Companies have been using customer relationship management (CRM) software as part of their back-office operations for decades. It provides automated workflow, centralized document management, outsourced management, integrated order processing, and advanced analytics and reporting to increase productivity.
However, over the past two decades, there have been significant shifts in outsourcing, automation, compliance expectations, and customer operational structuring due to the advancements in artificial intelligence (AI) technology. Many business owners and managers assume that they can integrate AI into their organizations and let it take full control over all their back-office operations.
Unfortunately, AI is not a simple plug-and-play tool that you can quickly integrate into the CRM of an organization. Even though the AI is powerful enough to manage customer relationships and back-office operations, there must be proper execution for it to work effectively for companies. Otherwise, the AI cannot conduct the necessary back-office operations that align with the company’s organizational structure and goals.
The Evolution of Back-Office Operations
Traditionally, companies hired internal office workers to oversee common administrative tasks, such as payroll, accounting, human resources, IT, compliance, risk management, data entry, record keeping, and inventory management. But the high labor costs of employing administrative workers led companies to look for more cost-efficient ways to perform back-office operational tasks.
Around the early 2000s, companies began remotely outsourcing these tasks to low-wage workers in other countries. In the 2010s, a shift was made toward robotic process automation (RPA), one of the earliest examples of technology automating back-office operations. It could automate common manual tasks, such as payroll processing, data entry, and answering basic customer questions and inquiries.
Today, generative AI technology has the ability to bring more intelligence to back-office operations management. Since AI is adaptive, it can learn to understand and interpret new data. It does not rely on preprogrammed automated responses the way RPA does. The advanced algorithms give AI technology the potential to understand a specific company’s back-office operations and make unique decisions that are more effective in managing them. The only problem is that companies continuously fail to execute them properly.
Execution Failure: The Reasons for AI Back-Office Operational Failure
According to published research studies from MIT, roughly 95% of all AI projects fail to deliver anything of significant value to companies. Too many business leaders assume that AI technology is smart enough to interpret the operational inner workings of their organizational environments on its own. What they don’t understand is that AI can only become smart after being fed an abundance of accurate data about those environments.
Developing and implementing an effective AI strategy is just as critical as the AI technology itself. You cannot purchase an AI tool and assume it will work miracles for your company’s back-office operations. You need a plan of action for how you’re going to use the AI tool to improve the back-office operations. Which back-office operations will the AI conduct? How will the AI improve your back-office operations?
Business leaders don’t usually answer these questions before implementing AI into their operations. So, when the AI doesn’t live up to their expectations, they cannot understand why the AI’s execution failed.
Let’s dive into the main reasons why AI execution fails more than the technology itself:
1) Bad Data
Data is where AI’s actual intelligence comes from. In fact, AI is entirely dependent on past data to help interpret new information and make effective data-driven decisions.
Back-office operations are notorious for their human-caused data inaccuracies, such as duplicated customer records, data entry errors, formatting inconsistencies, and irrelevant historical information. Bad information fed into the AI system will result in bad AI-based interpretations and decision-making.
That is why all data fed into the AI system must undergo thorough reviews and updates to ensure its accuracy. That will go a long way in helping a company execute AI technology successfully for its back-office operations.
2) Inefficient Automation Processes
Automation efficiency comes from operational efficiency, just like automation inefficiency comes from operational inefficiency. Some organizations have a tendency to store data throughout multiple systems and CRMs, making it more difficult to execute AI-based automated tasks quickly and effectively.
For instance, let’s suppose your company stores siloed customer data. It will be more difficult for AI to retrieve customer data or perform a particular action for the customer, such as processing a refund for their order.
After all, the AI needs to verify the customer’s data to process the refund, but the information might be spread across multiple CRMs or a legacy ERP system. If that happens, it might take longer for the AI to process the refund, if at all.
Fixing this problem will require you to identify these kinds of data bottlenecks and inefficiencies in the organization. Once you can clean up the broken processes and standardize the procedure for retrieving data, you can boost your AI-driven back-office operations.
3) Lack of Training
Company leaders frequently treat AI technology like some expensive product or IT project. They figure they can hire a company to implement the AI technology for them and then let their own back-office operations team manage it from there. Doing this will almost certainly result in execution failure.
A company may have a back-office operations team or even an IT department, but that doesn’t mean either one will understand how to execute and manage the AI technology. AI is still a relatively new solution for back-office operations, so many existing professionals don’t understand how to use it.
All back-office professionals, such as HR administrators, data analysts, and CRM agents, must undergo extensive AI training from qualified experts before the implementation takes place. Leaders need to ensure all their operational users understand how to adopt and manage the AI technology effectively.
How to Execute an Effective AI Back-Office Operational Strategy
Business leaders should not try to create an AI back-office operational strategy on their own. Instead, they should continue to rely on a customer relationship management service firm to develop and execute their AI back-office operational strategies for them.
Such a firm understands that AI technology is only as good as the strategy guiding it. They help commercial clients integrate AI into their back-office operations strategically and holistically by evaluating existing CRM systems and data workflows to verify their accuracy and streamline their processes to support automation.
