<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 is not rocket science, but it is essential. To achieve this, make sure you have: 

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 a new way 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. 

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.  

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 more effectively segmenting 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 realize 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 process and how we can help you with your AI implementation. 

Discuss this post

Recommended posts

The hype around the rise of generative AI technologies makes huge promises about the potential of the technology. Yet it would be fair to say the vast majority of organizations are only experimenting with the technology or using it in isolated use cases.
As artificial intelligence (AI) and machine learning (ML) reshape industries and redefine possibilities, mastering the art of AI and ML model development and evaluation has never been more crucial. Behind every powerful AI or ML model lies strategic decisions, intricate algorithms and critical evaluations. Within the Model and Evaluate phases of the Columbus AI Innovation Lab, we delve into these aspects and how to use diverse metrics to evaluate a machine learning model’s performance, along with its strengths and weaknesses.
If you have identified possible AI use cases for your business, the next step will be to test if they are possible to implement and if they will create great value. While there is a lot of momentum and excitement about using AI to propel your business, the reality is only 54% of AI projects are deployed. How do you ensure you’re one of the businesses that does unlock the new opportunities AI promises? Your success with AI begins by discovering AI use cases that work for your business. In the first blog of our Columbus AI blog series, we shared five areas where organizations should focus their efforts to generate ideas for AI implementations based on our experience. After generating some ideas for AI use cases that could potentially benefit your company from the first step of the Columbus AI Innovation Lab, the next step is to test which AI use cases could be operationalized by evaluating them. Columbus AI Innovation Lab
Demand forecasters do the impossible — predict what products and services customers want in the future. Their forecasts inform decision-making about production and inventory levels, pricing, budgeting, hiring and more. "While crystal balls remain imaginary, machine learning (ML) methods can give global supply chain leaders the support they need in the real world to create more accurate forecasts." The goal is to produce exactly the amount of product to meet demand. No more. No less. Demand forecasting is used to anticipate the demand with enough time to manufacture the right stock to get as close to this reality as possible. The cost is high if you don’t get it right. Your customers will go to your competitors if you don’t have what they need. Unfortunately, capacity, demand and cost aren’t always known parameters. Variations in demand, supplies, transportation, lead times and more create uncertainties. Ultimately demand uncertainties greatly influence supply chain performance with widespread effects on production scheduling, inventory planning and transportation. On the heels of the global pandemic, supply chain disruptions and a pending economic downturn, many demand forecasters wish for a crystal ball. While crystal balls remain imaginary, machine learning (ML) methods can give global supply chain leaders the support they need in the real world to create more accurate forecasts.
Only half of the companies starting an AI pilot project are actually executing it. The key is to choose an idea that will benefit your business. Read more about how! In 2022, 27% of chief information officers confirmed they deployed artificial intelligence (AI), according to a Gartner AI survey. Even though businesses across all industries are turning to AI and machine learning, prepare your organization before jumping on the AI bandwagon by considering a few factors. Ask yourself: Is AI necessary for achieving the project requirements or is there another way? Does your team have the skills to support AI and machine learning? How will AI impact your current operations if you adopt it? How will you integrate AI with existing systems? What are the data, security and infrastructure requirements of AI and machine learning? The Gartner AI survey found only 54% of projects made it from the pilot phase to production. After significant investment in AI, why aren’t companies deploying it? We found the problem begins when companies define a use case. Too often, companies are not identifying AI use cases that benefit their businesses and end-users will adopt. The question is then, how should companies unlock the value and new opportunities AI promises? It starts with a systematic approach for each stage of the AI life cycle. We developed the Columbus AI Innovation Lab, a comprehensive method to address and account for all challenges when adding AI to your business operations and bring stakeholders into the process at the right time to help you operationalize AI.
right-arrow share search phone phone-filled menu filter envelope envelope-filled close checkmark caret-down arrow-up arrow-right arrow-left arrow-down