Data-Driven Decision-Making: How AI Engineers Contribute

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AI is a gamechanger for businesses, allowing them to make great commercial decisions. The more data-driven decisions it makes, the more it learns and the more accurate its predictions turn out, allowing businesses to make great commercial decisions.

This is starkly different from how businesses have operated over the past century or even 20 years ago. Until then, every important decision was made at a central point: a human.

The issue for modern companies today is that they’re drowning in data. Processes like spreadsheets and databases just don’t cut it anymore and humans cannot keep up.

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From analyzing data and employing machine learning to train AI to develop strategies based on the collected data, AI engineers play a prominent role in enabling enterprises to make data-driven decisions.

AI Engineers can quickly analyze large datasets

Large datasets allow organizations to make data-driven decisions instantly. What content does their target audience prefer? What needs to be changed in a failing marketing campaign? Such an analysis can be incredibly time-consuming for humans, but for an AI engineer, it’s instant. 

AI Engineers can help attract, engage, and build loyalty

As businesses cater to activated and empowered customers, understanding differences in their attitudes, behaviors, and needs is of utmost importance. AI engineers are best poised to carry out intelligent segmentation and deliver a unique customer experience.

AI Engineers can boost sales and marketing campaigns

Neural networks have been proven to be by far the most precise way of countering many problems like translation, speech recognition, and image classification. Armed with natural language processing skills, AI engineers play a pivotal role in helping companies understand how their customers are interacting with their brand, and what changes need to be made to make them more appealing.

AI Engineers can help businesses understand their customers better

Using machine learning and natural-language understanding, AI engineers analyze the written or spoken word, categorizing, organizing, and analyzing customer data in real-time, to ultimately understand customer sentiment.

AI Engineers can predict future demand and supply 

Armed with a great knowledge of statistics, signal processing techniques, and neural-language processing, AI engineers help ensure the optimized quantities of inventory are maintained, leading to increased service, increased profitability, and increased capacity. 

AI Engineers can help optimize product pricing

AI engineers use AI-driven price optimization to increase conversions and margin and not only save the organization time but also money. 

AI Engineers can drive the implementation of AI within the organization

AI engineers can add value beyond the development and training of machine learning models to the implementation of these models within the organization. AI engineering brings together disciplines from across the organization to develop a unified path to attaining value when operationalizing a combination of multiple AI techniques.

Skills to Hone to Become an Expert AI Engineer

Enterprises looking to leverage AI in a big way must start with an audit of the skills required. Let’s take a quick look at essential technical and non-technical skill sets one needs to stand out as an expert AI engineer.

Applied Math and Algorithms 

This solid skill set will enable your understanding of subjects like Gradient Descent, Convex Optimization, Lagrange, Quadratic Programming, Partial Differential equation, and Summations. 

Neural Network Architectures 

Machine Learning enables simplifying and coding complex tasks, which are beyond human capabilities. One of the most precise and proven ways to solve issues is through neural networks such as image classification, speech recognition, and translation.

Programming Skills

Irrespective of specialization, AI engineers will benefit greatly from a basic understanding of computer science and programming languages like Python, R, Java, and C++. Not only are these easy to learn, but their applications also provide more scope than most other languages. 

Knowledge of Statistics

A good grasp of concepts including Matrices, Vectors, Matrix Multiplication, and knowledge in Derivatives and Integrals is essential. Proficiency in statistical concepts like Mean, Standard Deviations, and Gaussian Distributions along with probability theory for algorithms like Naive Bayes, Gaussian Mixture Models, and Hidden Markov Models are key to thrive as an AI Engineer 

Signal Processing Techniques 

AI engineers must have a good grasp of Signal Processing and possess the ability to solve several problems using Signal Processing techniques, a skillset crucial for feature extraction, an important aspect of Machine Learning. 

Non-Technical skills 

Ultimately, it’s not just the technical know-how, but softer aspects that make the difference in enabling sound data-driven decisions in organizations. A high degree of collaboration and getting together of different mindsets is essential to enabling AI to understand neural networks and the various behaviors of the human brain. 

An AI engineer needs to have excellent problem-solving skills and cultivate systematic, analytical thinking to come up with innovative solutions to problems.

While communication is key to collaborating effectively across the organization, rapid prototyping is critical to building on ideas within the minimum time requested. Success will depend upon the AI engineer’s ability to quickly test and modify models as required.

Build a Talent Foundation Before Launching AI 

What we see at KnowledgeHut (an upGrad company) as a digital and skills transformation partner to several Fortune 500 companies across the world, is that the tech skills gap is growing in large part due to talent strategies that are not sufficiently thought through.

As per a Teradata report, an estimated 80% of enterprises are investing in AI. However, companies at every level of AI sophistication are faced with a rapidly growing skills gap. 

To address this issue, a common approach that many companies are taking is to first hire Ph.D.-level data scientists. But this has led to a further set of talent issues emerging around AI product development and engineering.

Most firms are yet to fill essential roles such as AI engineers and as a result, we often see poorly managed AI development with costly, unproductive cycles. As we advance further into an AI-fuelled future, organizations that lay a greater emphasis on building a strong foundation of data-literacy skills across the organization now could be rewarded manyfold.

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Subramanyam Reddy
Subramanyam Reddyhttp://knowledgehut.com
Subramanyam Reddy, Founder and CEO – KnowledgeHut

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