AI and its impact on app competitiveness

AI in mobile tech world

This is the era of the fourth industrial revolution where technology without artificial intelligence (AI) is unimaginable. With the global acceptance of AI, it has encompassed all spheres, touching human life in several ways that also includes the mobile tech world. Research indicates that AI is rapidly gaining popularity, tech giants like Baidu and Google have already spent between $20 to $30 billion on AI to improve IT operations. Segments like healthcare, education, finance and IT ops are investing heavily in AI, however the prominence of AI in mobile tech world deserves a special mention.

Importance of AI in mobile app

The focus of AI is to develop intelligent machines that think, work and learn from experiences like humans. When AI joined hands with machine learning, the ability to analyze visual inputs such as gesture, object, and facial recognition was made seamless. For example, an iPhone app powered by AI can enhance perception, apply reason and even solve problems.

Deployment of AI in mobile app

AI uses the modest process of trial and error to learn about a solution when it comes to developing mobile app. Through this method, various attempts are made to locate the appropriate solution. Then that solution is stored for future usage, considering it as a reference point for similar circumstances. Along with the solution, the mobile app developers are also focusing on drawing appropriate inferences to enhance the interaction process. This helps users reach predefined solutions addressing various device problems.

Example of AI apps

The following are the existing apps that provides an enriched user experience:

  • Replika is an advanced AI app for iPhone that covers several aspects of a user’s life. This app can have conversations with the user like a real person.
  • App Airpoly can identify three objects in a single second.
  • Cortana can assess relevant information, sort them and deliver services efficiently like scheduling meetings, sending emails, tracking events, sharing updates and reminders.
  • Personal assistant like Siri became popular with its voice interface in place. It assists in phone and text actions, can provide information about weather and currency, schedule events, set reminders and provides an engaging experience.
  • My Starbucks Barista mobile app enabled customers to place their orders by mentioning it to the app.
  • Taco Bot launched by Taco Bell recommended personalized menu considering user-specific purchase trends.

Technologies empowering apps

In order to create apps empowered with AI, developers ensure they choose an appropriate platform and install features keeping the end user preferences in mind. The technologies that improve app performance and competitiveness include:

  1. Speech to text (STT) and text to speech (TTS) engine that converts voice to text message and vice versa.
  2. Tagging helps the app analyze users’ requirement.
  3. Noise reduction engine eliminates white noise improving voice command capacity.
  4. Voice biometrics and recognition works as an authentication for refining security.

Impact of AI on app competitiveness

Innovation has led end users expect better performance from mobile apps. Retail giants like eBay and Amazon have already proved the worth of AI in mobile apps. AI-enabled apps engage its user and strategically secure the brand, enhancing productivity and helps reduce errors. The algorithms present will adjust the app and forms more meaningful and context-rich prospects to keep end-users engaged. AI-aided chatbots on mobile devices use standard messaging tools and voice-activated interfaces, this reduces data collection time and simplifies the task. Also, user specific personalization will help with mundane or repeatable tasks. It even has a great impact in healthcare industry where reliability, predictability, consistency, quality and patient safety has seen improvements with the usage of AI-enabled apps.

AI in app market based on geography

The following geographical areas indicate extensive impact of AI on mobile app:

  • North America
  • South America
  • Europe
  • Asia Pacific
  • Middle East and Africa

Conclusion

We can conclude that AI has a dramatic impact on transformation and competitiveness of mobile app. As per market research, this competition is yet to increase by 2020 since more organizations globally are investing in AI for revenue improvements and cost reductions. The deployment rates among different industry verticals have surged exponentially over the fast few years.

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Pivotal Role of AI and Machine Learning in Industry 4.0 and Manufacturing

Industry 4.0 is a name given to the current trend of automation and data exchange in manufacturing technologies. It includes cyber-physical systems, the Internet of things, cloud computing and cognitive computing.Industry 4.0 is commonly referred to as the fourthindustrial revolution.

Industry 4.0 is the paving the path for digitization of the manufacturing sector, where artificial intelligence (AI) and machine-learning based systems are not only changing the ways we interact with information and computers but also revolutionizing it.

Compelling reasons for most companies to shift towards Industry 4.0 and automate manufacturing include;

  • Increase productivity
  • Minimize human / manual errors
  • Optimize production costs
  • Focus human efforts on non-repetitive tasks to improve efficiency

Manufacturing is now being driven by effective data management and AI that will decide its future. The more data sets computers are fed, the more they can observe trends, learn and make decisions that benefit the manufacturing organization. This automation will help to predict failures more accurately, predict workloads, detect and anticipate problems to achieve Zero Incidence.

GAVS’ proprietary AIOps based TechOps platform – Zero Incident Framework TM (ZIF) can successfully integrate AI and machine learning into the workflow allowing manufacturers to build robust technology foundations.

To maximize the many opportunities presented by Industry 4.0, manufacturers need to build a system with the entire production process in mind as it requires collaboration across the entire supply chain cycle.

Top ways in which ZIF’s expertise in AI and ML are revolutionizing manufacturing sector:

  • Asset management, supply chain management and inventory management are the dominant areas of artificial intelligence, machine learning and IoT adoption in manufacturing today. Combining these emerging technologies, they can improve asset tracking accuracy, supply chain visibility, and inventory optimization.
  • Improve predictive maintenance through better adoption of ML techniques like analytics, Machine Intelligence driven processes and quality optimization.
  • Reduce supply chain forecasting errors and reduce lost sales to increase better product availability.
  • Real time monitoring of the operational loads on the production floor helps in providing insights into the production schedule performances.
  • Achieve significant reduction in test and calibration time via accurate prediction of calibration and test results using machine learning.
  • Combining ML and Overall Equipment Effectiveness (OEE), manufacturers can improve yield rates, preventative maintenance accuracy and workloads by the assets. OEE is a universally used metric in manufacturing as it combines availability, performance, and quality, defining production effectiveness.
  • Improving the accuracy of detecting costs of performance degradation across multiple manufacturing scenarios that reduces costs by 50% or more.

Direct benefits of Machine Learning and AI for Manufacturing

The introduction of AI and Machine Learning to industry 4.0 represents a big change for manufacturing companies that can open new business opportunities and result in advantages like efficiency improvements among others.

  • Cost reduction through Predictive Maintenance that leads to less maintenance activity, which means lower labor costs, reduced inventory and materials wastage.
  • Predicting Remaining Useful Life (RUL): Keeping tabs on the behavior of machines and equipment leads to creating conditions that improve performance while maintaining machine health. By predicting RUL, it reduces the scenarios which causes unplanned downtime.
  • Improved supply chain management through efficient inventory management and a well monitored and synchronized production flow.
  • Autonomous equipment and vehicles: Use of autonomous cranes and trucks to streamline operations as they accept containers from transport vehicles, ships, trucks etc.
  • Better Quality Control with actionable insights to constantly raise product quality.
  • Improved human-machine collaboration while improving employee safety conditions and boosting overall efficiency.
  • Consumer-focused manufacturing: Being able to respond quickly to changes in the market demand.

Touch base with GAVS AI experts here: https://www.gavstech.com/reaching-us/ and see how we can help you drive your manufacturing operation towards Industry 4.0.