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Worksheets1 Introduction & 2.1 Artificial Intelligence
Total questions: 120
Worksheet time: 3600secs
Which statement best defines Artificial Intelligence in a business context?
Automated scripts that follow fixed rule-based workflows
Cloud services that virtualize on-premises infrastructure
Large databases supporting routine transactional reporting
Computer systems performing tasks needing human cognition
Which is a primary managerial benefit sought from integrating AI into product management?
Cost reduction and customer satisfaction improvements
Eliminating the need for stakeholder engagement
Hiring freezes and reduced headcount in all teams
Outsourcing core competencies to third parties
Which item is an implementation challenge frequently reported for AI in organizations?
Immediate return on minimal capital investment
High effort to build suitable data infrastructure
Universal availability of clean labeled datasets
Simple deployment comparable to basic web tools
What shift in managerial practice does AI integration most demand?
Greater cross-functional collaboration and governance
Strictly linear product development processes
More siloed decision-making within functional units
Reduced emphasis on data and analytics capabilities
In research on AI and product development, what gap does the article highlight?
Limited study on how AI alters core managerial practices
Comprehensive mapping of all existing applications
Excess focus on lifecycle management methodologies
Overabundance of unified AI-driven frameworks
Which pair correctly contrasts tangible and intangible AI benefits mentioned for managers?
Server virtualization vs. higher GPU clock speeds
Cost reduction vs. cultural shifts in innovation climate
Zero maintenance needs vs. permanent data accuracy
Immediate profits vs. guaranteed market monopolies
According to cited interview evidence, what proportion of firms reported low effort for establishing AI analytics infrastructure?
About 37 percent reported relatively low or very low effort
About 9 percent reported relatively low or very low effort
About 14 percent reported relatively low or very low effort
About 50 percent reported relatively low or very low effort
Which research question aligns with building a shared approach to AI in product management?
How can marketing budgets be doubled next quarter?
What would it take to create a unified AI-driven framework?
Why should firms abandon existing governance models?
When will AI fully replace human product managers?
Which statement best describes the shift toward data-centric AI methods between 2010 and 2020?
Greater reliance on labeled data and compute power
Preference for expert rules and ontologies only
Reduction in dataset sizes and model capacity
Return to symbolic reasoning and logic trees
In New Product Development, which AI type primarily covers demand forecasting, risk assessment, and sentiment analysis?
Visual AI for computer vision and overlays
Functional AI for connected devices and robots
Analytic AI for data-driven business insights
Robotic AI for automated back-office tasks
A team wants to automate repetitive back-office workflows like invoice processing and approvals. Which AI type is the most appropriate primary category?
Robotic AI for process automation tasks
Interactive AI for conversational agents
Visual AI for image understanding only
Text AI for translation pipelines
Which pairing is MOST accurate for the listed AI type and example capability?
Visual AI — object detection and augmented reality
Functional AI — speech-to-text conversion only
Interactive AI — demand forecasting pipelines
Text AI — robot motion planning modules
A product manager must choose an approach for object detection in a manufacturing line. Which rationale best supports selecting deep learning over knowledge-based methods?
Deep learning leverages abundant data and modern compute
Rule-based systems learn features without examples
Knowledge bases scale better with unlabeled datasets
Symbolic methods outperform convolutional networks
Which statement best defines a product in a business context?
Only ideas and patents licensed to others
Only physical goods sold in retail stores
Only services delivered through digital channels
Anything offered to a market to satisfy wants
What is the primary focus of product management research highlighted in this section?
New Product Development activities and processes
Pricing optimization for existing portfolios
Supply chain logistics and sourcing
Retail merchandising and shelf placement
In the stage-gate model, what occurs at a gate between stages?
Marketing launches a pilot to customers
Budgets are automatically increased each phase
Teams brainstorm new features informally
A decision checks prior phase quality to proceed
Which pair correctly lists initial phases commonly found in NPD?
Idea generation and idea screening
Commercialization and mass production
Postlaunch review and product retirement
Beta testing and lifecycle extension
Why is the early NPD phase sometimes called the 'fuzzy front end'?
Activities are exploratory and can be flexible
Engineering specifications are fully fixed
Legal approvals are already completed
Market launch timing is strictly defined
Which phase is most consistently included across NPD frameworks to evaluate market and financial viability before full development?
Post-launch monitoring and control
Product design and engineering
Business analysis and viability assessment
Concept screening and prioritization
Several models show a gate before commercialization. What is the primary purpose of this gate in stage-gate NPD processes?
Approve resource reallocation to operations
Authorize market entry based on evidence
Finalize intellectual property negotiations
Set long-term portfolio diversification
A team has completed concept development and product testing. According to common NPD sequences, what is the next logical step?
Core technology research
Post-commerce optimization
Business model experimentation
Market launch or test marketing
Which activity best characterizes the design and creation phase in new product development?
Tracking post-launch performance adjustments
Evaluating initial idea feasibility studies
Conducting large-scale market introduction
Converting a concept into a working product
During the testing and validation phase, which item is typically assessed alongside the product?
Executive compensation structures
Facility real-estate valuation
Marketing program effectiveness
Long-term warranty accounting
What is the main purpose of sales trials conducted in the testing phase?
Develop corporate tax minimization plans
Negotiate supplier long-term contracts
Design multi-year capital investment maps
Check positioning, advertising, and price
Commercialization most commonly refers to which transition in the NPD process?
Running internal prototype evaluations
Completing exploratory concept sketches
Preparing departmental training sessions
Moving to full-scale production and sales
Which sequence correctly lists the classic four stages of the product life cycle?
Ideation, Analysis, Prototyping, Scaling
Design, Testing, Launch, Optimization
Introduction, Growth, Maturity, Decline
Awareness, Consideration, Purchase, Loyalty
In contemporary practice, which set of systems is often integrated into a PLM environment to manage product-related information flows?
CAD/CAM/CAE with ERP, CRM, SCM
BPM/HRIS with ATS, LMS, TMS
GIS/BI with MDM, EPM, RPA
EHR/PACS with LIS, RIS, VNA
A manager shortens the testing phase because development costs are high but confidence in product success is strong. Which risk management rationale best supports this decision?
Reduce time-to-market while accepting higher uncertainty
Maximize brand equity via extended pre-launch campaigns
Increase regulatory compliance through added documentation
Preserve cash by delaying supplier qualification programs
Two views of PLM are discussed: one as a broad business activity across the life cycle and another as primarily software tools. Which implication follows for cross-functional teams?
They can ignore data governance during product changes
They will phase out ERP and CRM to avoid redundancy
They should separate engineering from marketing permanently
They must align processes with both managerial and IT integrations
Which database was primarily used to retrieve articles for the literature review on AI in product development and management?
Scopus database
ABI/INFORM collection
IEEE Xplore portal
Web of Science platform
What tool was employed to map keyword co-occurrences and visualize links between terms during keyword selection?
Tableau analytics
Gephi platform
VOSViewer tool
CiteSpace software
What was RQ2 focused on in the study’s research design?
Measuring ROI from AI pilots in retail
Comparing supervised and unsupervised learning accuracy
Creating a common AI-driven product development and management framework
Identifying top AI vendors for manufacturing
How many experts were consulted to deepen and validate insights from the literature review?
Five experts total
Ten experts total
Three experts total
Seven experts total
Which pair best represents the two divisions used to organize AI usage in the review?
Ethics frameworks and governance policies
Hardware systems and cloud services
General AI subfields and specific AI methods
Algorithm theory and data engineering
Which of the following belongs to the general AI subfields used in the study’s categorization?
Natural Language Processing area
Supply chain analytics optimization
Edge computing infrastructures
Market mix modeling techniques
Which item is listed as a specific AI method or use case in the review’s categorization?
Sentiment analysis application
Agile sprint ceremonies
Corporate venture financing
Design thinking workshops
Which inclusion criterion was applied during article selection?
Only books and theses were included
Non-English studies prioritized for diversity
Focus on AI methods used in new product development
No time restriction on publication year
Which was an exclusion criterion during screening?
Papers with more than three authors
Studies only in medical imaging field
False positives where AI acronym meant something else
Articles with full open access only
What complementary search approach was used in addition to database querying to expand sources?
Random sampling of conference booths
Crowdsourcing through online forums
Cold-emailing authors for drafts
Backward and forward citation searching
Which business area is most directly supported by AI-enabled demand forecasting when planning operations after product launch?
Supply chain management activities and logistics
Intellectual property licensing portfolios
Corporate governance structures and bylaws
Employee wellness programs and perks
Long-term capital budgeting decisions
What is the primary purpose of forecasting market demand for a product in commercial phases?
To eliminate inventory across the entire network
To replace qualitative expert interviews entirely
To guide design, distribution, promotion, and pricing
To maximize automation in every subfield of AI
To avoid conducting any market research at all
A firm is launching a new product and also reselling remanufactured units. Which forecasting focus best supports decisions across both offerings?
Eliminating promotions to stabilize baseline demand
Tracking only historical sales of the flagship model
Integrating new product and remanufactured demand signals
Prioritizing sentiment analysis over any numeric models
Ignoring market factors beyond internal operations
In which industries has AI-enabled demand forecasting been described as applicable?
Classical music and fine arts markets
Astronomical imaging and space travel
Primary education and childcare
Mining, heavy steel, and shipbuilding
Fashion, food, and beauty sectors
Which AI method is primarily used to assess the positivity and negativity of product reviews during idea generation?
Computer vision for image data
Conversational AI for user interviews
Sentiment analysis of textual feedback
Knowledge extraction from online sources
During concept testing and evaluation, which technology best supports extracting product-related facts from online sources?
Computer vision pipelines
Knowledge extraction techniques
Recommender systems
Anomaly detection models
A team wants to evaluate how a proposed product design was received across social platforms. Which AI approach should they prioritize?
Anomaly detection in sensors
Demand forecasting for sales
Computer vision for shelf scans
Sentiment analysis of user posts
Which AI technology most directly enables expert-like guidance for product customization during product design and development?
Knowledge-based systems
Social media customer profiling
Recommender systems
Anomaly detection
In product launch and management, what is the main aim of demand forecasting models?
Summarize customer support chats
Detect defects in production
Extract competitor product features
Predict sales and inventory needs
Which term best describes using algorithms to determine attitudes in text data from sources like reviews and social media?
Sentiment analysis of user text data
Supply chain risk optimization
Market basket affinity modeling
Predictive maintenance scheduling
What is a common data source for sentiment analysis in product management?
Social media posts and reviews
Patent filings by competitors
Internal ERP inventory logs
Point-of-sale receipt totals
Why do advanced sentiment systems introduce a fuzzy scale rather than only positive or negative labels?
To improve data encryption and privacy
To capture nuanced polarity and intensity
To eliminate topic modeling requirements
To reduce the need for labeled datasets
During the idea generation phase, how are sentiment analysis and topic modeling typically combined?
Sentiment assigns segments, topics predict churn
Topic modeling finds themes, sentiment gauges attitudes
Sentiment estimates demand, topics set prices
Topic modeling rates satisfaction, sentiment finds topics
A product manager wants to explore opinions on a proposed feature using external communities. Which approach aligns with this goal?
Running A/B tests on released versions
Calculating reorder points for components
Conducting crowdsourcing to gather viewpoints
Auditing financial statements for costs
User-generated content grows rapidly for a new device category. What strategic action does sentiment analysis enable for designers?
Forecast the product’s viral spread dynamics
Optimize supplier lead-time variability
Identify opinions of current or future users
Automate warranty claims adjudication
You must prioritize which product aspects to emphasize at launch. Which analytic pairing provides both themes and satisfaction signals?
Clustering with anomaly detection
Recommendation with pricing elasticity
Topic modeling with sentiment analysis
Churn modeling with forecasting
Which best describes knowledge extraction in product development?
NLP method to pull structured facts from sources
Visual tool to detect defects on production lines
Hardware process to automate assembly operations
Financial model to price new market entries
What is a primary use of knowledge extraction during idea generation?
Estimating quarterly sales using regression forecasts
Scheduling factory shifts with optimization algorithms
Outsourcing marketing to external agencies
Identifying innovative product ideas from diverse inputs
Which source is most aligned with knowledge extraction for product insights?
Patent databases and crowdsourcing communities
Executive calendars and board meeting notes
Warehouse inventory and shipment manifests
Raw sensor voltages from industrial robots
In later stages of product management, knowledge extraction can support which activity?
Facility lease negotiations with landlords
Carbon accounting for upstream emissions
Payroll tax reconciliation and audits
Competitor analysis and customer churn detection
What is a common application of conversational AI in management research?
Using chatbots to evaluate customer experience
Mining satellite images for weather impacts
Deploying drones to inspect construction sites
Training employees on forklift certification
A risk when deploying sales chatbots is that disclosure of the bot at the start may
Overfit product recommendations instantly
Violate labor laws in multiple regions
Lower sales due to perceived incompetence
Increase costs due to licensing fees
How are expert systems positioned within conversational AI for product development?
As cloud brokers that switch infrastructure providers
As encryption layers for secure payment gateways
As motion controllers for robotic manipulators
As knowledge-based tools for design and customization
Which task is most directly associated with image recognition in manufacturing quality control?
Classifying surface defects on parts
Scheduling labor shifts for operators
Simulating market demand scenarios
Aggregating quarterly sales reports
What is the primary purpose of object tracking on a production line?
Recommending products to online users
Encrypting design files for security
Summarizing financial statements monthly
Following items to detect process deviations
In product development, how is computer vision commonly used beyond defect detection?
Drafting intellectual property filings
Analyzing customer engagement from images and video
Setting executive compensation benchmarks
Negotiating supplier contracts automatically
Which statement best differentiates autonomous systems from simple automation in manufacturing?
They replace marketing teams for promotions
They only execute preprogrammed fixed sequences
They combine perception and decision-making to act
They require no sensors for environment input
A factory pairs robots with computer vision and expert systems. What capability does this integration most improve?
Expanding warehouse floor square footage
Creating brand slogans for campaigns
Reducing corporate tax liabilities annually
Handling complex tasks requiring planning
Which business benefit is a realistic outcome of using computer vision for on‑site part identification in remanufacturing?
Increased manual paperwork processing
Lower time to locate correct spare parts
Higher advertising click‑through rates
Longer machine setup times per batch
A retailer installs in‑store cameras to profile customer segments and adjust product placement daily. Which underlying technology enables this practice most directly?
RFID for pallet inventory counting
Edge caching for website acceleration
Blockchain for immutable purchase ledgers
Computer vision for human behavior analysis
Which statement best describes anomaly detection in product management contexts?
It ranks items by popularity for faster browsing
It finds typical usage clusters in transaction logs
It forecasts quarterly sales using time series models
It flags unusual patterns in text or process logs
A practical use of anomaly detection for marketing segmentation is to identify which group?
Price-sensitive users reacting to discounts
New users with incomplete registration forms
Users who deviate from target customer profiles
Frequent buyers matching target personas
What core business purpose do recommendation systems serve in e-commerce?
Replacing manual quality inspections entirely
Reducing production costs through automation
Suggesting relevant items to reduce information overload
Detecting fraudulent orders across regions
Which challenge is commonly associated with recommendation systems at launch?
Overfitting from excessive training examples
Cold-start from limited user or item data
Inventory shrinkage due to stockouts
Cannibalization from excessive price cuts
From a business perspective, what unintended effect can highly optimized recommenders have?
Increasing warehouse handling time significantly
Raising advertising costs for seasonal items
Decreasing sales diversity across product lines
Reducing return rates for fragile products
Which combined approach can help mitigate sales diversity loss and cold-start limitations?
Blending price optimization with AB testing only
Merging social network signals with recommenders
Switching exclusively to content-based filtering
Removing user personalization from rankings
In selecting reviews to inform product decisions, why pair recommendation with sentiment analysis?
To surface the most valuable opinions efficiently
To guarantee unbiased data without noise
To replace human judgment in final approvals
To eliminate the need for anomaly detection entirely
Which term best describes using data and algorithms to forecast future outcomes for product decisions?
Predictive analytics for forecasting outcomes
Diagnostic analytics for root causes
Prescriptive analytics for chosen actions
Descriptive analytics for past summaries
In early idea generation, which ML-enabled activity helps synthesize large volumes of user-generated content into themes?
Topic modeling for text mining themes
A/B testing for interface changes
Robotic process automation
Heuristic scoring by managers
Which example aligns most with AI optimization during product design?
Tuning design parameters with algorithms
Collecting raw customer interviews
Archiving historical design files
Scheduling weekly stand-up meetings
A retailer wants to set prices that adjust to demand while learning over time. Which approach fits best?
Reinforcement learning for dynamic pricing
Random price rotation each day
Static markup based on cost-plus
Manual manager overrides weekly
Which task is most suitable for computer vision in product quality testing?
Estimating annual sales trends
Prioritizing backlog user stories
Detecting surface defects on items
Segmenting customers by value
A manager asks not just what will happen but what action to take to remove a risk in a product launch. Which analytics type is required?
Descriptive analytics for dashboards
Predictive analytics for likely events
Prescriptive analytics for recommended actions
Diagnostic analytics for variance
You have thousands of online reviews and need to inform product redesign. Which combined ML workflow adds most value end-to-end?
Counting star ratings only
Translating all reviews to one language
Manual reading by two interns
Sentiment analysis plus topic extraction
Which AI application was frequently cited as essential for optimizing inventory and supply chain processes?
Edge computing for device orchestration
AI-based forecasting and demand prediction
Automated customer service routing systems
Rule-based anomaly detection for billing
Experts argued that AI’s biggest breakthrough comes primarily from which approach?
Focusing solely on post-launch A/B testing
Outsourcing all analytics to third-party vendors
Investing only in state-of-the-art algorithms
Integrating point solutions into end-to-end systems
What organizational factor did experts highlight as critical before launching AI initiatives?
Evaluating maturity and readiness across technology and culture
Hiring a celebrity brand ambassador for visibility
Migrating all operations to a single cloud provider
Eliminating human review from model decisions
Which statement best captures the role of data governance mentioned by experts?
Data governance matters only for regulated industries
Systematic data accuracy is vital for actionable insights
Model complexity can compensate for messy datasets
Occasional audits are enough for reliable model outputs
Why did experts stress human-centered design in AI tools for product management?
It eliminates the need for usability testing entirely
It ensures compliance with every international standard
It primarily reduces cloud computing costs for deployments
Understanding user psychology and comfort with delegation matters
What future research direction did experts identify regarding generative AI in product innovation?
Empirical studies to assess disruptive versus incremental impact
Immediate replacement of traditional prototyping workflows
Restriction to marketing content generation only
Mandatory adoption across all manufacturing processes
According to experts, how should organizations realize AI’s value across the product lifecycle?
Centralize decisions solely within the IT function
Prioritize short-term pilots without change management
Deploy isolated tools for each department independently
Integrate applications across phases with cross-functional collaboration
Which issue most commonly complicates phase-specific searches about AI in product development?
Excessive attention to post-launch analytics
Overly narrow industry focus across studies
Lack of quantitative methods in every study
Cross-sectional overlap of AI methods across phases
What does the term AI-enabled products primarily refer to in this context?
Products that manage development teams automatically
Products that exclude human-centered design principles
Products whose features are powered by AI capabilities
Products built only by autonomous robots
Which research gap highlights the problem of insights remaining siloed instead of informing later decisions?
Insufficient focus on customer lifetime value
Overuse of radical innovation frameworks
Shortage of cloud computing resources
Fragmented application of AI across phases
Which combination is cited as underexplored yet potentially valuable across product phases?
Integrating conversational AI into concept testing
Blending AR with corporate taxation models
Merging RPA with supply chain finance
Combining IoT sensors with HR performance reviews
Why can reliance on AI-driven ideation bias outcomes toward incremental rather than radical innovation?
AI models must avoid user feedback by design
AI models cannot be updated after deployment
AI models require legally patented datasets only
AI models are trained on existing historical datasets
Which challenge stems from terminology ambiguities during literature searches on AI for product management?
Exclusion of all management case studies
Removal of qualitative interview evidence
Duplication of every experimental dataset
Inclusion of non-relevant business analysis works
Which statement best captures a needed future direction for AI’s role in product development?
Replace all human decisions with automation
Limit AI to early-stage ideation support
Treat AI as an integrative intelligence layer
Deploy AI as isolated point tools per phase
Which factor is essential when tailoring an AI-driven product development framework to an organization?
Brand colors and slogans
Number of social media posts
Office location and layout
Product type and hybrid nature
What is the main difference between perpendicular and parallel mapping approaches for integrating AI?
Perpendicular uses cloud tools; parallel uses on-premise tools
Perpendicular is only for startups; parallel only for enterprises
Perpendicular focuses on one team; parallel covers all departments
Perpendicular integrates across the organization; parallel adapts within similar processes
Why must organizations define KPIs when implementing AI in product development?
To measure success and track performance
To comply with trademark regulations
To avoid hiring data scientists
To reduce the number of meetings
How do industry and organizational size influence an AI-driven framework?
They rarely impact processes or outcomes
They only affect server hardware selection
They mainly determine logo design choices
They change requirements and constraints across contexts
Which planning step best supports strategic alignment for AI initiatives?
Skipping pilots to launch faster
Buying the newest machine learning platform
Clarifying objectives, methods, resources, and maturity
Outsourcing all analytics immediately
Which statement best defines a research gap in applying AI to product development?
A field with saturated literature and consensus
An area with limited studies and unclear best practices
A topic with extensive trials and clear procedures
A well-established method with standard benchmarks
In early product phases, which AI method most directly supports understanding customer opinions?
Recommendation systems for cross-selling items
Autonomous systems for factory robotics
Vision AI for object recognition tasks
Sentiment analysis of customer feedback data
A firm has strong research on AI for product launch but little on concept testing. Where should it prioritize closing gaps?
Concept testing and evaluation activities
Post-launch product management
Autonomous systems integration
Vision AI for packaging design
Which pairing aligns an AI method with a plausible product development phase focus?
Autonomous systems with market sizing analysis
Demand forecasting with product launch planning
Vision AI with pricing strategy selection
Sentiment analysis with factory line balancing
You are planning a research agenda to address underexplored AI uses across phases. Which approach shows strategic prioritization?
Study every phase equally without considering gaps
Pick trending models first, then choose any phase
Fund only vision models, regardless of phase fit
Map methods to phases, score gaps, then allocate studies
Which phrase best describes the current pattern of AI adoption in product management across phases?
Integrated and continuous across the lifecycle
Fragmented and siloed by specific phases
Outsourced entirely to external AI vendors
Centralized under a single AI governance unit
What theoretical need arises due to phase-specific AI implementations?
Fewer studies on AI governance
Holistic models integrating AI across phases
Replacing human roles with automation
More detailed phase metrics only
Data-driven biases in AI analytics most likely lead to which outcome?
Immediate disruption of consumer habits
Elimination of historical data reliance
Perpetuation of existing market structures
Faster radical innovation cycles
Which managerial action is emphasized to move beyond isolated AI pilots?
Define clear objectives and scaling roadmaps
Acquire the latest AI hardware only
Focus solely on demand forecasting accuracy
Reduce cross-functional collaboration entirely
Which capability mix is considered essential for successful AI integration in teams?
Only product domain knowledge is sufficient
Only data science expertise is sufficient
Neither technical nor managerial skills
Both technical and managerial capabilities
Which measurement approach better captures AI’s value across the product lifecycle?
Single metric focused on accuracy only
Multi-faceted indicators covering broader outcomes
Ad-hoc anecdotal success stories
Vendor-reported benchmark scores
Which limitation of current research is explicitly highlighted?
Excessive focus on economic impacts
Universal agreement on AI definitions
Overabundance of ethical analyses
Subjective categorization and terminology variance
Which future research avenue is proposed to address under-researched phases?
Focusing only on post-launch analytics
Eliminating anomaly detection from design
Exploring conversational AI in concept testing
Replicating existing commercialization studies
What combined research design is suggested to understand AI’s evolving role in teams over time?
Laboratory simulations only
Longitudinal mixed-method studies
Randomized clinical trials only
Cross-sectional surveys only
Which strategic combination illustrates a more integrated AI approach across phases?
Chatbots, sentiment analysis, and recommendations
Recommendation system without upstream data
Sentiment analysis used without follow-up
Chatbots for interviews used alone
Which statement best captures the study's main conclusion about AI in product development and management?
AI is mature and fully cohesive in current literature
AI requires systematic integration across methods and phases
AI offers minimal impact on product lifecycle activities
AI should replace human roles across most product tasks
What is identified as a key opportunity of AI within New Product Development (NPD)?
Limiting innovation to incremental feature refinement
Reducing the need for ethical design considerations
Automating product management tasks for efficient outcomes
Eliminating cross-disciplinary collaboration requirements
A firm wants to leverage generative AI while avoiding unintended consequences. Which approach best aligns with the study's recommendations?
Prioritize rapid deployment without governance structures
Replace longitudinal analysis with short pilot projects
Adopt ethical guidelines and cross-disciplinary research plans
Center strategy on automation while ignoring user impacts
