Panorama of AI nowadays in business

It’s important to note that Artificial Intelligence (AI) by now has made its way, one way or another, into most aspects of business, and its presence hasn’t gone unnoticed.  

It has arrived and it is here to stay, and understanding what it has to offer, specifically its advantages and disadvantages, is fundamental in the search of staying competitive. 

This is why at Making Sense we decided it was important for each of our experts to dive deeper into what they were discovering around AI in their respective fields and share it in a detailed discussion of intersectoral pro’s and con’s of AI use. 

Here are our main takeaways of AI in business, before diving into the details of its effects in the Product Development Lifecycle:

Clearly, there is still a long way to go in having a determined roadmap on how to make all decisions related to AI: which AI tools to use, when, and in which projects. But, there is no doubt that, if used appropriately, AI can be a driver in helping get to market sooner, improve efficiency, reduce costs, build new products and improve overall business indicators.

That is why our key takeaway is:

“AI won’t replace professionals.
A professional using AI will.”

AI in the Product Development Lifecycle

At Making Sense we observe that: “AI is a tool but not a solution, Human critical thinking must accompany its findings.”

This is one of the main conclusions we reached after our multi departmental sessions dedicated to analyzing AI use in the Product Development Lifecycle (PDLC), and we found this is so because:

• AI tools can prevent Blank Page Panic by being able to kick off each step with something on the page and boosting efficiency, but human ideas still can’t be replaced and the most important decisions must be made by real people. 
• AI can Augment Your Thinking by surprisingly bringing about ideas you hadn’t even thought of, although this doesn’t mean that you mustn’t think for yourself. 
• AI can serve as a Rubber Duck by asking it for feedback in very initial stages of the PDLC before showing it to human colleagues for their comments, but the assessment and collaboration from coworkers still cannot be replaced. 
• AI tools can Efficiently Process Vast Amounts of Data, offering quick insights and recommendations, but because these tools are often still very robotic, relying on them can be problematic. The results provided may lack transparency and overlook qualitative aspects in their interpretations.
• AI can Significantly Enhance Productivity, automating complex tasks and accelerating project timelines, but these tools don’t always work perfectly. They can make mistakes, even inventing information, which could be missed by an untrained eye. 

To counter these obstacles, here are some useful tips:

• Success depends on the quality of the input data provided by humans which must be: Clean, Accurately Interpreted and Appropriately Prioritized.
• Pay special attention to legal aspects: Don’t use AI without consulting clients, protect their privacy and confidentiality regarding the input data.
• AI tools need to be used wisely: Feedback needs to be constantly reviewed and you cannot blindly trust the feedback it provides.
• Human expertise is essential: It’s crucial for the person using AI to be knowledgeable about what they are doing.

Key Insights of AI tools throughout the Product Development Lifecycle

Following our analysis of both the potential and limitations of AI in the Product Development Lifecycle, up next we wanted to share our core findings regarding the use of AI tools throughout the different steps of the PDC:

Project Managers:

They must bring their human side to project management, specifically for empathetic, perceptive and interpretative purposes. 

Product Analysts:

There is utility in AI for the product team throughout the entire development cycle (not just in implementation or definition). These tools can contribute to the decision making on the product side.

It’s not about taking what AI says as the ultimate truth, but rather it helps save time.

UX Designers:

AI can quickly analyze vast amounts of data and produce insights. It can generate prototypes and improve design effectiveness, updated to current best practices and trends. AI also has creative limitations, risk of bias and misinterpretation of insights; it raises issues around privacy and it can be expensive. 

UX Development: 

Such tools are very useful for rapid, basic prototyping and auto documentation. 

Alternatives in coding are provided and one can choose to accept them or not. Therefore, humans still have power because we still have the final say.

Developers:

Use AI to augment your decisions around system design.

Automated code-reviews don’t replace the real thing, but it’s a faster and quicker way to get feedback. Auto-complete can allow for easier workflow. To take advantage of the use of ‘Knowledge Retrieval’, it means we must document well first.

Quality Assurance: 

Although efficiency and time saving is enhanced, difficulties can arise. Regarding prompts, context is key and although AI can accurately predict repetitive codes, it can also make mistakes, so humans must always curate prompts.  

Ultimately: “AI can contribute a lot throughout the Product Development Lifecycle, but you can’t depend on it alone and therefore humans must accompany its use”.

Rely on Making Sense to use AI and increase value in the PDLC

Artificial Intelligence clearly is revolutionizing the way things are done in business, including the Product Development Lifecycle. Therefore, using these tools wisely is essential in the search of successfully adding value to precise production objectives, as well as overall business goals.

Just like we did in the following examples, count on us at Making Sense to assist you every step of the way alongside our one-of-a-kind- team of experts, who are not only specialized in what they do but also up to date with the latest AI trends.

Main takeaways from our experience using AI

• We explored natural language models that enhance coding efficiency, finding that they offer a promising future for developers to streamline their workflow and focus on creativity.
• Our testing of Generative AI suggests more advanced data normalization techniques will empower data-driven organizations in their data-driven endeavors.
• We experimented with an AI tool that generates user stories from requirements, and detected its potential to streamline the creation process.
• These tools are great boosters for productivity, but they need to be used consciously aware of their pitfalls.

As we mentioned at the start:

“AI won’t replace professionals. A professional using AI will”.

At Making Sense, the combination between the use of these tools and our specialized team is what sets us apart. Reach out to us at hello@makingsense.com and let’s work together with AI because –

Working Efficiently just Makes Sense.