Managing content and data is a challenge for many business processes. Companies can progressively apply AI to rationalize and optimize a variety of diversified, structured and unstructured information
AI content management means solving the management of a plurality of contents thanks to the help of technologies associated with Artificial Intelligence . The contribution of AI comes to the rescue of organizations that are literally drowning in data. Considering that 90% are unstructured data such as documents, images, e-mails, online data and videos, the strategic value of AI content management is clear. The ability to organize a heterogeneity of digital materials in such a way that computers can easily process information helps manage the rising wave of big data , extracting value from information.
Index of topics
AI content management: what is it for
The problem is that unstructured data is not analyzed in most organizations . This is how valuable information is lost unless an amount of time and human resources dedicated to extraction, processing and classification are used.
This is why forward thinking companies are leveraging advances in AI content management. In particular, the development of machine learning and Artificial Intelligence allows the generation of models within the information that allow to unlock contents thanks to the use of natural language, the use of speech recognition or image recognition as well as others technologies useful for processing data, images and videos.
Content intelligence: why it matters and how it works
Applying cognitive technologies to content does not require an immediate change of pace. In fact, AI content management can be introduced through a series of gradual steps. The greater the value an organization wants to obtain from its content, the more advanced the cognitive technology to apply. For this reason, content intelligence is a journey that organizations must take, increasing the value of content over time .
There are different levels of content intelligence . The first level is the most basic, where the content is simply digitized, taken from its native format, such as photos or paper documents, and archived digitally. Organizations begin their journey by starting with processes that are not particularly intelligent from a content point of view. The final goal, however, should be to have an intelligent system, capable of processing a vast range of contents in the most disparate forms, providing a greater degree of understanding, to obtain those smart dataorganizations need so badly. Ai content management models allow systems to generate value-added intelligence in the ocean of content without requiring human intervention.
Going up in rank , the evolution of cognitive abilities to a greater degree translates into a growth in value for companies that can strategically tackle even very difficult business problems. The smarter a content management service is, the more it is able to take on and solve tasks previously managed by employees with a waste of fatigue and less functional working hours. The goal of AI content management is to automatically create the meaning of unstructured content, transforming it into structured data that can be linked in various systems and processes. In this way, AI helps automate by speeding up processes and eliminating margins for error.
Content Intelligence use cases: administration
A wide range of industries are making use of AI content management to derive value from unstructured data. The purchasing processes for payments related to credit and debt management, for example, are areas where content intelligence can have a huge impact. With many companies still making purchases or payments based on paper documents, processing them is a time-consuming and physically demanding operation on the part of multiple employees.
For companies with hundreds or thousands of suppliers, managing a huge amount of paper invoices is a huge waste of resources. By applying AI to accounting processes , companies can create processes that are more efficient, accurate and cost-effective. Ai content management allows systems to automatically recognize and extract customer names, addresses, billing terms and other useful information from invoices. The tools that, moreover, can identify anomalies and identify relevant data in invoices, direct the results to human managers for the approval or application of internal codes for the validation of the payment process.
Content Intelligence use cases: customer services
Banking and financial institutions are the players that are turning more to automated services to replace all paper-based processes with digital ones. But online document management does not in itself mean innovation. Many banking processes such as onboarding new customers, processing a loan, or verifying an individual or corporate identity involve processing a diverse set of content. Ai content management allows you to get to know customers, real and potential, better, allowing the world of finance to be more proactive in offering. An example? By quickly analyzing a variety of data to quickly decide whether to proceed with a loan or a loan, content intelligence allows you to make a difference in the business.
Content Intelligence use cases: compliance
Ai content management allows institutions to automatically develop compliance or regulatory policies, completing and filing the necessary paperwork and then ensuring that no rules and regulations are violated. When a bank does business with a company or individual, there are many steps that must be taken to ensure compliance that become part of a Know Your Customer (KYC) file, and many of these steps involve contents of various documents. For the insurance, banking or Telco world, for example, which face large penalties in the event of non-compliance or which present other large potential risks, climbing the ladder of cognitive intelligence becomes a strategic way to guarantee the quality of ‘offer.
Intelligent evolution of digital transformation
Businesses are striving to achieve digital transformation goals by combining information digitization, collaboration, mobility and intelligence to help businesses take advantage of the data economy. Without content intelligence, digital transformation just isn’t possible. Combining process automation with content intelligence through AI helps automate content-centric processes by enabling companies to use human resources more efficiently and constructively, who can take care of supervision and validation processes , engaging in higher-value jobs.
AI content management within an appropriate data strategy
Even AI content management to be effective must be part of a broader data strategy , to define which it is necessary to focus on some essential elements:
- the ability to select data relevant to analytical purposes, excluding unnecessary information that could frustrate or distort the results;
- the construction of decision-making pipelines based on insights, with the possibility of learning hidden evidence and automating some activities thanks to artificial intelligence ;
- the integration of different information by format and origin, which allows the application of the analytical models in different contexts, according to the principles of repeatability and flexibility;
- the assumption of responsibility by the company in relation to the security and compliant use of data, also in order to obtain the trust of users.
It is then necessary to consider some equally fundamental issues such as the way and the purposes with which the data are used, evaluating, for example, how insights impact on decision making or if there are specific figures in charge of the analysis.
Cultural factors are then inspected , verifying what is the attitude of employees towards the use of information and analytical tools. Then, aspects such as data and access management (the way in which information is stored, protected and shared) or the presence or lack of internal data science skills are investigated . Another criterion to observe is the company’s propensity for an ethical use of information, which focuses on regulatory compliance and the protection of privacy.
We must then make some reflections on the link between the use of data and resilience: did analytics make it possible to anticipate sudden crises or to respond more effectively to any critical issues? Is the corporate data management system capable of withstanding the brunt of a possible emergency?
Looking to the future , it is finally necessary to question the ability of the data strategy to follow changes in the business: can the information management and analysis processes currently implemented meet future business needs
