Over the past few years, AI has evolved from a buzz word into a reality. Businesses that have machine learning systems aim to have sophisticated AI-based technologies. Companies that provide product design services that do not have machine learning culture are trying to incorporate an AI strategy into their business models. Given this hype, the fear of being left behind prevails in almost every company. So, how do you include an AI strategy in your business?
Let’s dig a little deeper into the challenges, possibilities, and opportunities that businesses face while they implement AI strategies into their product design services.
3Ts – Critical Challenges in Al Today
When it comes to creating an automated and AI-based ecosystem, there are lots of challenges companies have to face. These challenges can be summed up under the following major categories:
Team – One of the major concerns for organizations today is the need to have a group of talented individuals.
Time – Time is another critical factor. It is essential to monitor how quickly a business can get great results by implementing an AI strategy.
Trust – It refers to the trust in Machine learning models. It includes the ability to explain the results of the models to the stakeholders and regulators.
Below is the list of recommendations that can help you address these issues:
First, build a data culture
Businesses need to develop a proven data culture to tap into a massive amount of statistics and analyze it. The three aspects that guarantee a data-driven culture are:
Data collection – Start collecting data proactively. Use a variety of sources like customer analytics, product monitoring, sales departments, and marketing departments to collect data. Obtaining the right data builds a strong foundation.
Data accessibility – Make sure the data you collect is accessible to your team members. It should be in an easily understandable format so that your team can take meaningful insights from it.
Finding the right talent – have a team with a variety of technical abilities who can derive great results before passing the results to the experts. Make sure you train your team members to ensure great analytical results. Having AI-based technology is a cultural transformation, so instead of crafting a new team from scratch, try to use the existing staff members. Hire a few data scientists if needed.
Ask the right questions
To generate a great data culture, you have to ask the right questions. Some of the questions that businesses need to answer today are, how do I acquire the next client, who is the next client, and how do I secure the next business opportunity. More often than not, formulating the right solution to the right problem can be a great stepping stone for the implementation of AI. Companies need a team with analytical and creative mindsets to come up with the relevant questions and have viable solutions backed by data. Companies need to move forward strategically and rather than with a gut feeling.
What problem are you solving? Using data analytics, you should be able to understand the answer to this question. A variety of industries use AI and machine learning, but not all of them are successful in defining the right problem.
Determine outcomes – Translate high-end business goals into a business problem and determine the outcome. If you ask the right questions, you will have the right outcomes.
Measure success – Only the right metrics will help you measure success. No matter the definition of success for each product design services company, the result is the same – delivering value and making a profit.
Connect to the community
If you want to drive a change in the company, connect to the community. Use both online platforms and offline meetups to connect with the community. Participation in training sessions, webinars, and organizing meetups will enable you to learn from each other. No matter where you are in the world, there might be a local chapter of a machine learning community, so be on the lookout.
Technology considerations
Deciding what technology to use is critical when you want to impact your business profoundly.
Open source or closed source – When companies are making the AI shift, they need to decide whether they should strive for an open-source or closed source of technology or both. Open source technology is often a good starting point for most companies. They need a vendor to support when the new AI players mature.
On-premise or cloud – whether you want to opt for on-premise or cloud depends on how fast do you want to get the process started. It is good to get started on the cloud if you do not have any dev-ops systems in place already. On-premise can help you to optimize costs if you already have a DevOps infrastructure in place. A lot of other companies choose to opt for a hybrid model, which is a great practice.
Data- Again, whatever technologies considerations you choose, data is critical. Understanding how do you generate, save, and make the data sets available for your team is vital.
Trust in artificial intelligence
Machine learning and artificial intelligence should not be seen as ‘Black Boxes.’ You should always be able to identify logic and explain the rationale clearly. Being able to eliminate bias, have the sound documentation, and describe the model’s decision from the analytics are some of the critical considerations that need to be answered by companies to instill trust in AI.
What’s next
By incorporating the above five key points into your strategy, companies can get a perfect sense to begin their AI transformation. Thoroughly analyze the problems that you are trying to solve and see how AI and machine learning can leverage you.
Just like anything else, this transformation needs resources, patience, and time.