Why Your AI Solutions Require Business Expertise More Than Data Science?

For artificial intelligence (AI) in business, data scientists and domain experts stand as crucial pillars, each offering unique strengths in tackling business problems. However, as AI continues its ascent, the talent shortage poses a significant hurdle for businesses aiming to harness its potential. When confronted with a specific business challenge, the question arises: who holds the key to unlocking the solution? Is it the data scientist, armed with strong technical skills, or the domain expert, steeped in industry knowledge? Businesses are unsure how to navigate AI’s complexities and integrate them into their business solutions. So one way that always seems the fittest is to outsource problems to data scientists or third-party vendors. But is this the right way to address it?

Begin by asking the right question, “Are we solving a tech problem or a business problem?”  The other way out is to empower domain experts with the tools to navigate AI complexities and make informed decisions.

Let’s delve deeper into the roles of both data scientists and domain experts, examining who is best suited to tackle the challenges of AI-driven business solutions.

The Power of Data Science Expertise

Data scientists bring a unique skill set to the table. Here’s a closer look at their strengths:

• Expertise in Machine Learning: Data scientists possess a comprehensive understanding of machine learning, enabling them to leverage this technology effectively for data analysis and modeling.
• Mathematical, Statistical, and Programming Skills: Their proficiency in these areas equips them with the essential tools to work with data and create predictive models.
• Crucial for Data-Driven Decision-Making: Data scientists are essential for extracting valuable insights from data and driving data-driven decision-making. They possess the skills to clean and preprocess data, build predictive models, and select appropriate algorithms. Additionally, their problem-solving skills allow them to identify complex business problems and develop innovative solutions using AI.

However, data scientists also have limitations that can hinder AI projects:

• Limited Perspective of Specific Industry Domains: Data scientists may not always fully understand the particular industry or business they are working in. Domain expertise can help them interpret data in the business context, which is essential for making informed decisions and solving unique business problems.
• Unfamiliar with Pain Points and Factors: Data scientists may not be aware of the most critical pain points and factors affecting a particular business problem. 
• Inability to Quantify Model Performance: Data scientists may create models, but without a deep understanding of the business objectives and metrics that matter most, they may struggle to assess whether the model’s performance aligns with the company’s goals. This emphasizes the importance of collaboration between data scientists and domain experts to bridge this gap.
• The dearth of Data Scientists: The high demand for data scientists outstrips the current supply, creating a talent gap.

The Value of Domain Expertise

Domain experts, often called subject matter experts (SMEs), bring a wealth of industry knowledge to the table. Here’s how their expertise benefits AI projects:

• Deep Understanding of Business Problems: Domain experts have a deep understanding of the specific business problems and challenges within their industry. They can articulate these problems clearly and comprehensively, ensuring AI solutions address the right issues.
• Understanding Data Affecting the Problem: They are well-versed in the various factors and variables that can impact the business problem. Business experts with years of experience in a specific domain are often better equipped to identify critical elements that need attention when designing AI solutions.
• Understanding Industry Practices and Regulations: Domain experts have extensive knowledge of the industry’s practices, regulations, and processes. This expertise allows them to provide context and guidance for solving problems that align with industry standards and best practices.

However, domain experts also face limitations when it comes to AI:

• Lack of Knowledge of AI: Domain experts typically do not have a deep technical understanding of artificial intelligence (AI) and its underlying concepts. This can make it challenging for them to grasp the technical intricacies of AI-based solutions fully.

• Limited AI application Skills: They may lack technical skills such as mathematics, programming, and statistics, which are essential for implementing AI solutions. This can hinder their ability to contribute to the technical aspects of AI projects actively.

Confusion Around AI:

A common misconception persists: some think data scientists, with their expertise in machine learning, programming, and statistics, are the sole ingredient for successful AI initiatives. This raises the question: if mastering these technical aspects is all it takes, then why do so many AI projects fail?

Incorporating Human Insights into AI: A Case Study from New Zealand (Michael Agostini, Matlab Expo keynote 2019) offers a compelling illustration. A consortium of universities aimed to use AI to predict the quality of future powder milk production in near real-time. Armed with six years of data from three plants, they embarked on this endeavor. But their journey was anything but smooth. 

The initial results? Disappointing.

Why?

Because simply feeding volumes of data into an algorithm isn’t the magic bullet. The team began their quest to unravel the mystery behind their AI’s underperformance.

They started asking critical questions.

What emerged was a revelation: each plant had its peculiar behavioral habit. The data moved over time every year, behaving like a distinct entity. Why? The answer lay in the intricate chemical processes orchestrated by living organisms converting raw milk.

Their strategy shifted to building models for individual plants, one year at a time. Yet, even with this focused approach, expert opinions held that the models were still unsatisfactory. 

Here’s where the plot thickens. 

With an application-level understanding of data science, the engineers zeroed in on a crucial issue: data bias. Most data points were assembled in the middle of the distribution, skewing the model’s perspective. Undeterred, they embarked on a mission to synthesize new data, leveraging the information. The result? A resounding success. The models now sang in harmony with the expectations.

This case study is an excellent example of how domain experts, armed with the right tools and a deep understanding of their problem, can orchestrate AI triumphs. It’s a stark reminder that even the latest technology, seasoned programmers, and data wizards can only guarantee success with a critical ingredient: domain knowledge.

In the world of AI, it’s not just about the algorithms; it’s about the human touch.

Equipping domain experts – The advent of no-code AI platforms: 

The rise of no-code AI platforms, like emly labs has established an era where domain experts with an application level of AI understanding can leverage AI to solve complex business problems without requiring extensive technical expertise. Ensuring that AI solutions are closely aligned with real-world challenges and objectives.

No-code AI platforms empower domain experts to handle many maintenance tasks, such as data monitoring, model retraining, and interpreting results. This can significantly reduce reliance on data scientists for ongoing maintenance, leading to cost savings.

Let’s embrace the future where domain experts take the wheel, where experimentation knows no bounds, and where knowledge is for everyone. No-code AI platforms are your ticket to an exciting innovation and transformation journey.

Key Takeaway:

 Domain experts possess an unparalleled understanding of the real-world problems that AI aims to solve. They hold the key to unlocking the critical context and insights necessary for AI solutions to function and excel in practicality and relevance.

No-code AI platforms liberate domain experts from the daunting technical intricacies that often deter them from engaging fully in AI projects. By automating many of the technical aspects traditionally handled by data scientists, no-code AI liberates domain experts to do what they can do best – focus on AI’s application and problem-solving elements.

The reality is that collaboration between data scientists and domain experts is invaluable, but it’s only sometimes feasible. Resource constraints, time limitations, or other factors can hinder this partnership. No-code AI transcends these limitations by equipping domain experts with the tools they need to apply AI without demanding a deep dive into technical complexities.

Domain experts armed with the right tools, who know how to visualize and comprehend their problems quickly, can become the driving force behind superior AI solutions. 

So, let’s embrace this shift. Let’s empower our domain experts to take the lead, knowing they can unlock AI’s true potential. No-code AI isn’t just a tool; it’s a revolution that bridges the gap between business expertise and technology, making the extraordinary in AI achievable by every domain expert.