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Description of Djøf Organization

Questions:

1.Briefly describe the Organisation. What is the Ownership? What goods or services are provided? What is the recent Financial Performance? What is the mission, strategies and priorities? Who are the main Competitors? What are the major market trends? What are the major Stakeholder requirements?

2.Briefly describe the IT KM solution chosen. What is the new functionality and capabilities delivered by this solution? What processes are affected? What are major benefits? Which knowledge workers benefit? How?

3.What were the Business Goals for the IT project? What are the success metrics? Is there a financial justification in terms of ROI, Payback and Net Present Value? How should the solution continue to evolve in the future?

Artificial intelligence is a result of technological advancements. It can be described as the intelligence of the machines, the decision making ability of the in-built programs (Cohen and Feigenbaum 2014). Contract review and analysis is a rationale analysis process that clarifies the facts, delivers the forecasts of risks and measures the feasibility of any contract.  The following case study describes the utilization of an artificial system by Djøf, a professional organisation situated in Denmark that deals in academics. The association provides education in law, economics, business, social and political science. Due to the management and governance of unstructured corporate data, the organisation has relied on an artificial intelligence system. The purpose of this study is to analyse the case and describe the organisation and its missions and strategies, the functionality of the implementation of the technological system and to discuss the business goals and success metrics of the IT project.

Djøf is a professional organisation in Denmark, which provides academic opportunities for graduates and students in business, law, economics, political and social sciences. More than ninety thousand employees, working at different private and public sectors are represented by this organisation. Although it is a main board that leads the organisation, but politically it is divided into 6 federal departments. Some are Djøf public, Djøf Privat, Djøf Lawyers, Djøf Students and others (Djoef.dk, 2017). Development and enhancement in operations in both private and public sectors are the major significance of the objectives of Djøf. The organisation represents hard-core management in administration, human resources, marketing research and academics. Different professional events and courses are offered by the organisation throughout the country.


Strategy of employment of the company is to ensure the availability of proper physical and mental conditions of working of the employees. To ensure the progress of the business the organisation emphasizes on cultural changes in society and strategy of competence development lifelong. Following the market trend, which is collaborative result of global competition and technological advancement that elevated the demand of academic labour instead of regular employment, business policy of Djøf is developed on the basis of eight growth drivers. A strong goal towards education and research, maintenance of the global position as an IT expert nation, removal of inappropriate bureaucracy are the part of the strategy. Dolphins is one of the biggest market competitor of Djøf. Financial performance of the organised had been enhanced since 2012 and till now the organisation has a deep knowledge about the labour market. Additionally Djøf provides advice and legal assistance for cases like workplace dismissals and problems. Salary statistics of this organisation has been proved to be realistic.

IT KM Solution Chosen by Djøf for Contract Review and Analysis

Due to the vastness of the organisation, human labour in processing of information and maintenance of management, governance and analysis of scattered data had been proved to be error-prone and costly. Contract review and analysis is a very important task for any organisation and the review can only be done by experts. To minimise the error and maximise the benefits artificial intelligent systems are being used now-a-days, which provides semantic classification and congregating of clauses those are relevant inside legacy contracts. Advances in artificial intelligence help in using network technologies to detect the oddity in decision making patterns. With the help of artificial intelligence an organisation can fix issues relating legacy contracts (Brodie, Mylopoulos and Schmidt 2012).

To manage near about ten thousands employee contracts, Djøf required a good artificial intelligence system to manage data. To complete the job, the organisation has selected RAVN ACE platform. The operational systems of RAVN ACE platform is of the leading experts in national Language Processing and Machine Learning branches of Artificial Intelligence (Ravn.co.uk, 2017). With the technology of its artificial intelligence contract governance process as well as risk analytics can be managed very easily (Korbicz et al. 2012). The artificial intelligence based on RAVN ACE platform provides an understanding to automate the knowledge intensive procedures. This technology offers delivery of expertise for a very long term, bringing competitive advantages to the organisation and structuring the unstructured data (Tallon, Ramirez and Short 2013).


Major benefit of the process is description, diagnosis and prediction with a proper quantitative analysis. It has been seen that the analysis delivered by human experts in the field of management are error-prone depending on their experience. An organisation like Djøf, which chiefly deals in law and business economics is vulnerable to the results of the errors. A good system with artificial intelligence reduces the errors to a minimal rate. Organisational data like specific employment contracts and others are categorized intelligently by different techniques like semantic and methodical analysis (Castelluccio 2017). The data can be identified and classified in this method using artificial intelligence. The data can be accessed form anyplace of the enterprise such as local drives or business related application by using RAVN ACE platform. Irrelevant contents can be removed easily which automatically reduces the maintenance cost. Following the general compliance rules of the organisation the confidential data are protected by the method of general data protection.

Both artificial intelligence and information processing technologies are collaboratively performed by RAVN ACE system. Overall the platform delivers discovery, management , analysis and governance of scattered corporate data (Tallon 2013). This scattered or unstructured corporate data is generated from various emails, threads of discussion, collaborative sites, shared drives and many more and the data are very hard to analyse for the human experts. This artificial intelligence technology helps in reading, extracting and intercepting crucial business data automatically from the heap of unstructured data (Russell, Dewey and Tegmark 2015). Required data can be integrated into external contracts or secured management systems. The tools that are used by the platform are transformative and they took part in comparison between the contract estate of the organisation and cross section present in Danish market. Workers will also be benefitted as they will be able to operate with the structured data.

Business Goals for the IT Project

Business of the organisation, Djøf, is totally related with their productivity and in order to maintain and enhance it, it has integrated technological advances like addition of artificial intelligence to its business model. Currently more the 90,000 members of this organisation are working in various sectors in the law and business fields. The organisation maintains its operation in various sectors like administration, management, human resources, law, marketing, academics and research and that provides many opportunities to expand their business in different directions.

To support this business goals strategically the organisation needs to integrate technology its business model. The majority of the analysis and governance tasks are related unstructured corporate data and for that the first objective of the organisation has become to sort out the data strategically and intelligently (Prowse 2012). For this purpose, Djøf has selected RAVN ACE platform that provides artificial intelligence to deal with these matters. To maintain the strong position in the market any company needs to secure its data first and maintain the confidentiality and for this purpose an artificial intelligence system is more reliable than human employees (Kaplan 2015). With the help of this artificial intelligence solution, Djøf has been able to maintain the confidentiality of its contract review and analysis. Additionally this system will help to access and store the data, so that it can be reviewed in future (Nilsson 2014). According to the head of the public sector division of the organisation this platform of RAVN ACE is the required one for the completion of the present and future goals of the Djøf. The organisation will surely look to dominate the labour market in the near future and this technological platform incorporated by artificial intelligence will be able to gather market information swiftly and confidentially. To bring in the further revolution in its business model Djøf requires a transformative tool of action and that is provided by the artificial system. The system provides encouragement in knowledge sharing, efficiency in search and management of information and improved productivity. Information can be gathered quicker from both external and internal sources. Employees will also be benefitted with the discovery of the link between knowledge assets and them.

Conclusion:

From the above report, it can be concluded that artificial intelligence is the revolution in technological advances that is changing the traditional business models or strategies by a lot and the changes are productive. Artificial intelligence reduces the amount of error in works, provides a strong calculation or prediction capability basin on its programming. The storage of unstructured corporate data can create a massive confusion in contract review and analysis. Therefore, to sort out the data a huge number of experts will be required but it will increase the cost of any organisation. Artificial system deals with these problems in no time and with less amount of cost. It also provide services like data search, maintenance of confidentiality and other services that can help an organisation to expand its business in the future. However, sole integration of artificial intelligence into a business will reduce its innovative thinking along with the significance of employment.

References:

Brodie, M.L., Mylopoulos, J. and Schmidt, J.W. eds., 2012. On conceptual modelling: Perspectives from artificial intelligence, databases, and programming languages. Springer Science & Business Media.

Castelluccio, M., 2017. Artificial intelligence in business. Strategic Finance, 98(10), p.55.

Cohen, P.R. and Feigenbaum, E.A. eds., 2014. The handbook of artificial intelligence (Vol. 3). Butterworth-Heinemann.

Djoef.dk. (2017). Et fællesskab der styrker din karriere. [online] Available at: https://www.djoef.dk/ [Accessed 21 Oct. 2017].

Kaplan, J., 2015. Humans need not apply: A guide to wealth and work in the age of artificial intelligence. Yale University Press.

Korbicz, J., Koscielny, J.M., Kowalczuk, Z. and Cholewa, W. eds., 2012. Fault diagnosis: models, artificial intelligence, applications. Springer Science & Business Media.

Nilsson, N.J., 2014. Principles of artificial intelligence. Morgan Kaufmann.

Prowse, M., 2012. Contract farming in developing countries: a review.

Ravn.co.uk. (2017). RAVN Systems | Artificial Intelligence. [online] Available at: https://www.ravn.co.uk/products/artificial-intelligence/ [Accessed 21 Oct. 2017].

Russell, S., Dewey, D. and Tegmark, M., 2015. Research priorities for robust and beneficial artificial intelligence. Ai Magazine, 36(4), pp.105-114.

Tallon, P.P., 2013. Corporate governance of big data: Perspectives on value, risk, and cost. Computer, 46(6), pp.32-38.

Tallon, P.P., Ramirez, R.V. and Short, J.E., 2013. The information artifact in IT governance: toward a theory of information governance. Journal of Management Information Systems, 30(3), pp.141-178.

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