How hard is it to develop an AI app? – In the realm of AI, it is a constant journey and not a destination. Indeed, AI developers and experts are on a mission of solving the most complex problem – human behaviour. They are on a path to study patterns and produce results that a human being would most likely exhibit.

In the making of all of this fabulous innovation, what kind of challenges does an AI developer face? What are the hindrances in their role? Does AI Development manager approach in a responsible manner? To answer many such question lets dive deep into some of the stories of AI development.

‘AI – Opportunities’ in mobile app development

AI is kind of magic wand to its innovators, true to its nature of being complex it hosts a bunch of opportunities’ for developers to explore the world….

Voice Enablement Helps in understanding customer better and delivering the best

How often have you called up customer care to complain when the internet is not working or DTH not working? The first thing they ask you is – what kind of problem are you facing? While at times the problem is simple, many times the executives try to know the exact steps to reach a particular problem. While manually saying click this, click that could help, voice recognition or voice enablement allows developers in identifying the exact process that was followed.

As the user says OK Google on his phone, followed by instruction check new emails or the weather or the best deal for iPhone, it helps developers in understanding the behaviour of the customers. The kind of apps they use most, what are the instructions provided, what kind of instructions not working. The voice input also helps in understanding customers expectations from an app. I remember when my nephew instructed Google Home “You are useless,” the answer came in was I am sorry to disappoint you, and I would let my engineers know about it.”

Simplifying Complex needs

The most exciting opportunity for an AI app developer is about streamlining complex processes and workflows. Well, indeed otherwise how would the language translation work out? Or how could a chatbot help in resolving human beings technical problems? Or could you fathom of any human being going through thousands of lines of log to look for something suspicious? Or how about commanding Voice assistant to locate the best restaurant near you serving Mediterranean food?

All these are the needs to structure and present data in the simplified form. Thanks to AI app developer.

‘AI – Challenges’ in Mobile App Development

Well, the aim is to simplify lives but what are the challenges faced by developers?

No Standards tools and languages

While Google has launched some of the projects like Teachable Machine and Google AI tools to let users experience how AI works, it is still a challenge for developers to start off. In fact, Quora is flooded with queries like what are the languages or software used to develop an AI app. Many firms use Python due to the benefits it offers but has its limitations like weak in mobile programming and enterprises desktop shops. Similar is the case for other software languages like – Prolog, JAVA, C++ and LISP programming languages for artificial intelligence research

Lots of data create confusion

However, it’s the data that helps in creating the best AI app; the irony is that its also in a massive amount at times challenging to segregate and structure. With big data buzz and data tracking now a trend, developers at times face a hurdle in putting the data sets in a meaningful way.

The new availability and advancement of AI and ML are causing a revolutionary shift in the way that developers, businesses, and users think about intelligent interactions within mobile applications.

The Future-Ready Factory: The Power of Predictive Analytics in Manufacturing

By : Sampada S M

Published on Mar 6, 2025

Manufacturing Predictive Analytics

In 1989, a missing $0.50 bolt led to the mid-air explosion of United Airlines Flight 232. The smallest oversight in manufacturing can set off a chain reaction of failures. Now, imagine a factory floor where thousands of components must function flawlessly—what happens if one critical part is about to fail but goes unnoticed? Predictive analytics in manufacturing ensures these unseen risks don’t turn into catastrophic failures by providing foresight into potential breakdowns, supply chain risk analytics, and demand fluctuations—allowing manufacturers to act before issues escalate into costly problems.

Industrial predictive analytics involves using data analysis and machine learning in manufacturing to identify patterns and predict future events related to production processes. By combining historical data, machine learning, and statistical models, manufacturers can derive valuable insights that help them take proactive measures before problems arise.

Beyond just improving efficiency, predictive maintenance in manufacturing is the foundation of proactive risk management, helping manufacturers prevent costly downtime, safety hazards, and supply chain disruptions. By leveraging vast amounts of data, predictive analytics enables manufacturers to anticipate machine failures, optimize production schedules, and enhance overall operational resilience.

But here’s the catch, models that predict failures today might not be necessarily effective tomorrow. And that’s where the real challenge begins.

Why Predictive Analytics Models Need Retraining?

Predictive analytics in manufacturing relies on historical data and machine learning to foresee potential failures. However, manufacturing environments are dynamic, machines degrade, processes evolve, supply chains shift, and external forces such as weather and geopolitics play a bigger role than ever before.

Without continuous model retraining, predictive models lose their accuracy. A recent study found that 91% of data-driven manufacturing models degrade over time due to data drift, requiring periodic updates to remain effective. Manufacturers relying on outdated models risk making decisions based on obsolete insights, potentially leading to catastrophic failures.

The key is in retraining models with the right data, data that reflects not just what has happened but what could happen next. This is where integrating external data sources becomes crucial.

Is Integrating External Data Sources Crucial?

Traditional smart manufacturing solutions primarily analyze in-house data: machine performance metrics, maintenance logs, and operational statistics. While valuable, this approach is limited. The real breakthroughs happen when manufacturers incorporate external data sources into their predictive models:

Weather Patterns: Extreme weather conditions have caused billions in manufacturing risk management losses. For example, the 2021 Texas power crisis disrupted semiconductor production globally. By integrating weather data, manufacturers can anticipate environmental impacts and adjust operations accordingly.
Market Trends: Consumer demand fluctuations impact inventory and supply chains. By leveraging market data, manufacturers can avoid overproduction or stock shortages, optimizing costs and efficiency.
Geopolitical Insights: Trade wars, regulatory shifts, and regional conflicts directly impact supply chains. Supply chain risk analytics combined with geopolitical intelligence helps manufacturers foresee disruptions and diversify sourcing strategies proactively.

One such instance is how Mantra Labs helped a telecom company optimize its network by integrating both external and internal data sources. By leveraging external data such as radio site conditions and traffic patterns along with internal performance reports, the company was able to predict future traffic growth and ensure seamless network performance.

The Role of Edge Computing and Real-Time AI

Having the right data is one thing; acting on it in real-time is another. Edge computing in manufacturing processes, data at the source, within the factory floor, eliminating delays and enabling instant decision-making. This is particularly critical for:

Hazardous Material Monitoring: Factories dealing with volatile chemicals can detect leaks instantly, preventing disasters.
Supply Chain Optimization: Real-time AI can reroute shipments based on live geopolitical updates, avoiding costly delays.
Energy Efficiency: Smart grids can dynamically adjust power consumption based on market demand, reducing waste.

Conclusion:

As crucial as predictive analytics is in manufacturing, its true power lies in continuous evolution. A model that predicts failures today might be outdated tomorrow. To stay ahead, manufacturers must adopt a dynamic approach—refining predictive models, integrating external intelligence, and leveraging real-time AI to anticipate and prevent risks before they escalate.

The future of smart manufacturing solutions isn’t just about using predictive analytics—it’s about continuously evolving it. The real question isn’t whether predictive models can help, but whether manufacturers are adapting fast enough to outpace risks in an unpredictable world.

At Mantra Labs, we specialize in building intelligent predictive models that help businesses optimize operations and mitigate risks effectively. From enhancing efficiency to driving innovation, our solutions empower manufacturers to stay ahead of uncertainties. Ready to future-proof your factory? Let’s talk.

In the manufacturing industry, predictive analytics plays an important role, providing predictions on what will happen and how to do things. But then the question is, are these predictions accurate? And if they are, how accurate are these predictions? Does it consider all the factors, or is it obsolete?

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