HCI research has explored AI as a design material, suggesting that designers can envision AI’s design opportunities to improve UX. Recent research claimed that enterprise applications offer an opportunity for AI innovation at the user experience level. We conducted design workshops to explore the practices of experienced designers who work on cross-functional AI teams in the enterprise. We discussed how designers successfully work with and struggle with AI. Our findings revealed that designers can innovate at the system and service levels. We also discovered that making a case for an AI feature’s return on investment is a barrier for designers when they propose AI concepts and ideas. Our discussions produced novel insights on designers’ role on AI teams, and the boundary objects they used for collaborating with data scientists. We discuss the implications of these findings as opportunities for future research aiming to empower designers in working with data and AI.

The emergence of online labor markets has concentrated a lot of attention on the prospect of using crowdsourcing for software development, with a potential to reduce costs, improve time-to-market, and access high-quality skills on demand. However, crowdsourcing of software development is still not widely adopted. A key barrier to adoption is a lack of confidence that a task will be completed on time with the required quality standards. While good managers can develop good, intuitive estimates of task completion when assigning work to their team members, they might lack similar intuition for individuals drawn from an online crowd. The phrase, "Post and Hope" is thus sometimes used when talking about the crowdsourcing of software-development tasks. The objective of this paper is to show the value of replacing the traditional, intuitive assessment of a team's capability with a quantitative assessment of the crowd, derived through analysis of historical performance on similar tasks. This analysis will serve to transform "Post and Hope" to "Post and Expect." We demonstrate this by analyzing data about tasks performed on two popular crowdsourcing platforms: Topcoder and Upwork. Analysis of historical data from these platforms indicates that the platforms indeed demonstrate some level of predictability in task completion. We have identified certain factors that consistently contribute to task completion on both the platforms. Our findings suggest that a data-driven decision processes can play an important role in successful adoption of crowdsourcing practice for software development.

For large scale enterprise implementations, a key problem, that has not been tackled much, is the ability to automatically map users’ requirements to reference process models. We present a tool called Process Model Requirements Gap Analyzer (ProcGap), which uses a combination of natural language processing, information retrieval and semantic reasoning to automatically match and map textual requirements to industry-specific process models. We present the results of mapping requirements from an industry project to an existing process model. We compare our approach to two previously implemented approaches and show that our approach outperforms them. In a case study, we also found that a user group with ProcGap had better performance than a user group that performed the same task manually.

Practitioners spend significant amounts of time creating high-level design from requirements. Though there exist methodologies to describe and manage requirements and design artifacts, there is not yet an automated way to faithfully translate a requirement into a high-level design. While it is extremely difficult to generate design elements from free-form natural language due to its inherent ambiguity, it is possible to significantly improve the accuracy of the design from relatively structured and constrained natural language. In this paper we propose a technique to generate high-level class diagrams from a set of requirements, using a set of requirement-specific heuristics. In this approach, we leverage work we had previously done to first process a requirement statement to classify it into a requirement type, and then break it into various constituents. Depending on the requirement type and its constituents, our heuristics then discover a functional design comprising of coarse-grained modules, their relationships and responsibilities. We express the design as a UML class diagram in IBM rational software architect (RSA) format. Our preliminary investigation shows that the resulting class diagram is rich, and can be used by practitioners as a basis for further design.

In this paper, we give a progress report on an ongoing effort at Accenture to develop a technology platform for building a wide range of corporate radar applications,which can turn the Web into a systematic source of business insight. Our goal is to share the platform we have developed and the lessons we have learned, so others can leverage this knowledge when building similar applications. We give an overview of this platform, which integrates a combination of established AI technologies - i.e. semantic models, natural language processing, and inference engines - in a novel way. We then illustrate the kinds of corporate radars that can be built with our platform through two applications we developed at Accenture: the technology lifecycle tracker, which assesses the maturity of technologies from the wireless industry, and the technology trend tracker, which measures hype versus reality for emerging technology trends such as cloud computing, software-as-a-service, and more. Finally, we discuss our experiences in using this platform to build these applications and the lessons learned.

In this paper, we present a knowledge based approach to capture rich semantic representations from online news sources for business intelligence (BI) applications that know the representations of interest in advance. Our approach performs this task by generating phrases from these representations and matching these phrases against the news using a set of syntactic and semantic transformations. The representation that best matches a piece of news is selected as its meaning. We present two evaluations showing how our approach performs well on capturing the semantics of online new sources for BI applications.