When to use AI
Factors to determine if AI is a good fit for your business
Artificial Intelligence can be the right fit for your business, if your organization has:
- Digital data that potentially encompasses the information you need, even if hidden or disguised
- Large scale and repetitive tasks that can be automated to free human workers
- Large volumes of data to train and improve the AI model
- Sufficient data and storage provisions, such as servers and data warehouses to manage large amounts of complex data required to implement sophisticated AI algorithms
- Robust security maintenance programs and data governance policies to prevent the potential misuse of sensitive/ personal data and other security mishaps while giving AI algorithms access to your data
- Assessing the potential implications of AI on your business model, cost, and revenue is also a key consideration
AI Applications in Business
AI software development across core business functions
- Document assembly (automation) tools for the drafting of legal documents through forms and databases using “if-then” decision-tree logic
- Document mining and benchmarking tools to identify and compare legal documents, policies, and contractual provisions
- Document revision and analysis tools to analyze drafted language with transactional considerations in mind in order to evaluate the textual use and effectiveness
- Analysis of historical and existing data patterns to make predictions and improve the performance of supply chain, set prices that maximize profits, and launch products/ services that exactly meet customer needs
- Business leaders can understand what causes customer churn and how they can prevent it by innovating continuously
- Explains how the business is impacted by the volatility and unpredictability of commodity prices through near-real pricing forecasts
- Better understanding of historical data and hidden patterns improve the ability of business to forecast market growth
- ML algorithms leverage big data to deliver personalized recommendations that suit each customer’s tastes and preferences across all touchpoints
- AI recommender systems enable customers to discover products and services they might otherwise have not found on their own
- Recommendations AI equips companies to influence and shape customer decisions, improve customer retention, increase sales, and boost cart value
- Context-aware bots with the ability to self-learn continuously from prior interactions and improve their responses over time
- Predictive intelligence and analytics to personalize interactions based on a customer’s profile, preferences, and past behavior
- Integrates easily with any legacy/ back-end systems and specializes in completing conversations (tasks) by interacting with multiple systems
- Personalized course recommendations based on user behavior
- Tailoring of eLearning content to each learner based on their learning needs
- Targeted allocation of resources and learning content to save time
- Real-time learning assistance provided through AI-driven chatbots
- Automated knowledge checks
- AI helps analyze numerical and natural language data, referencing relevant insurance policy information, healthcare forms, and other input documents
- Analyzes claims management data to provide insightful recommendations to knowledge workers, so they can determine eligible claims, and percentage of payout
- ML algorithms can be trained to quickly spot “red flags” in fraudulent claims and risk management data, giving knowledge workers more time to spend on complex cases
Success stories
Examples of AI in business
1. Custom AI software to manage employee compensation claims
Fingent developed a custom AI software to enhance and expedite the tasks involved in processing workers’ compensation claims. The software employs Optical Character Recognition (OCR) and AI for smart data extraction and leverages contextual analysis to extract meaningful insights from larger datasets.
- 50% decline in claim cycle time and associated personnel costs
- 57% cost-drop for the insurers, carriers, and claim admins
- Average case settlement time reduced to 1-2 days
2. AI-powered virtual assistant to manage employee Q&A
Across several organizations, HR teams are forced to spend their time on routine and mundane tasks, like answering common employee questions. To solve this dilemma and prevent productivity loss in HRM, Fingent built an AI-powered virtual assistant (chatbot) that handles the most common questions asked by employees.
- Improved the HR team’s response time and minimized their workload
- Instant and accurate responses to common queries
- Rigorous AI training empowers the bot to deal with more complex questions and circumstances
3. AI-powered people development platform
Artificial intelligence offers a sound solution to personalize learning for each individual, while not compromising the learning goals. Skill Lake, a people development platform built by Fingent, leverages AI and ML to facilitate continuous and personalized learning for organizational training providers.
- Personalized course recommendations and learning content for users
- Discovery of relevant content promotes learner engagement
- Customized learning paths, mentoring, and competency mapping
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AI Integrations For Business
AI integrations to improve the performance of existing business systems
Customer service and support : AI-powered chatbots use machine learning algorithms and natural language processing to understand customer requests and respond appropriately faster than human workers and at lower costs. Drawing on customer data and predictive analytics, AI can also power recommendation functions.
Supply chain management : Machine learning algorithms can forecast demand level variations allowing businesses to create more efficient and cost-effective supply chains. By eliminating the risks of overstocking as well as running short on in-demand products, supply chain leaders will better know the optimal time to move supplies.
Intelligent decision making : Even in times of great uncertainty, AI- and ML-based algorithms allow business leaders to navigate change and elevate decision making across operational and customer-facing processes. The combination of data analytics and machine learning improve both customer and operations intelligence, so businesses can seamlessly shift from being reactive to proactive.
Enterprise IT automation : Using AI to predict, automate, and optimize many of the data center tasks that humans currently perform will significantly reduce the amount of human error in operational activities. AI-defined infrastructure and automated IT systems enable optimum cost utilization for infrastructure, enhance the security of data center operations, and improve the quality of services provided to customers.
Sales and marketing : Modern sales and marketing teams have access to more data than ever before. AI can assist marketers to deeply understand their customers’ behavior, actions, and indications, so they can target the right strategy to the right person in a time-saving and effective way. AI allows sales teams to improve sales forecasting, pipeline analysis, and buying group visibility, so they get more opportunities to drive greater sales ROI.
Infrastructure requirements
Infrastructure considerations and criteria for AI software development
Infrastructure requirements for implementing AI in business:
- High-performance computing resources, including CPUs (for simple AI workloads) and GPUs (for complex scenarios like deep neural networks)
- Storage capacity monitoring and planning for expansion, that is, building the ability to scale storage as data volumes grow
- Good, fast, and reliable networks with high-bandwidth, low latency, and consistent service wrap and technology stack
- Highly secured infrastructure technology to protect sensitive personal data from being exploited or misused
- Cost-effective dedicated servers, network, and storage infrastructure to facilitate and promote the use of AI technology
Securing AI solution
Security measures to protect AI systems and applications in business
- Understand the security risks and vulnerabilities to AI solutions, early in the project lifecycle.
- Inventory where and how the business incorporates artificial intelligence and machine learning components and assets.
- Follow a threat modeling practice to identify the potential attacks, vulnerabilities, and countermeasures that could affect your application.
- Data is the core of AI solutions. Protect data throughout the pipeline by adopting recommended practices for access management, data encryption, data classification, and monitoring.
- Ensure that sensitive data is cleansed, obfuscated, or anonymized when using it for training, testing, and inferencing purposes.
- Adopt as many recommended security practices as early in the project as possible, including network isolation, secure storage for passwords, tokens, strings, and keys, role-based access, and isolated compute targets, among others.
- Use a trusted AI automation tool for testing the security of AI systems and to routinely monitor and scan for AI-specific vulnerabilities.
- Implement a response plan in place that includes the threats to AI solutions, security response, and escalation procedures.
Pros & Cons of AI
Benefits and risks of implementing AI in business
Benefits
- Automation of workflows
- Deeper understanding of customer behavior
- Operational cost reduction while improving performance
- Improved resilience and response time
- Better business decisions and next actions
- Improved customer experience, awareness, and loyalty
- Mistakes like coding errors could create a significant financial crisis
- Vulnerability to cyberattacks
- Technology needs to sound more human than robotic
- Technological complexity and skills shortage
- Ethical concerns and potential lack of transparency
- Algorithmic bias originating from human bias in the data
- High costs of creation and maintenance
AI Tools & Technologies
AI technologies and tools to reimagine business operations
Top technologies that drive AI in business
Artificial Intelligence is not just one technology, it’s a select assortment of multiple cognitive technologies:
- Machine Learning
- Deep Learning Platforms
- Natural Language Generation
- Natural Language Processing
- Computer Vision
- Robotic Process Automation
- Speech Recognition
- Hardware Integrated with AI
- Business Decision Management
Custom AI software development is the best tool to accelerate business growth and profitability
Custom artificial intelligence software development services:
- Gives your business more control over the software features and the development roadmap.
- Eliminates the overhead of features you don’t need or wouldn’t like to pay for.
- Helps tackle specific business challenges and address unique enterprise needs.
- By developing a custom AI solution, you own the software forever. You can even potentially sell the technology to third parties.
- Custom AI software development is a better choice when you’re considering integrations with existing software.
- Updating or scaling the custom AI solution next is entirely up to you since you own the product.
- Custom AI applications are well-tested and configured to process specialized data sets in order to produce expected results.
Support & Maintenance
Managing custom AI software support and maintenance
Managing the maintenance of AI software:
Artificial Intelligence is not just one technology, it’s a select assortment of multiple cognitive technologies:
- Monitoring of machine learning models to detect any accuracy deviations and biases from the initial training data
- Retraining ML models with new hyperparameters or newly engineered features to reflect the shift in data patterns
- A/B testing to identify the drawbacks of existing ML models and replace them with advanced ML models featuring higher performance
- AI software operating in a volatile environment and feeding on dynamic user data needs regular training and maintenance
Managing support for AI software:
- Tracking and fixing software bugs
- Addressing issues related to integrations with AI
- Optimizing software performance
- Enhancing UI based on user feedback
- Development of new features
- Extension of AI-enabled functionality
- Enhancements to AI software based on rising user demands
Cost of AI implementation
Estimating the time, resources, and budget for AI implementation
Factors that influence AI software development cost:
- Technologies used to build the AI solution, such as data science, robotic process automation, image recognition, and so on
- Functionalities of the software, such as automation, deep data exploration, natural language processing, and computer vision, among others
- Specific capabilities of the software like voice and face recognition abilities, self-aware machines, fraud detection, etc.
- Integration of features that will augment the software’s function, accuracy, precision, and performance
- Hardware costs used to maintain and process the data required for training and optimizing AI and ML algorithms
- Labor costs to hire and maintain a team of AI and ML software engineers, business analysts, solution experts, quality assurance specialists, and project managers
- AI software development and maintenance costs, duration of the project, inclusion of extra features, and full or partial customization expenses
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Success & ROI
Measuring the success and ROI of AI software development
Planning the ROI from AI might be challenging, but following a few rules can help turn AI into a profitable investment for executives.
Assess holistically : While assessing and predicting AI’ ROI, consider the uncertainties of AI models, such as their exact error rate, changes in model performance, maintenance requirements over time, and how different AI initiatives could impact each other’s results.
Create a portfolio : Just like how you assess and predict the ROI of product innovations or financial investments in other areas of business, create and assess a mix of initiatives that will increase the chance of delivering the expected results. For instance, adopting a portfolio approach by applying test-and-learn, risk-return, and competitiveness-investment trade off could demonstrate how AI will pay off your business.
Integrated lifecycle management: Instead of managing individual AI projects, managing an integrated data-AI-cloud lifecycle will finetune project execution and avoid downside ROI surprises.
Investments and returns in AI software development
AI Investments
- Data (the most significant investment)
- Compute and storage resources
- AI and ML subject matter experts
- Data science training
- Licenses and subscriptions
- Time and cost savings
- Productivity improvements
- Increase in revenue
- Superior experience
- Skill retention
- Business agility
Insights
More Insights on AI Software Development
How AI Integration Helps Maximize Your Business ROI
Fear of losing jobs to automation, soaring IT budgets, lack of adequate skills and infrastructure, inability to perch on the ideal technology partner, and many other reasons are refraining businesses from venturing boldly into artificial intelligence initiatives.
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