• October 3, 2023

What the first 12 months of AI:CS deployment looks like

What the first 12 months of AI:CS deployment looks like

What the first 12 months of AI:CS deployment looks like 1024 536 Avocado55

Picture the scene…

You’re a large contact centre and, a few months ago, you decided to roll out AI tools throughout your business. You bought the tools, you fed in your raw data, you cut the size of your human team in half in preparation for the coming efficiencies and you implemented your new AI tools across all areas of operation quickly so you could start saving money and seeing the benefits as soon as possible.

If you look around you now, what do you see?

In all likelihood, you’ll see something like:

  • A very stretched workforce with low morale and a high turnover rate
  • Customers that are highly dissatisfied with their contact experiences, with declining ratings and reviews that back this up
  • Siloed departments and teams within the business that aren’t communicating effectively with each other, leading to delays and errors
  • Performance is significantly down vs before your AI:CS rollout.

Not exactly how you imagined it would go! Let’s look at the probable reasons why the implementation hasn’t had the impact you thought you’d signed up for.

  • You didn’t fully assess your existing processes and needs before implementing AI tools
  • You used too many AI tools, across too many areas of the business at once, and rolled them out too quickly
  • The data you fed into the tools was a rush job, so it hadn’t been cleaned up properly and was inconsistent
  • You reduced staff numbers before fully testing how the AI tools would impact your operations, leaving your remaining teams stretched when predicted efficiencies were not quickly realised
  • In trying to roll things out quickly, the retained staff didn’t receive the depth of training required to make the most of the AI tools they had been given to use
  • Customers were getting strange and unhelpful results from their interactions through AI because the data used by the tools was flawed, so performance fell well short or targets.

What a good AI:CS implementation should look like

We’ve looked at what can happen when AI tools are implemented poorly in a contact centre setting, but what does ‘good’ look like?

Proper planning for AI:CS rollout is key

Time spent thoroughly assessing your needs is never wasted. Having clear requirements for what AI tools will need to do in order to meet your business objectives is an essential foundation.

As well as looking at the practical outcomes you need to achieve with AI:CS, you’ll also need to decide how it will be managed within your operation; or, if you’ll be outsourcing, how that aligns with everything else you do operationally.

Choose your AI tools carefully

With so many AI tools entering the market in recent years, it can be tempting just to choose the ones most widely used by others, but they won’t necessarily be the right fit for your purposes. A proper assessment is important to not only look at the capabilities and cost implications, but also the scalability potential, how straightforward the integration will be and how each tool is supported. The last thing you need is to buy an AI tool where their customer support only stretches as far as a superficial onboarding exercise.

Optimise and cleanse your existing processes and data

Reviewing your key processes and re-engineering rapidly is a good way to help ensure that the ways your customers and your own agents interact with customer service tools will transition well to your chosen AI solution.

Identifying any issues with your data and making sure these are resolved before your AI tools enter the scene is something that should be prioritised.

Collaborate across the business

To see AI:CS reach its full potential, different departments and teams within your business need to communicate effectively and cooperate with each other. Keeping everyone in the loop and feeling involved, listened to and understood will make a big difference to the success of your deployment in the short, medium and long-term.

Starting small and testing AI tools on non-critical functions first, while keeping these internal lines of communication open, will help you to spot issues in the early stages before they become a bigger problem, and provide important learnings before wider rollout occurs.

Education and training should be a priority

Many of the existing skills of your customer service team can transfer to incorporate working with asynchronous communication, but it doesn’t happen on its own. Equipping your team properly and giving them adequate time to get to grips with new processes is essential before you implement AI:CS widely across the business.

Getting your AI:CS rollout right by preparing properly first is far preferable to a rushed implementation that ultimately fails to achieve your objectives and ends up costing you far more in the long run.

For a much more detailed framework for deploying AI:CS for maximum benefit, read our guide to Artificial Intelligence in Customer Service AI:CS).

If you want specialist assistance with an AI:CS deployment, our expert team can help. Get in touch today to find out more.

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