4 ways to start using AI in operations management right now (2024)

AI transformation with tech graphics

There is a strong desire to start using AI in many companies, but the “how” is not always clear. Everyone senses the enormous opportunity to operationalize AI for service delivery, but while some are charging ahead, others are still getting their ducks in a row.

A few months ago, I hosted a round table with Operations and Shared Services Leaders to ask whether they were AI Stormers or AI Strivers. On one side of the divide, 25% were AI Stormers, making significant strides in operationalizing AI. On the other, 75% were AI Strivers, eager to get started but yet to figure it out.

If you’re in the latter category, remember that the majority of service tasks revolve around the following three steps:

  1. Work out what the customer is asking you to do.
  2. Get the data that you need to do it.
  3. Complete the task.

Keep this in mind as you look to introduce AI in operations management. We’re going to get into more specifics later, but let’s start with planning your strategy.

Figure out an AI strategy

When you’re first getting started, you need to detail the practical order of events you want to happen and any potential consequences that may occur. Think about how you’re going to manage risks around AI. For example, what measures you will take to keep humans fully in control until the AI testing validation reaches an acceptable level - that should be at least 90% accuracy.

As you develop your strategy, the first rule of thumb should be to keep it simple. You don’t need the advice of a team of data scientists, but rather a methodical process for selecting the right off-the-shelf product.

The pool of true AI experts is very small (AI Olympians amongst a host of amateurs) - this means any attempt to build and train models in-house will likely fail miserably. You need to have vast amounts of data, you need to curate that data, and you need to have people with the skills to validate the data. If you don’t have these resources, you’ll end up with something very average, not to mention that subpar AI implementation could open you up to all sorts of security and privacy risks.

In 95% of cases, the answer is to use best-in-class external services and products that can get started immediately, with the support of experts who know what they’re doing.

Consider hiring a Chief AI Officer

You will need someone to coordinate all this change and that’s where a Chief AI Officer makes a good hire. This role is completely new, so rather than looking for someone with experience in that position, you’ll be recruiting someone who is one part entrepreneur and one part polymath - and preferably someome who is a bit legally savvy.

Machine learning wizardry isn’t vital, but they need to understand the technology and the 'art of the possible' to an extent where they can excite and coordinate the tech geeks and wider business appropriately.

This role can fulfil one of two purposes. If you’re looking for a person who will enable the business to use more AI, for instance introducing a customer service chatbot, then this will be an operational job that’s practically focused.

However, if your goal is to completely rip up the script and make your product or service AI-driven, then you’ll need a C-Suite hire to steer such a degree of innovation.

4 ways to start using AI in operations management right now

Below are four practical ways you can introduce AI into your operations right now:

1. Email classification

Manually reading and scanning emails is a huge time drain for large service centers dealing with thousands of daily email requests – but imagine an automated Hogwarts-esque 'sorting hat' for your inbox: that's email classification. It instantly categorizes incoming emails by urgency, who should handle them, and the nature of the request. With time-saving AI-enabled triage to analyze and sort your emails in the blink of an eye, staff are free to focus on higher-level work. Our testing shows a 30-hour reduction in time spent per 1000 emails. For mid-sized operations, this translates to saving the expense of two full-time employees each year, alleviating the strain on your resources.

2. Data extraction

Data entry for tasks like onboarding, invoices, and insurance policies is seemingly never-ending. Thankfully, AI is here to rescue you from the tedium. AI-enabled solutions automatically extract the information you need from emails and populate the necessary forms. These solutions use advanced language understanding to identify the key pieces of data within emails, even industry-specific terms that traditional models often miss. The result? Dramatically reduced manual work, fewer errors, and a super-smooth operation. Workflows that were previously clunky and laborious are transformed, slashing the time spent on painstakingly slow data entry, so you can spend your working hours on higher-value tasks.

3. Intelligent document processing

A constant stream of documents means a hefty amount of time spent categorizing and extracting the essential data. Ordinarily, this task takes a person around 2-5 minutes to perform manually. Intelligent Document Processing (IDP), however, can get it done in approximately 30 seconds per page. The AI solution scans the document, issues a confidence score on the certainty of the data, and if it’s below the threshold, passes it on to a human to verify and correct. For large-scale operations handling mountains of documents a day, this is a welcome advancement. IDP also means fewer errors are being made and the data is more reliable.

4. Sentiment analysis AI

How satisfied are your customers? Sentiment analysis AI removes the guesswork, employing Natural Language Understanding (NLU) to intelligently assess the emotional tone of your customers in emails and documents. Using traditional methods, this process requires complicated, expensive adaptations with around 70% accuracy. However, when sentiment analysis is integrated into existing workflows, that accuracy rate jumps to approximately 90%. Without the need for manual sentiment checks, the time spent on handling large email volumes is reduced. Customer service operations become far more efficient, allowing for prompt responses that register customer sentiment and create more positive interactions.

These AI superpowers are effective on their own, but the impact is amplified when you combine these tools across your entire service process. The sheer amount of time you'll save is astonishing. With all that extra capacity, your business can skyrocket its innovation, create unmatched customer experiences, and keep pace with competitors.

What does the future hold for service delivery?

Looking forward, there is going to be a radical change in the service delivery industry. Automation means there will be fewer jobs in the provision of service and there will be an expectation that service will happen much more quickly.

However, the answer to meeting this demand doesn’t lie in job losses, but in augmentation, and pivoting staff towards customer success. While AI takes care of the menial and repetitive work, the opportunity arises for the human workforce to deliver superior levels of customer service.

Take, for example, an insurance company, where claims typically take ages to be processed. With AI handling the manual tasks, employees can process claims faster and focus their energies on the human side of service delivery. We’re then looking at customer delight, instead of simply customer service.

Operationalize AI with Enate

Are you a stormer or a striver? If you’re ready to start leading the way in operationalizing AI, Enate makes it as simple as possible. Built on the Open AI Microsoft Azure network and integrated with our platform, EnateAI tackles those tedious jobs, freeing you to focus on what matters.