design thinking in artificial intelligence

Role of Design Thinking in AI Applicaitons

Today, the world’s top and emerging organisations are focusing on implementing Artificial Intelligence alike. In their enthusiastic attempt to include AI into their processes and applications, basic factors like the need and feasibility of these applications take a back seat. To ensure successful AI  implementations, organisations must first figure out why, where, and how they can apply AI to specific business problems. Here, the role of design thinking in Artificial Intelligence comes into play. 

To lead innovations in Artificial Intelligence, the C-suite executives will have to apply design thinking to develop coordination among cross-functional teams. Design thinking principles and process inspires employees to ask questions and probe deeper into the problem statement and its possible solutions. It also diminishes hierarchy, creates an environment that challenges the status quo, and encourages smart risk-taking. With design thinking, organisations can accelerate their AI adoption process and reduce the resistance to change. When design thinking is deeply embedded in an organisation’s culture, it becomes easier to adapt to the changing market conditions.  

In this article, we will have a look at some of the challenges that organisations face while including artificial intelligence in their processes, and how design thinking can make the implementation easier and faster.  

The Challenges in AI Adoption  

The biggest challenge in adopting Artificial Intelligence is that there is no universally accepted approach that draws the process outline for AI adoption. Organisations are leveraging AI capabilities on the need basis and not as an organisational culture that drives innovation and processes. Hence, the full potential of any AI application is seldom leveraged. 

The applications are limited to functional operations rather than an enterprise-level adoption. There exist many gaps at the strategy-level to come up with a holistic approach towards AI adoption. Most organisations tend to solve minute and basic problems in data analytics and logistics with the help of AI. 

Also, even though there has been a lot of information available when it comes to AI and its applications helping businesses perform better, there is still a portion of employees across organisations who believe that AI is here to replace them. Hence, it becomes difficult to drive the change towards AI adoption with complete consensus. 

Now, let us see how design thinking could help us overcome these challenges and aid successful AI adoption at an enterprise level. 

Also Read: How is design thinking helping professionals to excel?

Design Thinking in Artificial Intelligence

Know the Design Thinking Process

The design thinking model revolves around the following steps:

  1. Empathise
  2. Define
  3. Ideate
  4. Prototype
  5. Test

Now, let’s see how these steps apply when organisations try to implement AI applications through the design thinking way.

Empathise

This step involves identifying the underlying problem that needs to be solved with the help of Artificial Intelligence. The focus here is on the end-user and their perspective on the solution. The strategic team needs to understand the issue at a deeper level and analyze the current situation. When dealing with neural networks or integrated AI systems, it becomes imperative to address various challenges. This might also involve diving deep into informatics and understand the challenges in analytics. 

It is important to introduce an element of innovation in the early stage of the problem identification. Organisations then need to plan how to integrate AI and innovation to add value to significant processes. 

Define

It is important to clearly state the problem statement in a single line, or maximum a paragraph. To do this, understand if there are challenges with particular areas or is it a holistic technology barrier. When the companies define their core AI problem, coming up with scalable solution become a smoother process. This happens because the teams have better clarity and can align themselves easily to take the right decisions when it comes to an AI launch. 

Ideate

In this stage, organisations formulate real AI solutions for the problem they are trying to tackle. When you have already identified the problem areas, it becomes easier to identify which AI algorithms, tools, and techniques to apply and at what stage. One can also figure out how the concepts of machine learning and deep learning fit into the solution, if at all. 

Also, check for scalability in this stage before actually starting to implement the solution. See what tools can help you achieve scalability before jumping into the development phase. 

Prototype

Creating and testing a full-blown model of the AI solution is a time-consuming process. Rather, creating a few prototypes and test them for improvements is an intelligent design thinking ideology. All prototypes are working models that demonstrate unique capabilities towards solving the identified AI problem.

Prototyping is the best way to save time and effort on any lapse or error found on this stage. If one prototype does not make the cut, we can move to the other and test it. This is how we can select the best prototype and scale it to a full-fledge AI model. The risk of launching the AI solution developed without going through the design process is much higher. By prototyping, organisations can reduce this risk multifold.

Deploy and test

Once the team selects the successful prototype, they can implement the final AI solution. The last step includes rigorous testing on the algorithm and the overall AI technology being implemented to make it more efficient and accurate. Whether the solution solves a functional problem or an enterprise-level issue, the overall approach will still be the same. Only the magnitude of the problem and efforts and the overall impact would differ. 

After the level one testing and bug fixing, revisit phase 1 is to rethink the problem from an empathetic perspective. This helps in making necessary AI fixes to make the deployed AI solution fool-proof. Repeat this step multiple times. 

A Favourable Environment For Design Thinking Approach

Design thinking is an organisational phenomenon and should not restrict to specific functions in a company. Hence it is important to build a culture within the organisation to accommodate and implement design thinking at all levels. 

A crucial aspect to successfully pull off the AI implementations backed by design thinking is to select the right teams. Having leaders in the teams will help in driving design thinking approach and delegate the right tasks to the right person in the team. Having AI experts is a must as they would be the ones who would play a major role in the overall execution and testing of the AI solution. To drive design thinking projects, organisations need to become leaner, aligning all required resources to work towards achieving a common goal. 

Design thinking involves questioning progress at each step to make the solution more efficient. Hence, good management and regular auditing are also important functions to successfully implement an AI solution led by design thinking. 

Also Read: Improve Customer Experience by Applying Design Thinking

Design Thinking is The Way Forward

To conclude, it is safe to say that implementing AI applications to business processes is not an easy task to perform. There are instances that certain organisations might not be able to achieve exactly what they thought of. The final AI solution might not meet the expectations and might fail to deliver accurate outcome. Hence, it is important for organisations to gauge their capabilities and follow a process that reduces the risk of failure. And what better than design thinking approach, that has been proven extremely effective in various applications across industries. 

It is time that organisations planning on leading AI innovations learn from successful design thinking applications. It is a valuable approach that makes the implementation process straightforward and gives a clear picture of a project’s progress, completion, efficiency, and accuracy. In the words of Craig Nelson, “Ultimately, the value of AI is not to be found in the AI operating models themselves, but in companies’ abilities to harness them.” And these abilities can develop by following the design thinking approach. 


Great Learning’s Design Thinking course is a well-structured program for those who are interested to pursue a career in design thinking. For more information on design thinking and how it is being implemented by organisations, follow the blog section of our website.

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