Gone are the days when AI was merely a buzzword; in present times, AI has automated many routine data tasks that were earlier performed by human professionals. With rapid automation and the integration of AI in various tools, one might wonder if this signals the downsizing of data teams.
Glen Rabie, CEO of Yellowfin, is of the opinion that even though many analysts may be concerned about automation and AI replacing them, the role of the data analyst will become increasingly important to businesses, increasing their demand. How? Since AI and ML have become such an inherent part of so many business processes, a lot rides on their outcomes; therefore, these AI and ML tools or models need data professionals to ensure the accuracy of the training datasets and to verify the outcome of the AI models.
Let’s look at AI and its limitations before discussing how it collectively contributes to the increased demand for data experts.
AI and its limitations
Many areas of data science are benefiting from AI advancements. It has automated traditional tools for data analysis and visualization that used to solely rely on human effort. For instance, smart data preparation using machine learning has drastically reduced the amount of time analysts used to spend on preparing raw data. Eventually, streamlining data profiling, data matching, and data cleaning for businesses.
Keeping AI’s advancements to one side, it is essential that we also acknowledge the inherent limitations of AI, which still necessitate the involvement of human data professionals. As a result, adopting an approach that values the combined intervention of humans and automated tools may be more favorable.
Here are some of AI’s major limitations:
❖ Limited understanding
Machines excel at automation but fall short when it comes to understanding context. They may produce correct results based on patterns in the data but may fail to comprehend the underlying meanings.
An example of something that is beyond the capability of AI is analyzing a declining trend in product sales while simultaneously taking into account the anecdotal increase in marketing efforts by a competitor.
❖ Lack of common sense
Instead of having inherent comprehension, AI systems rely on statistical patterns in the massive amounts of data they process. This makes AI good at certain tasks but prevents it from showing common sense or generalization. While AI excels at processing large datasets, it often fails to grasp the bigger picture, complexity, and significance of nuanced details. This skill is still a hallmark of human intelligence.
❖ Moral void
Due to its reliance on coding and data, AI is incapable of making morally sound choices. It is devoid of any sense of “right” or “wrong”, empathy, or moral judgment. AI makes judgments based on the facts and patterns it has been taught to recognize. This leads to unintended consequences and ethical dilemmas. Ethical decision-making inherently requires a moral framework and AI currently lacks empathy and a critical understanding of human values.
❖ Bias in decision-making
Another significant limitation of AI is the potential for bias in their decision-making processes. AI models learn from historical data, and if this data contains biases, the AI system can inadvertently perpetuate and amplify those biases. These biases can manifest in various forms, including racial, gender, or socioeconomic bias. Consequently, AI-driven decisions may lead to unfair or discriminatory outcomes. This can impact individuals and communities adversely. Addressing bias in AI is crucial to ensure equitable and ethical use of these technologies and to build trust in their applications.
AI and Automation’s Impact on the Workforce: Driving the Demand for Data Professionals
While AI and automation have had a significant impact on several industries by increasing productivity and decreasing the need for manual labor, they are not a silver bullet for fixing all corporate ills. Humans, with their ability to discern and interpret contextual nuances, will remain indispensable.
Keeping humans in the loop is beneficial as they can help with the following:
❖ Monitoring and development
The combination of AI and automation technologies rapidly accumulates and processes vast amounts of data, primarily for training AI and ML systems. This brings us to the first need for data professionals, which is to monitor and manage the data used to train the AI and ML systems. The role of data professionals is essential in the process of gathering, preprocessing, and arranging data into meaningful, organized datasets. These structured datasets are crucial for enabling accurate model training and improving the efficiency of AI and automation tools.
❖ Safeguarding automation tools
Automation tools that are integral to AI and machine learning, are not immune to vulnerabilities such as security breaches and data integrity issues. These challenges underscore the necessity of human intervention. To mitigate these vulnerabilities and ensure the robustness of automation tools, data professionals are involved to actively monitor the tools’ performance. By actively engaging in quality control and security measures, human professionals maintain the correctness and integrity of the data and prevent the tools from being exploited by malicious actors. This human oversight is essential to ensure data accuracy and protect the integrity of automated processes.
❖ Algorithm development
Data professionals and data scientists play a crucial role in crafting algorithms that are specifically designed to extract patterns and correlations from data. These algorithms enable AI systems to create predictive models, offering valuable insights based on the data they’ve learned. For the algorithms to perform well, they engage in activities including feature engineering, model selection, and hyperparameter tuning. Automation is improved when data scientists work with subject-matter experts to design, train, and test these algorithms to provide accurate and valuable insights.
Data professionals serve a critical role in not only interpreting AI outputs but also in ensuring the accuracy of desired outcomes. While AI models, especially complex ones like deep learning models, may appear as black boxes with intricate decision-making processes, human professionals play a crucial role in verifying if the AI models are providing the intended results. They conduct quality checks to confirm that these models’ performance aligns with the desired outcomes. In cases where AI models deviate from the expected results, data professionals can identify the discrepancies and adjust the training labels, thereby making AI decisions more transparent and aligned with the objectives. This collaborative effort ensures that AI models consistently deliver the desired insights and outcomes.
❖ Tackling unpredicted situations
AI systems may struggle with handling edge cases or unforeseen situations. AI is often trained on historical data and patterns. These systems are designed to perform well in standard conditions but may encounter difficulties when faced with unusual or unprecedented situations. Data professionals not only test and validate AI systems but also fine-tune their performance based on real-world challenges. If an AI model encounters difficulties in specific scenarios, data professionals work to adjust and improve its training datasets, ensuring that the system can adapt to and handle unforeseen situations effectively.
❖ Confidentiality and data security
AI models require ongoing monitoring by data ethicists and compliance officers to make sure that these systems are trained on ethically sourced data that complies with regulatory standards. By ensuring the use of compliant data in the training process, these professionals contribute to the ethical behavior of AI models and ensure that the outcomes align with regulatory requirements and guidelines.
Promising Career Opportunities and Skill Sets
Now that we have established the fact that AI is not a standalone technology but a tool that augments human capabilities. Let’s take a look at the careers and skills that will be in high demand in the AI-driven future.
Skills in high demand
- Machine learning and deep learning proficiency are paramount. These skills enable professionals to design and deploy AI models for tasks like image recognition, natural language processing [NLP], and predictive analytics.
- Data science and analytics are needed to collect, clean, and analyze data to train AI models effectively.
- Programming languages like Python and R have emerged as industry standards for building AI. Engineers and scientists working with AI will find these language skills indispensable.
- Cloud computing with platforms like AWS, Azure, and Google Cloud is essential for AI engineers. These platforms provide the infrastructure for AI applications.
Roles in high demand
- Data scientists and analysts for collecting, cleaning, and interpreting data for AI-driven insights
- Machine learning engineers for designing and deploying machine learning models for various applications
- AI ethicists and compliance specialists for navigating the ethical and regulatory landscape of AI
- AI and automation engineers for building and maintaining AI and automation systems
- Robotics engineers for designing and maintaining robotic systems for manufacturing, healthcare, and logistics
Growth Potential in the Outsourcing Sector
The relationship between AI, automation, and proficient data professionals is pushing businesses toward success and growth. Service outsourcing companies have equally witnessed this transformative wave. Integrating AI/ML models is redefining how outsourcing companies conduct their operations, leading to a growing demand for data professionals within the sector. This transformation has prompted businesses to increasingly outsource their tasks to service providers, be it to avail of their data labeling services, data enrichment services, or any other data management service. This further validates that more and more data professionals will be required to monitor and carry out such tasks.
AI + human data experts = A powerful synergy
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The effect of AI will only continue to grow, which will result in the automation of a significant number of tasks that were previously performed by human professionals. However, when it comes to understanding data and using AI technologies, the particular abilities that data professionals possess, such as critical thinking, strategic planning, emotional intelligence, and ethical judgment, remain irreplaceable. No matter how advanced AI models become, they will never be able to replace the fundamental capabilities of humans.