics initiatives. They also emphasize the teams being aware of safe data practices. The AI followers are the organizations that had started in-house AI adoption, but their practices were less advanced than the AI leaders. The AI beginners are the novices getting started with AI initiatives and need to allocate resources and set down procedures to move up the maturity ladder.
The hurdles in the way of becoming AI leaders
An organization that aims to reach higher levels of AI maturity needs to tackle challenges that stem from within, that is, its readiness and its people.
The challenges in technology adoption
While AI and advanced analytics promise increased productivity, they may require further resources. An organization needs to be ready to invest in the expertise and the required computing services, including hardware. Additionally, the organization should be prepared for data governance issues as well.
The challenges in acceptance among people
The cultural background and economic situation of a country drive the acceptance trend of its people. On an organizational level, introducing new technology may cause fear of redundancy among the employees. They may feel the inertia to learn something new, or the need to scale up may depreciate their existing skills. Moreover, they may feel lesser in control as AI solutions begin to support their functions.
Apart from the fear, the disparity between the skills possessed by the teams and the expertise required for AI adoption can also prove to be a significant hurdle. The sentiment is common among individuals responsible for the introduction of AI and those who will be using the solutions for operations.
The adoption of AI in organizations is therefore vulnerable to technical and cultural challenges.
Humans and AI, together!
Now that we know the most common challenges faced in becoming an AI-leading organization, the solutions too can be found in the same places. While investing in people is the foremost strategy to foster a healthy human-AI relationship, an organization should also provide the environment for people to practice it. Technology is, therefore, the second factor that an organization should focus on. The deep-rooted factors like culture and economics are more challenging to control, but employee thinking can be conditioned to develop positivity towards skills adoption.
We suggest the following measures that an organization can take to bring together the two major forces of productivity:
- Provide educational workshops and training sessions to familiarize the workforce with AI and its benefits to their functions
- Provide incentives and resources to the employees to develop new skills for AI and analytics
- Gently introduce them to the benefits from the change
- Improve the usability and efficiency of the tools through inputs received from ML observability platforms
- Improve access to data sources and inculcate data governance policies
- Build an ecosystem for support between the teams. This can be achieved through MLOps tools that manage central repositories and versioning
- Familiarize the teams with AI and analytics through proofs of concept and examples
- Encourage the employees to use AI solutions so that they appreciate the increased productivity with the new work practice
- Ensure continual improvement of the AI solutions
Wrapping up
This blog presented some possible solutions for organizations to develop a sustainable human-AI relationship and scale their operations. While AI can genuinely improve the outputs and skills of the workforce, its incorporation is a process that involves constant change.
For AI beginners, the most significant change is taking that initiative to veer the organization towards mature practices. While AI followers will continue to work towards the goal, it is not the end of the road for AI leaders either. The field of AI and analytics is dynamic, and the prospects of gains are limitless. Therefore, organizations will need to prepare for challenges that each new step will bring their way to foster an AI-capable workforce.
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