AI Quality Assurance Software
Hearing all about AI quality assurance software, but not sure where to start?
You’re in the right place.
When it comes to artificial intelligence (AI), there are many different options that companies can choose from. In the same way, there are plenty of standard quality assurance software options.
Today we’re giving you a 10-point checklist to help you evaluate the best solution for your team.
Not only will this save you time, but it will probably save you money (from not signing a long-term contract with the wrong company)!
10-Point Checklist
1. Define the problem you’re trying to solve
AI isn’t going to solve a problem if you don’t even know what your problem truly is.
We’ve seen leaders run in endless circles trying to find a solution to a problem they don’t even understand.
How do you know if you have a quality assurance problem?
Start by assessing the weak points of your current processes.
Where are customers dropping off? Where do problems occur?
When it comes to AI quality assurance software, it may not even be a QA problem you’re trying to solve. It may be a headcount issue or a new regulatory requirement that creates a need in your QA department.
2. What’s it worth?
How long will it take to see ROI from this new solution? Is the pain you’re solving worth the investment?
Or as we like to say, “Is the juice worth the squeeze?”
This is #2 because you need to understand the type of budget you’ll have before entering into any software negotiations. It will help you weed out solutions that you’re not yet ready for as well as gauge what capabilities are “must haves”.
There is a caveat to this.
Just because it’s outside your originally planned budget DOESN’T mean it’s not doable. But having a budget in mind helps you navigate those pesky sales calls.
3. True capabilities of the technology
It’s important for your AI quality assurance software to have the capabilities that you need right away. How fast can you implement this and start seeing results?
Product roadmaps are great and when investing in AI technology it’s important to choose a partner that will continue to advance their platform. However, you need to make sure that the current capabilities can solve today’s problems.
Our most significant piece of advice: get the right people in the room in order to ask the right questions during the sales process.
4. Identify all possible use cases
This is often a missed step as leaders don’t always bring in other teams when evaluating a new solution.
Let’s say you’re looking at getting 100% of customer service calls scored with AI quality assurance software, but your sales team could also use that type of technology. They just aren’t searching for “QA software”.
Or even reducing your team’s current tech stack if a new software can produce the outcome of two current solutions.
Keep your leadership team up to date with the new technology you’re evaluating.
5. Does it layer into your tech stack?
Adding AI quality assurance software doesn’t have to be a big undertaking. Yet, most leaders tend to overcomplicate it.
Compatibility and integration into your team’s existing systems is a must.
There is no ifs, ands, or buts.
6. Security & data protection
We’ve all seen the rage ChatGPT created and how fast it was adopted within organizations. But have you read their privacy agreement?
Most people would answer “no”.
Identifying relevant regulations and standards needed to ensure your company remains compliant is a no-brainer.
Ask about data privacy laws when on a demo with the company. What are they doing to protect your data?
7. Vendor support
Honestly, this should be top 3.
It’s most commonly overlooked as buyers get entranced by the technology. Which is 100% important when it comes to AI quality assurance software.
But having a vendor whose sales team is helping you build a use case to get the project approved is just as important.
Along with a CS team that implements and helps your team use the software on a daily basis. The last thing you want to do is spend money on something that isn’t used.
8. Determine your rollout plan
Learn to walk first. I know we talked about seeing an ROI and results, but you can’t expect to have your entire team seeing improvements from day one without knowing exactly how this technology will change your processes.
Start slower.
Implement it across one team or a certain number of people and learn what works/doesn’t work.
This will help implementation across the rest of the company go smoother and faster.
When it comes to AI software, there will be a learning curve for some teams. Keep that in mind and ensure your vendor helps you build the best possible rollout plan.
9. What happens if it doesn’t solve the intended problem
If you aren’t able to find an AI quality assurance software that solves exactly what you need, what happens next?
Is it time to change a process or procedure within your organization?
It’s important to have backup plans with anything you do. Or at least be able to adapt quickly as your needs change.
10. Rethink what’s possible
We saved our favorite for last!
With AI, you’re opening up realms you didn’t know that was possible. Even if your initial focus is QA, there are endless possibilities.
When demoing a new software, ask how their best customers use their product. Or ask about their most unique use cases.
Companies are more than happy to share innovative ways that their customers are using their technology.
Let’s wrap this up
When it comes to evaluating new technology to add to your tech stack, there is no one answer that fits all situations.
Determine which steps are missing in your current evaluation process from this article and add those in there.
Not every point will be needed.
AI quality assurance software can make an enormous difference when it comes to time savings and ensuring you’re meeting compliance standards. However, that doesn’t mean it’s easy to find the perfect solution and even more so, roll it out seamlessly across your company.
Need help with what tools to start evaluating?
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