However, you can only unlock the full potential of AI analytics when you achieve adoption across the organization, and not everyone feels positively about the technology. Many employees are nervous about AI, don’t trust its output, feel worried that they’ll make mistakes, or simply share a basic unease when it comes to the unknown.
A recent study from TheyDo reflects ongoing nervousness around AI. According to the survey, one-third of decision-makers feel that their teams lack the skills to use AI effectively. Another 20% fear that AI will increase their data and insights overload, while 77% worry about an over-reliance on existing dashboards and their inability to dig deeper into the data they receive.
Helping everyone in the company to feel comfortable using AI analytics should be a key focus for IT, data, and analytics leaders.
Line-of-business (LOB) team members “may not know how to turn questions into queries that deliver useful answers, how to word them in ways that chatbots can understand, or which visualizations to request in order to gain the insights they need,” says Avi Perez, CTO of Pyramid Analytics. “Your data team has to be ready and waiting to support their line-of-business colleagues. They are going to have to step out of their usual role to serve as guides to BI thinking,” he advises.
Here are some practical ways that leaders can ease the path into AI analytics for all company’s employees.
Promulgate Basic Data Literacy
One key step involves focusing on underlying concepts of data management and analysis. LOB users can’t start using AI to turn data into useful insights until they know which data to access, what insights to request, and how an AI engine interprets their word choices. The TheyDo study reveals that 55% of employees feel that they don’t know how to identify valuable insights within the reports they receive or make the most of the data they get.
Data enablers need to teach prospective citizen data scientists how to turn their questions into coherent queries, what wording to use to reach the insights they need, and how to choose the right visualizations for various purposes.
One option is to use AI-powered educational platforms, but it’s also a good idea to bring in subject matter experts to run workshops and training sessions.
Reassure AI Skeptics
Some employees might simply be scared of using AI. Common fears include worrying that AI might take their jobs, and feeling nervous because AI is new and unfamiliar.
Many mainstream media outlets sensationalize AI tools as the slippery slope to a world run by robots who eradicate the human race (yes, the fear is out there, and no, it won’t happen). Even the alarmist claims that AI will take people’s jobs seem to lack a basis in reality, as most AI users find that the technology makes their own efforts more effective and efficient – not obsolete.
LOB team members need to be reassured that the worst is not likely to occur. Explaining, as much as possible, how AI models work can reduce the sense of an unknown and mysterious force. It’s often a good idea to assign low-stakes AI projects, so that users have time to grow comfortable using AI in a low-stress way.
Educate About AI Risks
At the same time, it’s crucial to educate about the real risks around AI. People need to understand issues like hallucination and bias and know how to spot and correct for them. Data privacy and data security should also be part of AI familiarity training.
Citizen data scientists should know which data can and can’t be inputted into an AI model, alongside reassurance that AI won’t somehow steal their own private data.
This will encourage LOB users who are scared about being led astray by AI insights or becoming the scapegoat for an AI-related data breach. It also helps calm executive leaders’ worries about these eventualities, so that they can promote widespread AI analytics usage with more confidence.
Empower Effective AI Analytics Use
Decision makers who aren’t comfortable with data and emerging tech need support to work out how to make the most of AI analytics. They might not be nervous or scared, but simply swamped by the possibilities in front of them. It’s a concern that arises in the TheyDo survey, with half of respondents saying that they feel overwhelmed by the amount of insights and dashboards that they receive every day.
Data and analytics experts can educate their colleagues about how to approach AI-powered insights and use them to advance each person’s goals.
An AI mentor could be assigned to every department to work with team leaders and identify the best ways to integrate AI into their processes so as to deliver maximum value in the minimum time. Together, they can choose the most suitable AI tools and develop a tailored AI strategy.
People Management Is Crucial for AI Analytics Adoption
Successful AI analytics adoption relies at least as much on educating and supporting people as on handling tech tools and infrastructure. Data, IT, and analytics teams need to step outside the box of tech management and data wrangling. By guiding and encouraging their colleagues, they can help the organization as a whole to overcome uncertainties and feel comfortable working with AI analytics.
