The Impact of AI on Digital Analytics: Opportunities and Challenges -

Artificial intelligence (AI) has been revolutionizing businesses. Studies show that around 77% of businesses are using or are exploring the use of AI.

And one of the most powerful ways AI can used is in digital analytics. With its ability to analyze, predict, and automate, companies that master AI will have large advantages over competitors.

But AI use is not without its pitfalls. In this article, we’ll outline the top 5 opportunities and top 5 challenges AI presents in the realm of digital analytics so businesses can make more informed decisions.

Opportunities in AI-Driven Digital Analytics

Automation of Repetitive Tasks

AI can automate many tedious and time- and labor-intensive tasks (such as data collection).

By using AI for these more menial tasks, companies can save on resources. Their data analysts, for example, can now have more time and energy for more strategic tasks, such as evaluating the insights generated by AI.

A common example of AI being used for automation is how AI chatbots are now the first line of contact in customer service.

Enhanced Data Processing and Interpretation

AI has the power to analyze vast amounts of data at an unprecedented scale, at far faster rates than manual human analysis or even traditional software.

This is because AI’s machine learning (ML) algorithms specialize in rapidly identifying patterns, correlations, and anomalies.

This has the potential to generate market insights that are both of more quantity and better quality.

Predictive and Prescriptive Analytics

But AI’s capabilities are not—and should not, if the goal is to maximize its abilities—be limited to descriptive analytics (i.e., “what happened?”)

It can also be used for predictive analytics (i.e., “what will happen?”) and, consequently, prescriptive analytics (i.,e. “what should be done?”)

After all, when people make predictions or prescriptions, they may be hindered by bias or misinterpretation. AI, on the other hand, only looks at the cold, hard data.

Improved Customer Insights and Personalization

And one of the ways its predictive power can be harnessed is by generating customer insights. AI can be used to analyze people’s browsing patterns, purchase history, and social media activity. With this, it can recommend businesses the best course of action to engage customers.

For example, it can recommend specialized marketing campaigns to different customer base segments or predict customers’ needs based on past online activity and then offer personalized product or service recommendations. This can be particularly useful for e-commerce platforms.

Real-Time Decision Making

Perhaps just as amazing as its raw analytical and predictive power is its ability to do so in real-time. With this, businesses will be better equipped to act proactively, responding to emerging trends and anomalies instantly rather than waiting for manual analysis.

After all, it is often the key to business success to be the first among its competitors to move and adopt or respond to trends.

For example, in banking and finance, AI-driven fraud detection can identify suspicious transactions as they occur, minimizing losses.

Challenges in AI-Driven Digital Analytics

Data Privacy and Security

AI relies on data, so data privacy and security naturally become major concerns.

The public is now more forward-thinking about cybersecurity and privacy. They are more wary about companies using their personal data for their benefit. We can see this, for example, in the increase in VPN usage rates. Read more about how this tool can help you enhance your digital privacy.

Companies whose customers are unhappy about how their data is being used risk reputational damage—and, thus, a loss in sales.

Additionally, companies who use AI must be extremely cautious about unauthorized access and data breaches. These can result in similar repercussions as well.

Ethical and Regulatory Challenges

In the same light, government regulating bodies now also enforce strict laws about digital data, such as the California Consumer Privacy Act (CCPA) or the European Union’s General Data Protection Regulation (GDPR).

Not only do they have severe consequences for violating these regulations, but these laws may continue to change over time, especially given how AI is also still a new and rapidly evolving technology.

Other ethical challenges exist as well. For example, if decision-making is left all up to, which people should be held accountable for mistakes? Or be credited for successes?

Dependence on High-Quality Data

AI models are only as good as the data they are trained on. Inaccurate, incomplete, or biased data can lead to misleading insights and flawed decision-making.

As a starting point, they will have to begin with human-gathered data. Unfortunately, most human-gathered data throughout history has been inaccurate, incomplete, or biased in one way or another.

Workforce Adaptation and Skill Gaps

In the same vein, AI is also only as good as the people who create, train, and use them. AI is still in its infancy, so there is still a shortage of skilled workforce who have AI-related expertise.

Companies will need a whole host of data science, machine learning, and AI experts to maximize their use of AI. Or even use it properly—or even use it in a non-disastrous way.

It’s therefore important for companies to develop AI training programs for their employees—as well as hire the few AI experts there are on the market right now.

Integration with Existing Systems

Many businesses struggle with integrating AI tools with the systems they’re currently using legacy systems.

Older hardware, for example, may not be designed to handle the computational power required by AI. Meanwhile, their software programs may need many updates or even complete overhauls to be AI-ready.

In fact, it’s likely that companies may need to conduct some sort of system overhaul if they want to maximize AI.