<img src="https://secure.leadforensics.com/133892.png" alt="" style="display:none;">

A 2021 McKinsey report found that the use of artificial intelligence for marketing efforts, business processes and product/service development has become widespread. In fact, 56% of respondents said their business uses AI for at least one function, which is a 6 percentage-point increase from last year. 

A report from PwC found that a quarter of companies in their latest survey on the topic have widely adopted AI, and another 54% say they are moving toward scaling their investments. 

Although this increased interest in AI is promising, businesses need to take care they’re not just implementing AI tools because they’re trendy. When companies lead with the tool, without thought to process, there can be low user adoption and fewer benefits. 

As PwC says, “too many investments end up as ‘pretty shiny objects’ that don’t pay off.” 

Like with any implementation, make sure your focus is on how the technology can support your business goals. When an AI-enabled solution can help your team do their jobs better, you’ll see increased user adoption and greater ROI. 

Some questions to start with include: 

  • Where are our pain points? 
  • What are our business goals?
  • What are we trying to achieve, and how do we get there? 

With the answers, you and your technology partner can evaluate the ways that AI can be implemented to best benefit your business and employees, such as automating processes, customer segmentation or demand forecasting. 

Unfortunately, businesses often get AI running, accumulate insights, but then don’t do anything with them. Because there wasn’t a plan to get the information into the hands of the employees who needed it, the benefits were muted.  

For example, a finance department introduced AI into their accounts payable process. They automated and streamlined their workflow, with the goal of a significant reduction in overhead and time spent. 

However, three months later, the company was spending the same amount of time on this process. The employees were still doing accounts payable the old way by printing PDFs and typing everything by hand, rather than letting the technology populate the data. It’s just how they’d always done it.

Without giving thought to training and giving them a reason to change their processes, the company wasn’t seeing the benefits of their investment in AI.  

The hardest part of AI implementation isn’t the technology: It’s the people part. Businesses often overlook how difficult it might be for some employees to adapt to new ways, while others may be wary of the new technology or simply confused on how to integrate a new process into their daily tasks.  

To plan for success, businesses need to change the processes for employees. If you introduce new insights and capabilities, you’ve invested in your business for the better. But to reap the rewards of your investment – to see things done faster, more efficiently and at a higher profit – you also need to invest in ensuring user adoption. 

Best practices for user adoption in AI 

User adoption isn't rocket science, but it's essential. To achieve this, make sure you have: 

1. A heavily involved management team 

It’s important for management to understand that the responsibility for achieving strong user adoption falls on their shoulders; the leadership must lead. Management needs to establish a process for communicating and getting the rest of the team excited and on board with the changes.

Access to a new insight or capability itself will not drive cultural change. Remember that employees are less likely to invest time and effort into learning new ways of doing things if management doesn’t show that they believe in and support the change. 

Here’s an example. The management of one company took responsibility for setting a positive tone for user adoption after their solution was implemented. The CEO’s team reviewed the new system every week and led a company-wide dialog on how they could use the new insights to make more informed decisions.

The client experienced increased growth and employee satisfaction because of a shared sense of purpose and investment in a better way of working.

This united effort helped employees see that the new solution was real and important to the company – a testament to how businesses can get optimal value from their data and AI investment. 

2. Active communication 

Although you don’t need to send out an email reminder every week, management should open a dialogue with employees before the new solution goes live, start to answer questions and address concerns, and clearly outline what changes will be made – along with the benefits of those changes.

Keep up steady communication throughout, and be transparent with the changes.  

3. Start small  

Start with small, easy wins so that employees can slowly build trust with the technology, the kind of trust that stems from seeing real results. Even simple projects, such as automating text analysis to help HR evaluate resumes or better segmentation of customers for more targeted sales efforts, have high value.

But more importantly, they offer the opportunity to establish a high user adoption. 

Often, the problems that AI or machine learning can solve are problems the company didn’t realise they had, simply because no one has ever considered an alternative approach. The processes that have been done the same way for years, even though they’re time-consuming, are often primed for AI, which can help make the workflow faster and more efficient.

When your employees have become familiar with the real-world potential of AI, opportunities for integration into other processes will emerge.  

Columbus Global has accumulated a wide range of experiences and best practices on user adoption. Learn more about our processes and how we can help you with your AI implementation by clicking the button below.

Contact us

Topics

Discuss this post

Recommended posts

A successful data breach can ruin a brand’s reputation in the competitive online world. Though some companies can salvage their reputation and remain relevant in their industry, many never recover from a data breach. You should never assume that your disaster recovery plan will be able to save your company if this type of disaster occurs. In fact, you should be tightening your cybersecurity measures across all online touchpoints and invest in preventive measures. This blog explores the anatomy of a data breach, what it can look like, and what you can do to keep your brand and your customers safe. Here’s what you need to know.
Many companies don’t see anything wrong with their data quality, management and governance processes. But upon examination, this often isn’t the case. It’s easy to forget how big of a role humans play; your people are an important cog in the stream of data. They contribute to these streams and if they’re inconsistent in their approach, it can lead to issues further down the line. I talk about this topic in more depth on an episode of Migration Minded, a podcast series created by the D365 Migration Community. Hosted by Tom Wisniewski, Microsoft’s Global Product Marketing Manager for Dynamics 365, he asks me about cloud migrations, the importance of change management, data management and more. Listen to the episode below (or watch it here).
Data has increasingly become an asset in its own right, capable of identifying and generating extensive business value in today’s digital-focused world. Data monetisation alone is a growing industry with an expected growth rate of 24% by 2027. There’s no doubt that it’s set to become more valuable this decade to those who fully embrace it.
Microsoft Power Apps, which is part of the Power Platform stack, was launched to allow organisations to quickly and easily create custom apps, regardless of your coding knowledge. Power Apps allows users to drag and drop components to create apps. Not only does this remove the need for manual code and programming knowledge, but it also makes app development much faster and more convenient.
Big Data is the term given to the immense supply of quantitative and qualitative information produced by people, tablets, laptops and smartphones every day. This blanket term also refers to internal data generated within your company and external data from the market. It includes information produced by your closest and fiercest competitors.
right-arrow share search phone phone-filled menu filter envelope envelope-filled close checkmark caret-down arrow-up arrow-right arrow-left arrow-down