How to democratize AI within your organization?
Artificial intelligence (AI) is poised to play a major role as businesses strive to regain their footing in the wake of the massive human and economic toll caused by the pandemic. The pressure on businesses to use AI was already building prior to the crisis since the technology paid off for early adopters. Many firms are utilizing AI to quickly triage the massive difficulties they confront and define a new route for their employees, customers, and investors in an uncertain, constantly moving landscape, thanks to the COVID-19 crisis. This calls for rapid AI democratization within an organization.
With recent research focusing on the remarkable (and often unrealistic) uses and consequences of AI democratization, it’s unsurprising that many individuals are wary of its implementation in the workplace. One of the simplest ways to help employees gain a practical grasp of AI is to explain how they can utilize the technology to improve their day-to-day efficiency and effectiveness. While only about 5% of professions are likely to be totally automated, AI and similar technologies will alter the character of many current roles, putting a higher emphasis on tasks that require technological, creative, and critical thinking skills without completely removing human interaction.
Dispel common myths
A data scientist’s understanding of AI is frequently vastly different from a business’s understanding. To democratize AI and your data in a commercial environment, you must debunk some common misconceptions.
1. Data Scientists are Magicians
Artificial intelligence and the democratization of AI are overrated. Companies are launching these AI projects for a variety of reasons, not the least of which is to be the next big thing. Data scientists are regular individuals, and hiring one will not instantly transform your company into an AI-enabled one. Instead, the data scientist can use statistical approaches to uncover patterns using AI’s increased abilities. You get more accurate predictions to help you make better decisions in the future. This isn’t magic at all. Instead, it’s about making targeted, well-informed decisions.
2. It’s hard for a business to apply AI
It’s not simple, but it’s not as difficult as its reputation suggests. Some of the complications arise from the fact that a corporation lacks a specific query or aim, but AI cannot both construct and answer the question. Because AI is advanced problem-solving, defining the correct problem yields the desired outcomes. It’s possible that your AI democratization project is failing because you don’t have the correct problem structure in place. You’ll have more success if your team can identify the proper problem.
3. It needs millions of observations for model creation
Businesses sometimes become overly engrossed in the complexities of projects. When modest efforts aimed to bring customers closer or create better predictions will better serve their business outcomes, they follow the newest, coolest technology.
Know your users
Businesses frequently make broad claims, claiming that drag-and-drop tools have democratized data intake, data cleaning, and data mining. Or they say that by automating the entire machine learning or data science process, they have democratized AI sophisticated statistical and computational model creation. But who is able to access these tools and techniques? Have those users received adequate training, not only in the technology but also in the concepts that govern it?
- Provide proper training
Lack of sufficient training in AI creation and deployment could be disastrous, particularly in systems that deal with people’s health or financial well-being. For example, if unskilled or casual users do not grasp the need of separating data into buckets for training, validation, and testing, AI could easily deliver false or unexpected outcomes. If we want to move beyond simply giving access to encouraging the safe use of AI tools, we must first educate casual and power users on the fundamentals of data science and achieve AI democratization.
- Encourage usage of existing business intelligence tools
Employees get more comfortable diving into data to test theories as they become more accustomed to using self-service analytics tools. They begin to trust the data more as time goes on, and they become more data-driven. Finally, such mindsets can help employees better prepare for the use of future AI systems and inspire them to think ahead about how AI can help them solve business problems.
- Encourage and empower non-tech savvy users to embrace AI
Demand for AI use cases will expand as employees discover how they can utilize AI to address their day-to-day difficulties, and successes will spread awareness among all. This enthusiasm may drive companies to invest more in AI talent and data, leading to additional achievements and elation among even non-tech-aware personnel. The cycle will eventually reach a tipping point, where AI will have a natural “pull” and the entire business will be engaged and involved in democratizing AI.
As the aftershocks from the COVID-19 crisis continue to upset business models that were already facing considerable disruption, getting employees on board and enthused about AI is critical to helping them become a part of the work and preparing them for the changes ahead. This can also start a virtuous cycle that will make it easier to make the larger, transformational changes that will be required to become a successful AI-enabled company for the democratization of AI.
How is your organization is planning to accelerate AI adoption and democratized it across businesses? Let me know your thoughts in the comments section below.
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Subex is a leading telecom analytics solution provider and leveraging its solution in areas such as Revenue Assurance, Fraud Management, Partner Management, and IoT Security.