case study group project

IS501 Team 4 – Final Project Paper (Rough Draft)

Title Page & Abstract

Title: Strategic Information System Recommendations for Aviva Course: IS501 – Management of Information Systems Team 4 – Ranjith (Team Lead), Willie J. Allen III (GQ), Yi Kiu Ho,, [Member 3] Professor: Vanessa Casillas Date: July 2025

Abstract: This paper presents the final recommendations to Aviva stakeholders based on a detailed analysis of strategic information system approaches. Using methodologies learned throughout IS501, the team evaluates options through the lenses of AI, data infrastructure, ethical technology use, and organizational decision-making. The goal is to drive digital transformation with measurable impact.

Section 1: Problem Definition & Business Challenge Framing

Aviva is at a crossroads in its digital transformation journey. As a multinational insurance company, it faces growing pressure from competitors offering hyper-personalized services, streamlined claims systems, and enhanced user experiences powered by AI and data-driven platforms. The challenge is to modernize its systems without compromising on ethics, compliance, or customer trust. Our initial proposal identified these core issues and began framing solutions through stakeholder and systems analysis.

Section 2: Individual Analyses

AI & Predictive Decision Support – by Willie J. Allen III

Artificial Intelligence (AI) has become more than a buzzword—it's now a fundamental building block for competitive decision-making in data-rich industries such as insurance. For Aviva, AI can serve as a catalyst for achieving operational excellence and precision in customer engagement, risk evaluation, and claims handling. This section explores how integrating AI into Aviva’s core systems—specifically within its Customer Relationship Management (CRM) and claims workflows—can augment decision-making capabilities, enhance speed and accuracy, and foster long-term strategic advantages.

At its core, AI-driven Decision Support Systems (DSS) blend structured data with advanced analytics to provide human decision-makers with actionable insights. These tools move beyond static dashboards to incorporate predictive analytics, natural language processing (NLP), and real-time data mining. Aviva can leverage these capabilities in three high-impact areas: policy recommendation engines, fraud detection in claims processing, and churn prediction within customer service functions.

According to Laudon & Laudon (2021), modern organizations must differentiate between structured, semi-structured, and unstructured decisions. AI shines particularly in semi-structured domains—where patterns can be learned but human judgment is still valuable. For example, using supervised learning algorithms, Aviva can identify high-risk policy applicants by analyzing unstructured behavioral data alongside structured actuarial inputs. This enables better underwriting accuracy and targeted customer outreach.

AI also supports operational efficiency. Automating low-value, repetitive tasks—such as data entry or standard claims triage—frees human staff to focus on exceptions and complex cases. Over time, this hybrid model can reduce cycle times by up to 40% and improve consistency in service delivery. Drucker’s assertion that 'you can’t manage what you don’t measure' is especially relevant here: AI makes it possible to measure, track, and improve decision processes with real-time dashboards and machine-generated reports.

However, the benefits of AI come with ethical and strategic responsibilities. Bias in training data, lack of algorithmic transparency, and over-reliance on automated systems can result in reputational and legal risks. It is critical that Aviva implement AI governance frameworks, including fairness audits and explainability protocols, to comply with GDPR and maintain customer trust. Additionally, employee upskilling must accompany any AI rollout to ensure the workforce remains empowered, not displaced.

Based on this analysis, our team recommends that Aviva implement a phased AI strategy, beginning with a predictive analytics module in its CRM. This pilot can demonstrate value in customer retention and cross-selling strategies while building internal confidence in AI tools. Long-term, the architecture should expand into real-time claims analysis and underwriting decision aids—always with ethical oversight and human involvement.

In conclusion, AI offers a viable path for Aviva to transition from reactive to proactive decision-making. By integrating AI-enhanced DSS into its ecosystem, Aviva will not only improve its bottom line but also reinforce its reputation as a forward-thinking, customer-first organization. This vision aligns with Drucker’s broader management principles and supports a scalable, resilient information system strategy for the next decade.

In today’s data-rich environment, artificial intelligence (AI) presents an opportunity for Aviva to elevate decision-making capabilities. By deploying AI models within customer service and claims processing workflows, Aviva can automate lower-level decisions while offering predictive insights for more complex cases. Decision Support Systems (DSS) augmented by AI can interpret behavioral patterns and recommend next-best actions in real-time.

This directly aligns with structured vs. unstructured decision frameworks discussed in Laudon & Laudon (2021), where AI assists with semi-structured tasks to reduce inefficiencies and enhance quality. AI also reinforces Drucker’s belief that 'what gets measured gets managed.' By embedding machine learning into core systems, Aviva gains measurable insight into customer behavior, operational lags, and retention risk.

That said, ethical implementation is key. Models must be transparent and explainable to reduce bias and regulatory risk. Our team recommends a hybrid model where AI tools assist, but don’t fully replace, human judgment.

Data Infrastructure & BI Modernization – []

[Placeholder for Member 2’s section: discuss CRM, business intelligence, cloud platforms, integration systems]

Ethics, Risk & Customer-Centric Design – [Member 3]

[Placeholder for Member 3’s section: discuss IS ethics, bias in systems, compliance frameworks]

Section 3: Final Recommendation & Synthesis

After evaluating each area, our team recommends Aviva invest in an integrated AI-enhanced CRM solution. This would allow the company to automate initial customer interactions, streamline claims triage, and enable personalized offerings. This solution supports predictive analytics, improves customer lifetime value tracking, and creates transparency in customer-facing decisions.

Implementation should be staged: starting with high-ROI pilot projects and scaling based on KPI performance. Change management, staff training, and internal dashboards will support adoption. This approach honors Drucker’s framework of effective information flow while delivering competitive advantage and operational efficiency.

Conclusion

Aviva’s success in the next decade hinges on making bold, ethical, and data-informed decisions today. Through systems analysis, stakeholder mapping, and course-aligned tools, our group has crafted a recommendation that blends modern tech with timeless principles. The solution is not just about tools—it’s about transforming how Aviva thinks, operates, and leads.

References

Laudon, K. C., & Laudon, J. P. (2021). *Essentials of Management Information Systems* (14th ed.). Pearson.

Drucker, P. F. (2007). *Management: Tasks, Responsibilities, Practices*. HarperBusiness.

[Additional team references to be inserted by each member]

Section 2: Individual Analyses (Revised)

AI Integration into Claims Workflow – by Ranjith Yachamaneni

[Content TBD: claims automation, fraud detection, and AI-enhanced intake systems]

Digital-First Strategy & Workflow Optimization – by

[Content TBD: business transformation strategy, operational KPIs, system roadmap]

AI’s Impact on Workforce Roles & Job Design – by Willie J. Allen III

(Note: This section has been expanded to incorporate ethical, cultural, and organizational insights previously assigned to Yinxiao Ji.)

Artificial Intelligence (AI) has become more than a buzzword—it's now a fundamental building block for competitive decision-making in data-rich industries such as insurance. For Aviva, AI can serve as a catalyst for achieving operational excellence and precision in customer engagement, risk evaluation, and claims handling. This section explores how integrating AI into Aviva’s core systems—specifically within its Customer Relationship Management (CRM) and claims workflows—can augment decision-making processes and unlock new efficiencies. At its core, AI-driven Decision Support Systems (DSS) blend structured data with advanced analytics to provide human decision-makers with actionable insights. These tools move beyond static dashboards to incorporate predictive analytics, natural language processing (NLP), and real-time data mining. Aviva can leverage these capabilities in three high-impact areas: policy recommendation engines, fraud detection in claims processing, and churn prediction within customer service functions... [continued from previous section already drafted]