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AI Business Strategies and Applications

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[Neuschwanstein Castle, Germany]
 

Leaders Chase Vision While
Managers Chase Goals

 

- Overview

Most CEOs recognize that AI has the potential to revolutionize the way organizations operate. For example, they could envision a future in which retailers offer personalized products before customers even ask for them—perhaps on the same day they are produced. This scenario might sound like science fiction, but the AI that makes it possible already exists.

Today, every organization relies on data. This data can be used to improve employee satisfaction, operations, understand customer behavior, optimize supply chains, and more.

Therefore, data plays an important role in the success of an organization. However, based on the volume, velocity, and variety of data being used, the best results can only be achieved with business intelligence (BI).

The latest BI tools open up many possibilities to uncover new ideas, economies and innovations. By optimizing data usage, organizations can be more proactive in their day-to-day activities. 

 

- Business Intelligence

The use of AI in business applications and operations is expanding. AI is the future of business intelligence. Business intelligence (BI) can be defined as systems that combine:

  • Data gathering
  • Data storage
  • Knowledge management

with analysis to evaluate complex corporate and competitive information for presentation to planners and decision makers, with the objective of improving the timeliness and the quality of the input to the decision process.

Business intelligence is a set of methods, processes, architectures and technologies that transform raw data into meaningful and useful information for more effective strategic, tactical and operational insights and decisions. 

According to this definition, business intelligence covers information management (data integration, data quality, data warehousing, master data management, text and content analysis, etc.). Therefore, data preparation and data consumption are two separate but closely linked parts of the business intelligence architecture stack.

 

- AI Business Strategies

As your organization grows, it will generate more and more data. Just as an effective data strategy will ensure that information growth is properly managed, an effective AI strategy will ensure that information growth is translated into business value. Use your data to: 

  • Segment customers and products into groups with similar behaviors and needs
  • Predict customer purchase and churn risk
  • Estimating the lifetime value of a customer or product
  • Optimize manufacturing supply chains and perform predictive maintenance to increase uptime

However, to realize the full potential of AI, companies must reimagine their business models and the way work is done. They can't just plug AI into existing processes to automate or add insights. 

While AI can be used natively in the functionality of a range of specific applications (called use cases), this approach will not drive corresponding changes in a company's operations or profits. It also makes it harder and more expensive to scale AI, as each dispersed team has to reinvent the wheel in terms of stakeholder buy-in, training, change management, data, technology, and more. 

Without an effective strategy and roadmap, many companies find themselves in a technical dead end: they initially select technologies that cannot scale or support cutting-edge AI when they are developed. Poor strategy can result in siled projects that cannot build on each other into a comprehensive AI program. 

An effective AI strategy is opinionated and actionable. They are based on real-life experiences of AI practitioners and provide results. With a time-tested AI strategy, the investments you make today will continue to provide value in the future.

 

- AI and Businesses 

Artificial intelligence (AI) and machine learning (ML) are no longer just buzzwords. Emerging technologies are changing our daily lives. It is clear that AI and ML will play a major role in transforming business in the coming years. 

The use of AI in business is now mainstream, with many organizations adopting AI as a stand-alone technology for specialized use cases or embedding it in general-purpose enterprise software systems that handle core business processes. 

Data security, process automation, and customer service are the top areas for AI applications. Natural language processing (NLP) is at the forefront of AI adoption. Here are 5 ways AI will change business: 

  • AI-powered chips: AI-powered microprocessor chips will support applications that run on AI algorithms. The chips will boost the performance of complex applications used in gaming, healthcare, manufacturing and banking. Qualcomm is at the forefront of the development of these chips.
  • Cybersecurity: With the availability of data, cyberattacks have increased dramatically. Companies are investing in improving their cybersecurity infrastructure. AI will play a key role in advancing corporate cybersecurity. The technology will also reduce the company's response time and expense.
  • Voice search: Google and Amazon have captured the largest market for voice-enabled smart home products. Apple is joining the fray with its own smart speaker. In the future, the company will work on applications that support this voice-based technology.
  • Chatbots for Customer Support: In a digital-driven world, companies use AI-powered chatbots in business communications. These bots are mainly used to increase customer engagement. AI-powered chatbots minimize the need for human intervention.
  • Data Highway: Companies and users have realized the value of data. The generation of data is growing exponentially. AI-based systems and solutions will enable startups to conduct data mining, business data analysis, and predictive analytics implementation.

 

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[Bergen, Norway - eirikbjo]

- Business Intelligence Tools and Applications

Business intelligence (BI) software is a tool for retrieving large volumes of unstructured data from internal and external systems. It then analyzes and transforms this data to power business intelligence reporting. 

Modern businesses increasingly require BI software. As demand grows, so does the development effort. Thus, the future of BI is likely to add features for improved usability for non-technical users, more streamlined workflows, and greater predictive capabilities.

Among many uses, business intelligence tools enable organizations to gain insight into new markets, assess the demand and suitability of products and services in different market segments, and measure the impact of marketing efforts. 

BI applications use data collected from a data warehouse (DW) or data mart, the concepts of BI and DW combined as "BI/DW" or "BIDW". Data warehouses contain copies of analytical data useful for decision support.

 

- AI Applications in Business

Artificial intelligence (AI) is all around us. You've probably used it on your daily commute, searching the web, or checking your latest social media updates. 

Whether you realize it or not, AI will have a huge impact on your life—and your business. Here are some examples of AI that you probably already use every day. 

  • AI-enabled innovations, products and services
  • Automating routine cognitive work
  • AI for leveling up workers
  • AI as a creative force
  • Accessing and organizing knowledge via AI
  • AI for optimization
  • Higher productivity and more efficient operations
  • More effective learning and training through AI
  • AI as coach and monitor
  • Decision support
  • AI-enabled quality control and quality assurance
  • AI for personalized customer services, experiences and support
  • Safer operations
  • AI for functional area improvements
  • AI for addressing industry-specific needs

 

 

[More to come ...]

 

 

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