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Digital Transformation in 2024: Trends and Predictions

Digital transformation has been a buzzword in the business world for the past few years, and for good reason. According to Statista’s latest report, global digital transformation spending is forecasted to reach 3.4 trillion U.S. dollars by 2026. Artificial intelligence (AI), big data, and the cloud are considered to be core transformative technologies with broad applications across multiple industries. As technology continues to advance at a rapid pace, companies must adapt and evolve to stay competitive. But what does the future hold for digital transformation?

In this article, we will explore the top trends and predictions for digital transformation in 2024, shedding light on the future of this ever-evolving landscape. From the rise of artificial intelligence to the integration of physical and digital experiences, we’ll uncover the key drivers shaping digital transformation in the coming years. 

The Rise of Artificial Intelligence (AI)

AI-powered technologies such as machine learning, natural language processing, and robotic process automation are already being used to streamline processes, improve customer experiences, and increase efficiency. 

In 2024, we expect to see even more companies incorporating AI into their digital transformation strategies. This will not only improve internal processes but also enhance the overall customer experience. AI-powered chatbots, for example, will become more sophisticated and will be able to handle more complex customer inquiries, freeing up human employees to focus on more high-value tasks.

Hitee, a conversational AI platform developed by Mantra Labs has helped insurers in India in managing millions of customer queries related to onboarding and retention.

Companies have long struggled to enhance employee and customer experience, with overburdened employees, manual work, and delayed responses to customer queries. The introduction of Gen AI last year has opened new opportunities for companies across industries. For example, gen AI in healthcare can streamline laborious and error-prone operational work, instantly placing years of clinical data at a clinician’s fingertips in seconds and upgrading health systems infrastructure. 

(Read our latest blog on Gen AI to know more: Gen AI’s next leap: Predicting the Future of AI in 2024 & Beyond)

The Importance of Data and Analytics

Data and analytics

Data has always been important in business. In 2024, we can expect to see a continued focus on data and analytics as companies strive to make data-driven decisions.

According to the report by Expert Market Research (EMR), the global predictive analytics market size reached a value of USD 15.70 billion in 2023 and is estimated to increase at a CAGR of 21.7% between 2024 and 2032. Data analytics has opened a new horizon for companies across industries. They can gather and analyze vast amounts of data in real-time enabling them to have a closer look at customer behavior, forecast trends, and optimize their business processes. This helps them offer a better experience and service to their customers and improve operations at the same time. 

Biopharma company like Abbvie uses an AI-powered research tool developed by Mantra to extract information about genes and their interconnectivity from research papers. This helps interpret screening results in an unbiased way, significantly reducing drug development time. 

The Shift to Cloud Computing

Cloud computing has been a game-changer for businesses, allowing them to store and access data and applications remotely. In 2024, we can expect to see a continued shift towards cloud computing as more companies realize the benefits it offers.

Cloud computing not only allows for more efficient and cost-effective data storage, but it also enables remote work and collaboration. 

McDonald’s has collaborated with Google to utilize Google Cloud technology in its restaurants to transform its business and customer experiences. 

Increased Focus on Cybersecurity

Cybersecurity

As technology continues to advance, so do the threats to cybersecurity. In 2024, we can expect to see an increased focus on cybersecurity as companies work to protect their data and systems from cyber-attacks.

With the rise of remote work and the use of cloud computing, companies must ensure that their data and systems are secure. This will lead to the adoption of more advanced cybersecurity measures, such as biometric authentication and AI-powered threat detection.

The Integration of Physical and Digital Experiences

In 2024, we can expect to see a blurring of the lines between physical and digital experiences. With the rise of technologies such as augmented reality and virtual reality, companies will be able to create immersive experiences for their customers.

Companies like Loreal & Nykaa offer AR-powered virtual try-ons where customers can try the product from the comfort of their homes before making the purchase. 

The Continued Importance of Customer Experience

Customer experience

Conclusion

In 2024, customer experience will remain a top priority for businesses. With the rise of digital transformation, companies will have even more opportunities to enhance the customer experience and build strong relationships with their customers.

This will involve using data and analytics to gain insights into customer behavior and preferences, as well as leveraging technologies such as AI and chatbots to provide personalized and efficient customer service. Companies that prioritize customer experience will have a competitive advantage in the digital landscape.

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Machines That Make Up Facts? Stopping AI Hallucinations with Reliable Systems

There was a time when people truly believed that humans only used 10% of their brains, so much so that it fueled Hollywood Movies and self-help personas promising untapped genius. The truth? Neuroscientists have long debunked this myth, proving that nearly all parts of our brain are active, even when we’re at rest. Now, imagine AI doing the same, providing information that is untrue, except unlike us, it doesn’t have a moment of self-doubt. That’s the bizarre and sometimes dangerous world of AI hallucinations.

AI hallucinations aren’t just funny errors; they’re a real and growing issue in AI-generated misinformation. So why do they happen, and how do we build reliable AI systems that don’t confidently mislead us? Let’s dive in.

Why Do AI Hallucinations Happen?

AI hallucinations happen when models generate errors due to incomplete, biased, or conflicting data. Other reasons include:

  • Human oversight: AI mirrors human biases and errors in training data, leading to AI’s false information
  • Lack of reasoning: Unlike humans, AI doesn’t “think” critically—it generates predictions based on patterns.

But beyond these, what if AI is too creative for its own good?

‘Creativity Gone Rogue’: When AI’s Imagination Runs Wild

AI doesn’t dream, but sometimes it gets ‘too creative’—spinning plausible-sounding stories that are basically AI-generated fake data with zero factual basis. Take the case of Meta’s Galactica, an AI model designed to generate scientific papers. It confidently fabricated entire studies with fake references, leading Meta to shut it down in three days.

This raises the question: Should AI be designed to be ‘less creative’ when AI trustworthiness matters?

The Overconfidence Problem

Ever heard the phrase, “Be confident, but not overconfident”? AI definitely hasn’t.

AI hallucinations happen because AI lacks self-doubt. When it doesn’t know something, it doesn’t hesitate—it just generates the most statistically probable answer. In one bizarre case, ChatGPT falsely accused a law professor of sexual harassment and even cited fake legal documents as proof.

Take the now-infamous case of Google’s Bard, which confidently claimed that the James Webb Space Telescope took the first-ever image of an exoplanet, a factually incorrect statement that went viral before Google had to step in and correct it.

There are more such multiple instances where AI hallucinations have led to Human hallucinations. Here are a few instances we faced.

When we tried the prompt of “Padmavaat according to the description of Malik Muhammad Jayasi-the writer ”

When we tried the prompt of “monkey to man evolution”

Now, if this is making you question your AI’s ability to get things right, then you should probably start looking have a checklist to check if your AI is reliable.

Before diving into solutions. Question your AI. If it can do these, maybe these will solve a bit of issues:

  • Can AI recognize its own mistakes?
  • What would “self-awareness” look like in AI without consciousness?
  • Are there techniques to make AI second-guess itself?
  • Can AI “consult an expert” before answering?

That might be just a checklist, but here are the strategies that make AI more reliable:

Strategies for Building Reliable AI

1. Neurosymbolic AI

It is a hybrid approach combining symbolic reasoning (logical rules) with deep learning to improve factual accuracy. IBM is pioneering this approach to build trustworthy AI systems that reason more like humans. For example, RAAPID’s solutions utilize this approach to transform clinical data into compliant, profitable risk adjustment, improving contextual understanding and reducing misdiagnoses.

2. Human-in-the-Loop Verification

Instead of random checks, AI can be trained to request human validation in critical areas. Companies like OpenAI and Google DeepMind are implementing real-time feedback loops where AI flags uncertain responses for review. A notable AI hallucination prevention use case is in medical AI, where human radiologists verify AI-detected anomalies in scans, improving diagnostic accuracy.

3. Truth Scoring Mechanism

IBM’s FactSheets AI assigns credibility scores to AI-generated content, ensuring more fact-based responses. This approach is already being used in financial risk assessment models, where AI outputs are ranked by reliability before human analysts review them.

4. AI ‘Memory’ for Context Awareness

Retrieval-Augmented Generation (RAG) allows AI to access verified sources before responding. This method is already being used by platforms like Bing AI, which cites sources instead of generating standalone answers. In legal tech, RAG-based models ensure AI-generated contracts reference actual legal precedents, reducing AI accuracy problems.

5. Red Teaming & Adversarial Testing

Companies like OpenAI and Google regularly use “red teaming”—pitting AI against expert testers who try to break its logic and expose weaknesses. This helps fine-tune AI models before public release. A practical AI reliability example is cybersecurity AI, where red teams simulate hacking attempts to uncover vulnerabilities before systems go live 

The Future: AI That Knows When to Say, “I Don’t Know”

One of the most important steps toward reliable AI is training models to recognize uncertainty. Instead of making up answers, AI should be able to respond with “I’m unsure” or direct users to validated sources. Google DeepMind’s Socratic AI model is experimenting with ways to embed self-doubt into AI.

Conclusion:

AI hallucinations aren’t just quirky mistakes—they’re a major roadblock in creating trustworthy AI systems. By blending techniques like neurosymbolic AI, human-in-the-loop verification, and retrieval-augmented generation, we can push AI toward greater accuracy and reliability.

But here’s the big question: Should AI always strive to be 100% factual, or does some level of ‘creative hallucination’ have its place? After all, some of the best innovations come from thinking outside the box—even if that box is built from AI-generated data and machine learning algorithms.

At Mantra Labs, we specialize in data-driven AI solutions designed to minimize hallucinations and maximize trust. Whether you’re developing AI-powered products or enhancing decision-making with machine learning, our expertise ensures your models provide accurate information, making life easier for humans

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