Unlocking Human Potential
AI for all
Take our free AI maturity assessment to find out where you land. Get personalized results and industry-specific use cases to help guide you toward advancing your organization’s effectiveness, improving customer satisfaction, and achieving your AI goals.
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Which best describes the industry your organization is in?
Question 1 of 6
Financial Services
Healthcare and Life Sciences
Manufacturing
Media and Communications
Natural Resources
Public Sector
Retail
Technology
Other
Next
How well can the business value of AI use cases be quantified for your organization?
Question 2 of 6
No measurement of ROI or value-add of AI projects is done and there is little awareness of the value of AI
Some development to quantify the business case for AI use-case, but the business is still prone to making costly decisions that could be avoided with a more thorough business case analysis
Business case analysis for planning and decision support is still largely ad-hoc and ROI models are not standardized or reusable
Internal standards exist for business case creation and ROI models are built keeping reusability in mind
Entire organization consistently adheres to a standardized process to monitor KPIs and value captured from AI
How do your data scientists approach reframing a business problem in an AI problem?
Question 3 of 6
Data scientists take requirements from business users without thorough questioning
Rudimentary requirements process with a heavy reliance on business and data SMEs to reframe the business question into available inputs and desired outputs
Data scientists tend to have specific domain expertise and follow an existing process to reframe business requirements
Data scientists work across multiple domains with guidelines for how to ask the right questions to translate the problem; May still work primarily in one domain and/or work closely with advocates in the business to frame business needs
Data scientists dissect a problem into its core parts and work across domains by asking the right answers to gain enough domain-specific context in order to frame the problem
Which of the following best describes how data science teams are structured within your organization?
Question 4 of 6
A single data team or individual data scientists exists in the organization
Small teams with internal experts in data science exist across different business units, but they typically work in siloes
Organizational structures like Center of Excellence (CoE) have been created, but teams are still working on formalizing responsibilities across different technology and business units
Organizational structures like CoE are formalized, and their mandates include managing the organization’s relationship with the broader AI ecosystem, such as through vendor and partnership management
Center of Excellence (CoE) for multiple data science teams penetrate the entire organization, encouraging communication, shared knowledge, and partnership across lines of business
Which of the following best describes how models are deployed to production?
Question 5 of 6
Models are not deployed anywhere
There are models in production, but there is no standard process for deployment; many parts of the business still struggle to deploy models
There is a standard deployment process that can be leveraged, but some parts of the business still rely on other methods
There is a standard deployment process that is used across multiple lines of business
There is an MLOps process tied directly into model development that is used across the business
Which of the following best describes your organization's approach to resolving bias?
Question 6 of 6
No clear steps or processes to resolve biases
Reactive, ad hoc steps to resolve biases in pockets of the organization on an as needed basis
Clear processes to resolve biases identified in pockets of the organization with external parties consulted to combat bias in some stages of the delivery lifecycle
Standardized and centrally-governed processes to resolve biases with external parties consulted to combat bias in all stages of the delivery lifecycle
Standardized and centrally-governed processes to resolve biases reviewed with 3rd party SME’s with external parties consulted to combat bias in all stages of the delivery lifecycle and with active contribution to open-source forums on ways to combat bias
results
The underwriter workflow for a large reinsurance provider involves tremendous manual effort of sifting through thousands of unstructured claim documents. Slalom optimized their workflow by implementing a cloud-based data solution, together with domain-specific natural language processing models, to save thousands of hours of manual work.
Reimagining reinsurance with pricing process automation
success story
Intelligent Customer Support
Security and Fraud Prevention using AI
Trading Bots Predict Higher Returns
Here are some ways organizations in the financial services industry are getting started with AI
AI-powered Chatbots are being used to interact with customers and answer queries resulting in massive potential costs reductions in front office and helpline staffing.
AI is being used to aid in fraud prevention by leveraging anomaly detection and clustering algorithms to spot fraudulent activity.
Trading novices and professional firms alike are relying on AI trading bots to help predict the best time to buy and sell stocks to maximize their returns.
AI for Efficient Debt Collection
AI Automated Procure-to-Pay Process
Account Reconciliation in Commercial Banking
Here are some ways organizations in the financial services industry are mobilizing their AI initiatives.
Banks are using AI to optimize the debt collection workflow by providing a compliant and efficient process which reduces delinquency rates.
Financial companies are using AI-based invoice capture technologies to automate their invoice systems and use accessible billing services that remind their customers to pay.
Companies are leveraging Optical Character Recognition (OCR) and Natural Language Processing (NLP) to extract unstructured data from bank statements and compare them to glean insight and find patterns for anomaly detection.
Advanced NLP For Regulatory Compliance
Deep Learning for Expense Management
Recommendation Engines for Stock Trading
Here are some ways organizations in the financial services industry are innovating with AI.
Companies are leveraging Natural Language Processing (NLP) and Optical Character Recognition (OCR) technologies to scan legal and regulatory documents for compliance issues.
Finance companies are using advanced deep learning algorithms along with document capture technologies to prevent non-compliant spending and reduce approval workflows.
AI is being used to discover a broader range of trading opportunities undetectable by humans to empower traders to generate record-high returns.
Maturity Level:
Not Started – Exploring
Organizations at this maturity level have varying levels of excitement about AI.
They want to get started, or continue to lay foundations, but efforts have been siloed and inconsistent to date.
Quick wins can help to gain traction.
Mobilized
Organizations at this maturity level have a strategic vision for AI that is aligned with other organizational roadmaps.
They are streamlining development tools and resources but often need help scaling capabilities and impact.
Robust – Pioneering
Organizations at this maturity level of a depth of AI capabilities being leveraged to see, understand, and act on factors sharping the business.
They are leading the way and shaping how organizations could and should use the power of AI to unlock human potential.
Want to learn more?
Visit our Financial Services page for more ideas and stories of success from organizations in your industry.
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Providing patients with safe and trustworthy medicine is a top priority for our health IT client and having fast and efficient testing methods alleviates the burden of delivering on this task. Slalom helped our client develop a mobile app that makes use of computer vision and machine learning to enable rapid, automated analysis of medical test strips—even in remote locations.
Detecting fraudulent medication with machine learning
Here are some ways organizations in the healthcare and life sciences industry are getting started with AI.
Mental Illness Predictions and Prevention
AI Powered Coordination and Treatment
Wearable Technology Improves Patient Health
AI is being used in detecting potential mental health issues resulting in data-driven recommendations for patients and psychiatrists.
AI chatbots are being integrated into patient portals to screen for symptoms and efficiently book appointments.
AI is being used to assess patient data from wearables and smartphone apps to help improve adherence to health management protocols, build trust and improve overall patient health.
AI-Driven Disease Management Research
Explainable AI for Patient Trust
AI-Optimized Medical Trials
AI is being used to help assess trial-and-error clinical pathways by using random forests to look for indicators and different parameters that have led to successful disease treatment.
Explainable AI frameworks are becoming a requirement as AI recommendations for an individual’s health or treatment must be able to explain what logic and data was used by the model to reach its conclusion.
AI is being used to reduce obstacles in data cleanup and management that will leave researchers free to focus on what the data is telling them in relation to their study.
Vaccine Distribution Logistics
Surgical Robots Using Machine Vision
AI Supported Medical Imaging Analysis
AI applications are enabling healthcare workers to collect and interpret real-time data of supply and distribution logistics for lifesaving vaccines.
Robot-assisted surgeries combine AI and robots for procedures that require the same repetitive movements as they can work without fatigue.
AI is being used as a tool for case triage by supporting clinicians in reviewing images and scans, enabling radiologists and cardiologists to identify essential insights for prioritizing critical cases and avoiding potential errors in reading electronic health records (EHRs), resulting in more precise diagnoses.
Here are some ways organizations in the healthcare and life sciences industry are mobilizing their AI initiatives.
Here are some ways organizations in the healthcare and life sciences industry are innovating with AI.
Visit our Healthcare and Life Sciences page for more ideas and stories of success from organizations in your industry.
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For a leader in global manufacturing, providing top notch customer service through personalization is a necessary element in setting their company apart from the competition. Slalom worked in collaboration with the client’s team in assessing their current data sources to build a personalized recommendation system to better serve their customer base.
Personalizing product recommendations
Predictive Maintenance for Cost and Safety Optimization
AI-Powered Inventory Management
Bill of Material Processing
Predictive maintenance (PdM) systems are assisting companies in predicting what replacement parts will be needed and when.
AI systems are being used to predict whether short-life ingredients will arrive on time or, if it is running late, how the delay will affect production to prevent production line bottlenecks.
Robotic Process Automation (RPA) can automate Bill of Material (BOM) processing to reduce errors in BOM which can lead to adverse chain impact on the remaining production cycle and result as a loss.
Here are some ways organizations in the manufacturing industry are getting started with AI.
Optimized Raw Material Price Forecasting
Proof of Delivery (POD)
AI-Assisted Product Development
Here are some ways organizations in the manufacturing industry are mobilizing their AI initiatives.
Businesses are adapting to the unstable price of raw materials to remain competitive in the market by using AI to predict material prices more accurately than humans.
Robotic Process Automation (RPA) tracks logistics systems and once delivery happens, transfer the shipping data into the warehouse management system which reduces the highly labor-intensive and high prone human errors traditional POD method.
Manufacturers are using digital twins (a virtual representation of a real-world product) before its physical counterpart is manufactured, enabling businesses to collect data from the virtual twin and improve the original product based on data.
Machine Learning for Generative design
Computer Vision for Optimized Production Quality
Edge Computing for Analysis
Here are some ways organizations in the manufacturing industry are innovating with AI.
Generative design uses machine learning algorithms to mimic an engineer’s approach to design by entering parameters of design like materials, size, and weight. This allows manufacturers to quickly generate thousands of design options for one product.
AI is being used to detect the differences from the usual product outputs by using computer vision and alerting a human operator to adjust.
Capturing sensor data in real time is being used to provide fast and decentralized insights to improve production quality and detect early signs of deteriorating performance of equipment.
Visit our Manufacturing page for more ideas and stories of success from organizations in your industry.
The need for iron-clad cyber security is at an all-time-high, and Slalom is utilizing machine learning to help tackle the problem for our sports entertainment client. Slalom was able to successfully identify the client’s known account takeover (ATO) attacks with a low false-positive rate, allowing the client to put a hold on the accounts and protect their customers without impacting their platform experience.
Preventing identity theft with machine learning
Content Personalization from AI
Computer Vision for Meta Data Tagging
Reporting Performance Automation
Here are some ways organizations in the media and communications industry are getting started with AI.
Media streaming companies are leveraging AI algorithms to study user demographics and behavior, then provide users with content that satisfies their specific interests and provide them with a highly personalized experience.
Computer vision is being used to analyze media content by identifying and framing objects to include suitable tags (such as category, color, size, etc.), saving countless hours searching through images for the perfect content.
AI-generated channel performance reports are being created from raw advanced analytics information, enabling producers to better understand which strategies and tactics should be prioritized or left behind.
Movie Search Optimization
Improving Streaming Quality
Recommendation Engines for Improved Personalization
Here are some ways organizations in the media and communications industry are mobilizing their AI initiatives.
AI improves movies' categorization so that search results become more accurate when user type category names rather than movie titles.
Streaming companies are predicting future demands using AI and position assets at strategic server locations based on predictions.
Social media companies are using AI to provide more personalized products and services for their users such as ads or suggested user groups.
NLP-powered Journalists
AI-powered Targeted Advertising
E-sports Sponsorships Vetting and Selection
Here are some ways organizations in the media and communications industry are innovating with AI.
Major news outlets are using AI to create storylines such as for football games based on some parameters and data collected from the analysis.
AI is helping advertisers reach their target audiences with hyper-targeted ads to improve the monetization of their web traffic.
Companies wanting to sponsor a team or a player in the E-sports industry are using AI driven methods to scan relevant audience information for decision-making.
Visit our Media and Communications for more ideas and stories of success from organizations in your industry.
Slalom created a topic model solution and chatbot UI that enabled a large energy company to access years of data through machine learning.
Enabling intelligent search through chatbot interface
Sensor Forecasting
Meter Readings using Robotic Process Automation (RPA)
Chatbot Scales the Customer Service Experience
Here are some ways organizations in the natural resources industry are getting started with AI.
Electric and gas companies are using data from sensors such as wind turbines to develop high-resolution wind forecasts through predictive analytics and AI.
Robotic process automation (RPA) is automating the verification of meter readings resulting in immediate time saved of work per employee.
Solar electric companies are generating instantaneous revenue by using simple messenger chatbots to service their customers’ immediate needs.
Defect Detection and Enhanced Quality Assurance
AI-Optimized Safety Standards
Reducing Production and Maintenance Costs
Here are some ways organizations in the natural resources industry are mobilizing their AI initiatives.
The oil and gas industry are using AI-powered computer vision tools in identifying improper threading in pipelines or defects in error-prone mechanisms.
AI is being used to monitor the work site, ensuring workers are following safety procedures without any deviations and send alerts with proactive recommendations.
AI and IOT technologies are helping to detect signs of corrosion to approximate the corrosion occurrence probability and raise alerts to pipeline operators.
Forecasting CO2 emission
AI-powered Early Alert System
Identifying Optimal Planting Areas
Here are some ways organizations in the natural resources industry are innovating with AI.
Edge computing technology is being applied to predictive analytics to pinpoint which regions of farmland will emit the most greenhouse gases.
AI is being used to calculate the movement and spread of hazardous occurrences such as fire to minimize resource damage.
Computer vision technology is being used to locate suitable planting sites, maintain plant health to aid in improving agriculture processes.
Visit our Natural Resources page for more ideas and stories of success from organizations in your industry.
For a large humanitarian organization, having intelligent insight into its donors' profiles aids the company in allocating funds correctly. Slalom used artificial intelligence to create a fast and thorough way for a worldwide humanitarian organization to review project proposals and get funding to top priority projects.
Allocating $1.3B in humanitarian funds to those who need it most
Social Media Bots for Political Advocacy
Automate Routine Government Tasks
Reducing Fraud and Error in the Tax System
Here are some ways Public Sector organizations are getting started with AI.
AI bots are being programmed to automatically promote engagement across social media platforms. These bots are being used to identify and flag harmful political posts, as well as create and target content to help aid and inform citizens.
The U.S. Citizenship and Immigration Services (USCIS) uses an AI-powered virtual assistant named Emma to answer questions and direct individuals to the right area of the website.
AI-powered anomaly detection can be used to help the government streamline the process of identifying tax and benefits fraud, which accounts for billions of dollars in incorrect payments within the benefits system.
Real-Time Media and Social Intelligence Analytics
Detecting Grant Fraud
Data Deduplication for Record Matching
Here are some ways Public Sector organizations are mobilizing their AI initiatives.
The government is utilizing social intelligence analytics by implementing a knowledge graph built from real-time streaming media sources to impact how it reacts during an emergency to aid civilians.
AI systems can extract text, process it, and determine a risk profile for grant money from the government which is often issued with specific criteria for how the money is supposed to be spent.
Many government agencies are modernizing their record systems by applying Natural Language Processing (NLP), Named Entity Recognition (NER), to reduce duplicated records associated with the same person, allowing less time spent searching for records by the government, and less time filling out paperwork by the citizen.
Efficiently Allocating Resources
AI for Crime Prediction
AI-Based Models for Weather and Climate Prediction
Here are some ways Public Sector organizations are innovating with AI.
AI uses photos to analyze road conditions, and to give local authorities the intelligence they need to direct maintenance efforts, improving resource allocation and public safety simultaneously.
AI is being used to identify patterns in policing heat maps to forecast where and when next crimes are likely to occur.
Government agencies are advancing their weather prediction models with the help of Machine Learning neural networks, which can predict weather and climate patterns at scale faster and more accurately than human analysis alone.
Visit our Public Sector page for more ideas and stories of success from organizations in your industry.
Theft and fraud are huge profit-killers for large retail companies. Slalom worked with one retail client to develop and implement a machine learning approach to theft and fraud detection in just 9 weeks, saving the company in manual effort and cost of theft.
Detecting retail fraud with machine learning
Autonomous Price Adjustment
AI-powered Product Categorization
Recommending Complementary Items
Here are some ways organizations in the retail industry are getting started with AI.
AI is able to predict different pricing strategies so retailers can produce the best promotional offers, acquire more customers, and increase sales.
Major retailers are employing AI to categorize millions of commodities from numerous vendors, saving thousands of hours of manual work.
E-commerce companies are utilizing AI to not only know what their customers searched for but to better understand why a customer is searching for a product which in turn helps recommend complementary items to customers.
Automating the Return Process
Product Recommendations by Search History
Value-added AI Customer Service
Here are some ways organizations in the retail industry are mobilizing their AI initiatives.
RPA bots are automating the manual returns process such as checking customer purchase record from the system.
AI systems are being used to analyze the purchasing patterns of the customers that helps retailers automatically send personalized recommendations to customers.
AI-powered in-store digital assistants are helping to build an effective communication channel through which customers can get answers to their queries.
Demand Prediction & Management
Virtual Fitting Room
Computer Vision Reducing Product Misplacement
Here are some ways organizations in the retail industry are innovating with AI.
AI-powered short term demand forecasting is being used to assist inventory managers to maintain products with respect to demand such as seasonal trends.
Cellphone cameras with AI software are being used by online users to virtually try-on a variety of clothing items, shoes, and other accessories.
Product detection algorithms are being used to find missing items and notify store staff about this using computer vision technology sending out notifications when products are misplaced.
Visit our Retail page for more ideas and stories of success from organizations in your industry.
Slalom realizes the value of quick adoption of revolutionary technology. That’s why we took on a project to help discover the newest open source opportunities faster by leveraging a machine learning pipeline to deliver rapid results.
Scouting open source opportunities with machine learning pipelines
Hyper-personalized B2B Marketing
Event Prediction
Automated Code Review for Release Management
Here are some ways organizations in the technology industry are getting started with AI.
Business to business companies are utilizing pattern recognition and predictive learning to identify top revenue generating leads and transform this data into a tailored marketing strategy.
Machine learning can help predict user preferences and conduct, which can ultimately trigger actions or alerts when the user appears to be disengaged.
AI is being used to enhance SaaS developers’ coding capabilities by creating essential checks to verify the quality of the coding, saving valuable time from senior developers during code review processes.
AI-powered Cybersecurity Systems
Lead Generation from Customer Data Profile
Natural Language Processing Survey Analytics
Here are some ways organizations in the technology industry are mobilizing their AI initiatives.
AI efficiently and instantaneously responds to security threats, augmenting the work of security analysts which greatly lowers the risk of human errors.
AI is being used to analyze the comprehensive data profile of customers or visitors to identify which companies a sales team need to connect with.
Companies are leveraging Natural Language Processing (NLP) to analyze text fields in reviews and surveys to discover insights to increase customer satisfaction and improve efficiency.
Predicting Website Traffic Sequencing
Knowledge Graphs for Intelligent Notetaking
Machine Learning as a Service
Here are some ways organizations in the technology industry are innovating with AI.
AI models are being used by web content developers to predict the next pages they will visit, which aids developers in load balancing and marketing strategists in knowing how to target a sequence of ads to entice the end user.
Knowledge graph technology is being used in notetaking application services to provide the end user with streamlined mind map of information based on learned relationships from the user’s annotations.
Automated Machine Learning is being offered as a service by large tech companies, which helps enable smaller companies with less ML experience to still benefit from ML model insights.
Visit our Technology page for more ideas and stories of success from organizations in your industry.
Regardless of industry, company size, or maturity level, finding and retaining talent is a priority across the board. That is why Slalom built a cross-practice team to tackle employee retention with an advanced ML model to help HR teams be more proactive with employee engagement.
Using People Analytics to Transform How We Think About Talent Attrition
Real-Time E-Commerce Analytics
HR Retention Management
AI For Recruiting
Here are some ways organizations are getting started with AI.
AI provides you with recommendations for your time-sensitive decisions which allows you to act timely and keep your KPI’s unharmed.
AI is helping to predict which employees are likely to churn and improve their job satisfaction to retain them.
AI is being used to screen potential hires, assessing eligibility, and answering questions candidates have about the role then delivering a ranked shortlist directly to recruiters.
Rural Farming Optimizing Crop Yields
Mitigating Climate-Related Disasters
Fraudulent Insurance Claims Detection
Here are some ways organizations are mobilizing their AI initiatives.
Agricultural service providers in rural Africa are using AI to predict farmer behavior, harvest yields, and general data ETL processes for small farms.
AI-enabled Weather dashboards are being used to provide accessible, localized, and verified data on threats of wildfires, air pollution and coastal flooding that can help individuals and organizations make data-driven decisions, to ensure safer outcomes.
AI-powered predictive analytics and text analysis tools are being used to detect fraudulent claims based on business rules with data captured from the overall story.
AI-powered Factory Robotics
Semiconductor Logistic Anomaly Detection
The Ocean Cleanup
Here are some ways organizations are innovating with AI.
Factory floors are changing with programmable bots that can work next to employees to take over more repetitive tasks, automating physical processes such as manufacturing or coordination.
Using satellite imagery, AI is being leveraged to track activity and abnormalities in factories and monitor the flow of raw materials in supply chains.
AI is being used to extract plastic pollution from the ocean and catalog the amount of plastic waste and marine debris collected.
Visit our Industry pages for more ideas and stories of success.