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AI Agents: Who do they work for?

AI Agents are being increasingly integrated across multiple roles within organisations, addressing long-standing inefficiencies but introducing challenges like trust and ethical concerns. Key functionalities vary but revolve around improving decision-making, customer interaction, and efficiency.

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In the qualitative reseach entitled “Innovative Approaches in Business Development Strategies through Artifical Intelligence Technlogy” (Edilia & Larasati Oct 2023), the sudy revealed:

The utilization of AI in business is not just about automating tasks; it's about enhancing human capabilities and making informed decisions. From decision-making to information retrieval, AI has the potential to streamline processes and make computer interfaces more user-friendly. Its application in business spans various aspects, easing workloads and offering new avenues for innovation and efficiency.

Key Tasks AI Agents Are Used For

Here’s an overview of the use of AI agents, chatbots, and virtual assistants across a variety of organisational roles, based on recent studies (as cited).

Business Strategists

Researchers

Design Strategists and UX Designers

Business Analysts

Pain Points and Challenges

The following is a comparison of pre & post implementation issues as uncovered by the related cited studies:

Before AI Implementation:

After AI Implementation:

  • Challenges with user trust and adoption due to insufficient “human-likeness” in AI interaction (Chandra et al., 2022).
  • Ethical concerns, such as data privacy and algorithmic bias (Tran, 2024).
visionary thinker

How does Alice use AI Agents?

A Product Manager could leverage Service Bricks AI Agents to streamline workflows, enhance decision-making, and ensure that their company’s solutions align with client needs and market demands. Here’s a breakdown of how these AI agents could assist across key responsibilities:

1. Market Research and Client Insights
  • Automating market trend analysis by collecting and synthesizing data from industry reports, news, and competitor updates.
  • Conducting customer sentiment analysis using feedback and reviews.
  • Predicting market demand through AI-powered forecasting models.
2. Ideation and Product Roadmapping
  • Facilitating brainstorming sessions using AI collaboration tools that suggest ideas based on past product successes and emerging trends.
  • Prioritizing features by analyzing historical data, customer pain points, and ROI predictions.
3. Cross-Functional Communication and Collaboration
  • Summarizing key project updates for stakeholders from multiple channels (email, task management systems, etc.).
  • Managing meeting schedules and creating follow-up action items.
  • Translating technical development updates into user-friendly reports for non-technical stakeholders.
4. Product Development and Testing
  • Automating backlog grooming by grouping related issues, identifying duplicates, and prioritizing tickets based on urgency.
  • Running simulations to predict performance outcomes for various features.
  • Generating test case suggestions based on user scenarios and edge cases.
5. Customer Feedback and Continuous Improvement
  • Aggregating customer feedback from surveys, support tickets, and social media.
  • Identifying patterns in complaints or feature requests.
  • Generating recommendations for product updates or enhancements.
6. Data-Driven Decision Making
  • Providing dashboards with real-time analytics on product usage, revenue impact, and customer churn.
  • Running predictive models to assess the potential success of new product features or pricing strategies.
  • A/B testing automation to evaluate feature effectiveness.
7. Risk Mitigation
  • Monitoring project timelines and budgets with proactive alerts for delays or overspending.
  • Conducting competitor risk assessments through AI-driven competitive analysis.
  • Identifying technical debt risks through codebase analysis.

How does Bob use AI Agents?

A Lead Designer could use Service Bricks AI Agents to enhance creativity, streamline workflows, and ensure user experiences are engaging, intuitive, and aligned with client expectations. Here’s how AI agents could support their core responsibilities:

designer in studio
1. User Research and Insights
  • Aggregating user behavior data from analytics tools to identify pain points and usability trends.
  • Conducting sentiment analysis on user feedback from surveys, social media, and support tickets.
  • Generating personas and user journey maps based on demographic and behavioral data.
2. Ideation and Creative Concept Development
  • Suggesting design inspirations by analyzing current trends and competitor designs.
  • Generating wireframe or mockup ideas using natural language input (e.g., “Create a minimalist home screen for a fitness app”).
  • Offering alternative visual styles or layouts for consideration.
3. Prototyping and Iterative Design
  • Automating the creation of interactive prototypes based on static wireframes.
  • Running simulations to predict user flows and identify potential bottlenecks.
  • Recommending layout optimizations based on usability heuristics or accessibility standards.
4. User Testing and Feedback Integration
  • Analyzing heatmaps, clickstreams, and session recordings to uncover areas of confusion or drop-off.
  • Summarizing user testing sessions and prioritizing actionable insights.
  • Creating reports on the effectiveness of specific design elements (e.g., button placement, typography readability).
5. Collaboration with Cross-Functional Teams
  • Translating design concepts into development-ready assets and specifications automatically.
  • Documenting and sharing design rationale with stakeholders in a clear, concise format.
  • Syncing design updates across tools to ensure consistency with product management and development teams.
6. Accessibility and Usability Optimisation
  • Scanning designs for accessibility compliance (e.g., color contrast, font sizes).
  • Simulating how designs appear to users with visual impairments or other challenges.
  • Providing recommendations to improve readability, navigation, and interactivity.
7. Design System Management
  • Automatically updating design systems with new components and ensuring consistency across projects.
  • Suggesting reusable components for new designs to maintain brand cohesion.
  • Identifying outdated or redundant design elements.
8. Measuring Impact of Design
  • Tracking KPIs like user engagement, task completion rates, and satisfaction scores post-launch.
  • Correlating design changes with performance metrics to evaluate impact.
  • Generating reports to communicate the value of UX design to stakeholders.
young female call centre operative wearing a telephone headset

How does Cathy use AI Agents?

A Data Analyst could leverage Service Bricks AI Agents to streamline data gathering, analysis, and reporting processes, enabling them to identify trends, generate actionable insights, and support strategic decision-making. Here’s how these AI Agents can assist:

1. Data Collection and Integration
  • Automating data collection from multiple sources such as CRMs, marketing platforms, website analytics, and databases.
  • Cleaning and preprocessing raw data (e.g., deduplication, handling missing values, normalization).
  • Integrating data from disparate systems to create unified datasets for analysis.
2. Exploratory Data Analysis (EDA)
  • Automatically generating summaries, visualizations, and statistical insights (e.g., identifying correlations, anomalies, or distribution patterns).
  • Highlighting emerging trends, such as spikes in product demand or shifts in customer behavior.
  • Providing suggestions for deeper drill-downs based on initial findings.
3. Trend and Predictive Analysis
  • Running predictive models to forecast market trends, sales figures, or customer churn using machine learning algorithms.
  • Analyzing historical data to identify recurring patterns and future opportunities.
  • Generating “what-if” scenario simulations for strategic planning (e.g., impact of a price change).
4. Automation of Reporting and Dashboards
  • Generating real-time dashboards with key performance indicators (KPIs), such as sales performance, customer retention, and ROI.
  • Automating periodic reports (daily/weekly/monthly) for stakeholders with clear visualizations.
  • Providing alerts for unusual patterns, such as sudden drops in engagement or spikes in expenses.
5. Identifying Business Opportunities and Risks
  • Detecting market gaps, such as underserved segments or rising product categories.
  • Analyzing customer segmentation data to identify new target groups or opportunities for personalization.
  • Highlighting risks like declining product adoption, customer churn, or inefficiencies in operations.
6. Collaboration and Communication
  • Summarizing complex analyses into simple, digestible reports for non-technical stakeholders.
  • Translating data insights into actionable recommendations for marketing, product, or sales teams.
  • Creating visual presentations of trends and predictions to share with executives.
7. Performance and KPI Monitoring
  • Continuously monitoring KPIs such as revenue growth, conversion rates, and customer acquisition costs.
  • Providing anomaly detection alerts for unexpected changes in performance metrics.
  • Recommending strategies to address underperforming areas (e.g., low customer satisfaction scores).
8. Advanced Analytics for Competitive Analysis
  • Collecting and analyzing competitor data (e.g., pricing trends, product launches, customer sentiment).
  • Providing benchmarking insights to compare company performance with industry standards.
  • Identifying opportunities to differentiate offerings in the market.

How does David use AI Agents?

A Marketing Specialist could utilize Service Bricks AI Agents to enhance campaign execution, audience targeting, and performance monitoring, ensuring product innovations connect effectively with the right audiences. Here’s how these AI agents can support this role:

bearded man wearing glasses sitting at a desk writing with his left hand under a lamp
1. Audience Research and Segmentation
  • Aggregating customer data from multiple platforms (social media, CRM, website analytics) to identify audience demographics, behaviors, and preferences.
  • Segmenting audiences into groups based on purchase behavior, engagement levels, and geographic locations.
  • Recommending new audience segments for product innovations based on emerging market trends.
2. Campaign Planning and Optimisation
  • Generating campaign ideas by analyzing competitor campaigns and audience preferences.
  • Predicting optimal posting times and content types for higher engagement.
  • Recommending the ideal mix of channels (social media, email, PPC) based on past campaign success.
3. Content Personalisation
  • Automating content creation for ads, emails, and social media posts tailored to different audience segments.
  • Dynamically adapting website or email content based on user behavior (e.g., showing specific product recommendations).
  • Recommending tone, style, and messaging for different audiences.
4. Ad Campaign Management
  • Automatically adjusting ad bids in real-time to maximize ROI.
  • Suggesting keywords, ad copy, and creatives for PPC campaigns based on market trends.
  • Running A/B tests to determine the most effective ad variations.
5. Social Media Strategy and Execution
  • Monitoring social media trends to suggest relevant content ideas.
  • Automating post scheduling and audience interaction (e.g., responding to comments, liking posts).
  • Measuring the performance of social media campaigns and providing actionable recommendations.
6. Email Marketing Enhancement
  • Automating email drip campaigns triggered by user actions (e.g., abandoned carts, product views).
  • Personalizing email content, subject lines, and recommendations for each recipient.
  • Analyzing open rates, click-through rates (CTR), and conversions to optimize future emails.
7. Real-Time Campaign Monitoring
  • Tracking campaign KPIs like CTR, conversion rates, and revenue in real-time.
  • Sending alerts for anomalies (e.g., sudden dips in performance or high bounce rates).
  • Suggesting corrective actions for underperforming campaigns.
8. Predictive Analytics and Strategy
  • Forecasting customer behavior trends (e.g., purchase likelihood, seasonal demand).
  • Recommending strategies for launching product innovations based on historical data.
  • Simulating “what-if” scenarios to test different strategies before execution.
9. Competitive Analysis
  • Tracking competitors’ marketing campaigns, keywords, and ad placements.
  • Analyzing competitor pricing, audience targeting, and messaging strategies.
  • Identifying gaps or opportunities in the market for differentiation.
10. Reporting and Stakeholder Communication
  • Automating the creation of reports with campaign performance metrics.
  • Visualizing data in dashboards for easy stakeholder understanding.
  • Summarizing insights and providing recommendations for future campaigns.
african american woman sitting at her computer with multiple monitors displaying data and graphs

How does Eva use AI Agents?

A Technical Lead can use Service Bricks AI Agents to ensure secure, reliable infrastructure and efficient technical development processes. These AI tools enhance monitoring, troubleshooting, and decision-making while reducing manual efforts. Here’s how they can be leveraged:

1. Infrastructure Monitoring and Optimisation
  • Real-time infrastructure monitoring to detect performance issues (e.g., server downtime, latency spikes).
  • Predicting future infrastructure needs based on usage patterns and scaling automatically.
  • Optimizing cloud resource allocation to reduce costs while maintaining performance.
2. Security Management
  • Continuously scanning systems for vulnerabilities or misconfigurations.
  • Detecting suspicious activity or potential breaches using behavior-based anomaly detection.
  • Automating compliance audits and generating reports for regulatory standards like GDPR or HIPAA.
3. Data Integrity and Management
  • Monitoring data pipelines to ensure timely and accurate data processing.
  • Automatically detecting and correcting data quality issues such as duplicates or missing values.
  • Ensuring secure data storage and encryption while managing access controls.
4. Incident Detection and Resolution
  • Analyzing logs to identify root causes of issues and providing actionable insights.
  • Recommending or executing automated fixes for common incidents (e.g., restarting services, applying patches).
  • Alerting teams in real time and prioritizing incidents based on severity.
5. Development Workflow Automation
  • Suggesting code optimizations or detecting potential bugs during development.
  • Automating CI/CD pipelines for seamless deployment and rollback if issues are detected.
  • Recommending best practices for version control and code documentation.
6. Team Collaboration and Productivity
  • Summarizing project updates, highlighting blockers, and suggesting next steps for sprint planning.
  • Integrating with task management tools to automate assignments and track progress.
  • Providing insights into team workload distribution to prevent burnout.
7. Disaster Recovery and Backup Management
  • Continuously monitoring backup systems to ensure they’re operational and up-to-date.
  • Simulating disaster recovery scenarios to test readiness.
  • Automatically restoring systems in case of failures, minimizing downtime.
8. Scaling and Performance Tuning
  • Identifying bottlenecks in system performance and recommending optimizations.
  • Simulating different traffic scenarios to test system scalability.
  • Dynamically scaling resources during peak demand periods without manual intervention.
9. Vendor and Tool Integration Management
  • Monitoring third-party APIs for availability and performance issues.
  • Managing tool integrations to prevent conflicts or redundancy.
  • Recommending new tools or services based on evolving team needs.
10. Reporting and Strategic Insights
  • Generating detailed infrastructure performance reports for stakeholders.
  • Providing insights into trends such as increased traffic, higher resource usage, or emerging threats.
  • Recommending long-term strategies for infrastructure scaling and security improvements.

How does Frank use AI Agents?

A UX Researcher can leverage Service Bricks AI Agents to streamline user research processes, analyze user behaviour, and generate actionable insights. This ensures the company’s products are continuously refined to enhance customer satisfaction. Here’s how AI agents can support their role:

3 service designers standing in front of a working wall under 2 lamps
1. Automating User Data Collection
  • Aggregating data from multiple platforms (user surveys, app analytics, website heatmaps, and social media feedback).
  • Automating distribution and collection of surveys tailored to user segments.
  • Tracking and capturing real-time user interactions via tools like clickstream analysis or session recordings.
2. User Behaviour Analysis
  • Identifying patterns in user behavior, such as drop-off points, navigation challenges, and feature usage trends.
  • Analyzing session recordings to pinpoint areas of friction in user journeys.
  • Generating heatmaps to highlight frequently clicked or ignored areas of an interface.
3. Sentiment Analysis and Qualitative Feedback
  • Analyzing open-ended survey responses, reviews, and support tickets to extract themes and sentiments.
  • Identifying recurring pain points or positive experiences expressed by users.
  • Summarizing qualitative feedback into actionable insights.
4. Conducting User Interviews and Focus Groups
  • Assisting with interview scheduling and creating question templates based on research goals.
  • Transcribing interviews in real-time and summarizing key findings.
  • Detecting patterns or common themes across multiple user interviews.
5. Persona Development and User Journey Mapping
  • Generating user personas by analyzing demographic, behavioral, and psychographic data.
  • Automating the creation of user journey maps based on collected interaction and feedback data.
  • Updating personas and journeys dynamically as new data becomes available.
6. Usability Testing
  • Automating usability test setups, including participant selection and task scenario generation.
  • Analyzing usability test recordings and identifying bottlenecks or task failures.
  • Generating reports with performance metrics such as time-on-task, success rates, and error patterns.
7. A/B Testing and Experimentation
  • Automating A/B test creation and result analysis.
  • Recommending design variations based on historical test data and user behavior patterns.
  • Identifying statistically significant trends in user preferences or interactions.
8. Accessibility Audits
  • Scanning designs and products for accessibility compliance (e.g., WCAG standards).
  • Highlighting areas that don’t meet color contrast, font size, or navigation accessibility requirements.
  • Simulating experiences for users with visual or motor impairments to identify usability barriers.
9. Reporting and Sharing Insights
  • Generating automated reports summarizing research findings, usability metrics, and user feedback.
  • Visualizing insights with dashboards that communicate key themes and trends.
  • Creating shareable research briefs for stakeholders to align design and development teams.
10. Predicting Future User Needs
  • Using historical user behaviour data to forecast future preferences and pain points.
  • Identifying emerging trends in user needs before they become widespread.
  • Recommending features or experiences that align with predicted user behaviours.