Skip to main content

Abstract

Before we explain how AI could be leveraged to dramatically transform the Solutioning phase, it is important to outline the role of this phase across the Service Delivery lifecycle as well as the major personas involved.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 44.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 59.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Nezhad HRM, et al. eAssistant: cognitive assistance for identification and auto-triage of actionable conversations. WWW2017.

    Google Scholar 

  2. Màrquez L, Carreras X, Litkowski KC, Stevenson S (2008) Semantic role labeling: an introduction to the special issue. Comput Linguist 34(2):145–159

    Article  Google Scholar 

  3. Nigam K, Hurst M (2004) Towards a robust metric of opinion. In Proceedings of the AAAI Spring Symposium on Exploring Attitude and Affect in Text: Theories and Applications.

    Google Scholar 

  4. Bakis R, Connors DP, Dube P, Kapanipathi P, Kumar A, Malioutov D, Venkatramani C (2017) Performance of natural language classifiers in a question-answering system. IBM J Res Develop 61(4):14:1–14:10

    Article  Google Scholar 

  5. Roth M, Klein E ( 2015) Parsing software requirements with an ontology-based semantic role labeler. Proceedings of the 1st Workshop on Language and Ontologies

    Google Scholar 

  6. Käding C et al (2016) Fine-tuning deep neural networks in continuous learning scenarios, Asian Conference on Computer Vision. Springer, Cham

    Google Scholar 

  7. Sumeet A, et al. (2007) How much noise is too much: A study in automatic text classification. Data Mining, 2007. ICDM 2007. Seventh IEEE International Conference on. IEEE.

    Google Scholar 

Download references

Acknowledgements

We’d like to thank the whole “Cognitive Solution Designer” research and development team and all visionary supporters of this project.

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

Copyright information

© 2018 The Author(s), under exclusive licence to Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Kloeckner, K. et al. (2018). AI for Solution Design. In: Transforming the IT Services Lifecycle with AI Technologies . SpringerBriefs in Computer Science. Springer, Cham. https://doi.org/10.1007/978-3-319-94048-9_4

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-94048-9_4

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-94047-2

  • Online ISBN: 978-3-319-94048-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics