[ad_1]
When it comes to software automation, many teams are turning to AI as a potential answer.
Artificial intelligence in the form of machine learning or NLP can be a great solution to the problem. But did you know that the best way to start AI initiatives is to start without AI?
This may seem counter-intuitive, but there is a simple reason for this.
Because You may not be ready for AI as a solution. There may be some missing elements that prevent you from succeeding with AI if applied too early.
When it comes to AI, it’s not far-fetched to say that several critical stars must align to get results from initiatives in terms of usability.
Let’s take a look at the reasons why it might be wiser to hold back on AI than to start with it and hit the checkpoints, and later, tips on how you can overcome these obstacles.
Why it makes more sense to hold back on AI
1: You don’t yet understand the problem you’re solving
Typically, when you’re trying to solve an existing problem using artificial intelligence, the input and desired output are often well understood. You might be looking for AI to improve the accuracy or speed of an existing solution.
But in my experience, many of the problems that engineering teams and entrepreneurs are solving today are New problems. Problems are loosely defined and you may not fully understand what your expected output is, let alone what will be input into the system.
Take the problem of predicting sentiment. Do you know if you’re trying to predict broad overall sentiment (eg positive or negative) or more specific ones like 10% anger and 90% sadness for a given text? Are you looking for paragraphs of text or just single sentences or short snippets?
Yes, this is a design problem. And good design comes from a good understanding of the problem. Without it, you will struggle with many design dilemmas. Such design issues, combined with the complexity of developing AI systems, may require constant updating of models to accommodate design changes, introducing confusion and reducing the chances of success with AI.
2: You may not have the required data
As I discuss many times in my book, AI systems require data. It’s not just data to train models, but data to better understand the problem you’re solving and the expected output from the system.
Often, when you have a new problem, this data doesn’t exist. Even for older problems that are solved manually, the data may not exist or be available in an inaccessible format. This is exactly what happened with the healthcare client. They had been doing the billing annotation task for over eight years, but when it came time to automate the process, the data just wasn’t there.
Without the right kind of data, you won’t really know what problem you’re solving, let alone train the model.
3: Your customers may be skeptical of automation
Let’s take a look. People are skeptical of artificial intelligence, especially those who don’t know what it is and the current state of its capabilities. The moment you talk about automating employee workflows, some people will get uncomfortable and worried and start to think it’s going to be an “AI race”.
People are also used to a preferred way of doing things. They worry about how the integration of AI will affect their current “efficient” workflow. Some think this new artificial intelligence is just a gimmick. This is exactly the problem I ran into with the healthcare client I mentioned earlier. While the CEO was very enthusiastic about integrating AI into one particular workflow in their business, the employees were not as enthusiastic and made it clear.
The problem with resisting the use of artificial intelligence is that people may not perceive the solution as long-term. Additionally, subject matter experts who are skeptical of the idea of automation may be reluctant to help co-develop a working AI solution, as was the case with my healthcare client. Additional education, training, and buy-in were needed to understand why automation would make their lives easier. Without user buy-in, no matter how impressive an AI solution is, its existence will be short-lived.
So what’s to give?
Forcing AI solutions on humans won’t work… in the long run.
Starting an AI initiative without data will ensure you hit a dead end.
An ill-defined problem requires redesigning your AI tool, and that can be expensive.
What can you do?
3 Tips for Managing AI Reluctance
Even if you’re not ready for AI today, here are three things you can do to eventually see significant benefits from AI for the problems you’ve been struggling with.
1: Start with a manual or semi-automatic approach
If your customers are embracing AI, but you don’t have the data to support the initiative, or you don’t fully understand the problem you’re solving, consider a mechanical approach.
This means that you put together a small team (eg, virtual assistants for non-domain specific problems) and have them perform tasks manually, while also saving data from manual execution.
Alternatively, if the workload is too high, you may consider automating the task with less ideal software automation to bring some level of control to the task.
For example, if your virtual assistant is expected to analyze thousands of images to spot a stop sign. But you know that images with a specific color distribution will not have a stop sign with 99% certainty. You can develop a simple software script to remove such images from consideration and reduce the workload of your virtual assistants. There are many such opportunities to integrate simple software automation before implementing artificial intelligence.
Why does this work?
- You can easily change the design of your solution until you are comfortable.
- You can continue to change the type of data collected
- You can generate high-quality data for machine learning
- You can establish baseline metrics that you can later compare with an AI-powered solution
2: Collect data effectively
If your only problem is a lack of data to develop your AI solution, there are ways to do it efficiently without having to fully develop a full data strategy.
I won’t go into that in depth in this article, as you can read my article where I talk about data generation strategies for your machine learning projects.
3: Get over the fear of adoption
If your problem is well understood and you have the data you need, but users want nothing to do with an automated solution, there’s a lot of work to be done on the cultural side of things.
You need to think about how to get buy-in from customers who could be your employees, customers or even vendors.
A way to approach this is to first ask them what they think about automating specific tasks. If you feel resistance, you want to understand their worries and fears. This will give you an idea of what your focus will be when trying to “sell” them on the solution.
If there is concern Fear of losing your jobYou can show employees how the nature of their work will change or be simplified by integrating artificial intelligence.
If the fear is a potential rigidity in the work processes, you can educate the users about how you not only develop the solution in the dark, but with them (the users) to make sure that they are happy with it, and it is really happy. Solving the pain point.
Why does this work?
- By actively removing fears and seeking feedback, you foster collaboration.
- The more collaboration between potential AI users, business stakeholders, designers and developers, the better the solution and the higher the chances of AI adoption.
The whole word
As you’ve seen in this article, even though your business problem may be a good candidate for AI, you may not be ready to start with AI.
Chances are you don’t have the information for the initiative, you may not have a good understanding of the problem, and your customers may not be ready for an automated solution. The solution to this is not to start with AI, but to start without it. Address key issues using simple yet effective approaches.
That’s all for now.
To continue learning from me:
- Join my AI Integrated Newsletter, which demystifies artificial intelligence and teaches you how to successfully integrate AI to drive profitability and growth in your business.
- Read the business case for AI Learn the applications, strategies, and best practices to be successful with AI (get 3 free chapters here).
- Send me a message For a more customized roadmap and AI strategy for your business
Recommended articles
connected
[ad_2]
Source link
