Artificial Intelligence (AI) was one of the main themes of the CeBIT 2017, and its application possibilities seem limitless. These range from autonomous vehicles to robots that help children with their homework − many applications sound as if they came straight out of a science fiction novel.
However, what counts in the business environment are not dazzling visions but solid facts. Before you even consider the often quite sizable investments in the field of AI, you need to have clear answers to the following questions:
- Which business areas would benefit concretely from the use of AI?
- What measurable competitive advantages can be achieved by AI?
If you have a well-founded AI strategy and have taken the first steps towards realizing it, you are well on the way to becoming an organization referred to in the Anglo-American sphere as a “predictive enterprise” (http://www.huffingtonpost.com/james-canton/the-predictive-enterprise_b_8656326.html). Here are the most important aspects of this forward-looking strategy:
- Deep learning that mimics and even surpasses human learning
- Big data analytics, which helps to manage large data streams in order to gain insights
- Cloud computing that provides access to knowledge and information from anywhere at any time
- Artificial intelligence that supports business decisions
- Applied data science, which provides a new understanding of business algorithms that help shape the future of business.
Six examples of intelligence doping
The predictive enterprise is no longer a just vision – it encompasses real technologies that have all reached a high level of maturity. Below you’ll find an overview of business applications that can already employ the support of AI and AI-related technologies – specifically the use of enterprise search, which combines all aspects of the predictive enterprise.
1. Automated document processing: Where manual processing of incoming mail or electronic documents such as faxes, e-mails, and attachments cost a great deal of time and money, enterprise search is the solution. The combination of semantic analysis and deep learning automatically detects the difference between a proposal and an order, and in the process, the intelligent classification gets even better over time. The result is that an advanced enterprise search solution provides a success rate of between 85 and 95 percent − with fast and easy implementation, and without the need to set up an elaborate set of rules.
2. Assistance system for health care: Research is becoming more and more important for doctors and medical specialists, yet at the same time, the volume of information is growing. With enterprise search, which combines the strengths of semantic analysis and big data, it is possible to recognize medical patterns from clinical findings, illnesses, and medications and generate correlations, which opens the gateway to a new era in treatment and research. The special features of this clever assistance system are that not only structured data sources, but also unstructured data sources can be included in the analysis.
3. Proactive information management: Already today, artificial intelligence in the form of enterprise search can be used to learn from the user’s everyday work routines and create a personalized interest profile. On the basis of these, it is possible to proactively provide the user with information, for instance as a notification, so that he gets real help for a particular task or simply broadens his horizons without having to spend time searching for relevant content.
4. Recognition of high potentials: In times in which there is a shortage of skilled specialists, increasing focus is being placed on looking to a company’s own employees to fill open positions and on finding the best candidates for the job. Enterprise search can help HR departments by analyzing employee activity and interest profiles on the basis of transparent rules. Subject-specific entries in the company's intranet or in social media, for example, reveal the author's strengths, which would go undetected using traditional tools. A further advantage of enterprise search in this context is that the analysis can be performed across all applications and even include online activities in the assessment.
5. Clever field management: For companies in the service sector with mobile technicians, the question of the optimum utilization and efficiency during on-site service often arises. With the help of artificial intelligence, dispatchers can access a continual 360-degree view of a specific problem from the available, often scattered information, and can thus optimally prepare the field staff for the site visit. This saves companies time, money, and wasted kilometers. At the same time, customer satisfaction can be increased.
6. Smart customer service with chatbots: Intelligent systems that interact with customers to automatically respond to inquiries or make recommendations are one of the most important developments in 2017. It is already possible to use technologies such as natural language processing or natural language question answering to simulate human consultation and advice and thus to significantly increase the quality of customer service. Ideally, an enterprise search solution works in the background here as well.
Conclusion: Using enterprise search to become a predictive enterprise
Even the few practical examples from different fields and industries show that already today there are technologies that help companies to operate much more flexibly, faster, and at a higher quality level than ever before. The strategic use of enterprise search, which combines AI, big data analysis, deep learning, and other related tools under one roof, can help a company evolve into a predictive enterprise, delivering tangible competitive advantages.