Artificial Intelligence in Business - Practical Applications for 2018 and Beyond

Artificial intelligence has moved beyond the hype cycle to become a significant force in business transformation. As we approach the end of 2018, organizations across industries are implementing AI solutions that deliver tangible business value. This comprehensive guide explores practical AI applications, implementation strategies, and the organizational changes required to capitalize on this transformative technology.

The State of AI in Business: 2018

The business AI landscape has evolved significantly over the past year:

• From Experimentation to Implementation: Organizations moving from pilots to production
• Democratization of AI Tools: More accessible platforms reducing barriers to entry
• Focus on Business Outcomes: Emphasis shifting from technology to value creation
• Integration with Existing Systems: AI augmenting rather than replacing current technologies
• Ethical Considerations: Growing awareness of responsible AI development and deployment

According to recent research, 47% of organizations have implemented at least one AI capability in their business processes, up from 20% in 2017, signaling a significant acceleration in adoption.

High-Impact AI Applications in 2018

Several AI applications are delivering measurable business value today:

1. Intelligent Process Automation

Enhancing efficiency beyond traditional automation:

• Intelligent Document Processing: Extracting and processing information from unstructured documents
• Cognitive Workflow Automation: Systems that can handle exceptions and make decisions
• Predictive Maintenance: Anticipating equipment failures before they occur
• Quality Control Automation: AI-powered visual inspection and anomaly detection
• Smart Resource Allocation: Optimizing staff and asset deployment based on predicted demand

Business Impact: Organizations implementing intelligent process automation report 20-35% cost reduction in targeted processes while improving accuracy and consistency.

Example: JPMorgan Chase implemented COIN (Contract Intelligence) to review legal documents, accomplishing in seconds what previously took 360,000 hours of lawyer time annually.

2. Customer Experience Enhancement

Creating more personalized, responsive customer interactions:

• Conversational AI: Chatbots and virtual assistants handling customer inquiries
• Personalization Engines: Tailoring experiences based on individual preferences and behavior
• Customer Journey Optimization: Predicting and addressing pain points proactively
• Sentiment Analysis: Monitoring and responding to customer emotions across channels
• Voice of Customer Analytics: Extracting actionable insights from customer feedback

Business Impact: Companies leveraging AI for customer experience report 10-15% increase in customer satisfaction scores and 15-20% improvement in conversion rates.

Example: Sephora's Virtual Artist uses AI and augmented reality to allow customers to try makeup virtually, increasing engagement and reducing product returns.

3. Predictive Analytics and Decision Support

Enhancing decision-making with data-driven insights:

• Demand Forecasting: Predicting future customer demand with greater accuracy
• Risk Assessment: Identifying potential risks and recommending mitigation strategies
• Market Opportunity Analysis: Uncovering untapped market segments and needs
• Scenario Planning: Modeling potential outcomes of different strategic choices
• Performance Optimization: Identifying factors that drive business performance

Business Impact: Organizations using AI-powered predictive analytics report 15-25% improvement in forecast accuracy and 20-30% faster decision-making.

Example: Unilever uses AI to analyze market data, social media trends, and weather patterns to predict ice cream demand, reducing waste and stockouts by 30%.

4. Sales and Marketing Optimization

Improving revenue generation through AI-powered insights:

• Lead Scoring and Prioritization: Identifying the most promising prospects
• Churn Prediction and Prevention: Anticipating and addressing customer attrition
• Dynamic Pricing: Optimizing price points based on multiple factors in real-time
• Content Personalization: Delivering the right message to the right person at the right time
• Marketing Mix Modeling: Determining optimal channel allocation for marketing spend

Business Impact: Companies implementing AI in sales and marketing report 10-15% revenue increase and 15-20% marketing efficiency improvement.

Example: Harley-Davidson increased leads by 2,930% after implementing an AI-driven marketing platform that optimized ad spend and personalized outreach.

Industry-Specific AI Applications

How different sectors are leveraging artificial intelligence:

1. Financial Services

AI applications transforming banking and insurance:

• Fraud Detection: Identifying suspicious patterns in real-time
• Algorithmic Trading: Executing trades based on market analysis
• Credit Risk Assessment: Evaluating creditworthiness using alternative data
• Claims Processing: Automating insurance claims review and processing
• Regulatory Compliance: Monitoring transactions for compliance issues

Example: HSBC implemented AI-powered fraud detection that reduced false positives by 20% while improving actual fraud identification by 50%.

2. Healthcare

AI innovations improving patient care and operational efficiency:

• Diagnostic Assistance: Supporting clinicians in image analysis and diagnosis
• Treatment Recommendation: Suggesting personalized treatment plans
• Patient Monitoring: Tracking patient conditions through remote sensors
• Administrative Automation: Streamlining scheduling, billing, and documentation
• Drug Discovery: Accelerating pharmaceutical research and development

Example: Stanford's AI system can detect skin cancer with accuracy comparable to dermatologists, analyzing images to identify potentially cancerous lesions.

3. Manufacturing

AI applications enhancing production processes:

• Predictive Maintenance: Anticipating equipment failures to prevent downtime
• Quality Assurance: Detecting defects through computer vision
• Supply Chain Optimization: Predicting disruptions and optimizing inventory
• Generative Design: Creating optimized product designs based on constraints
• Energy Optimization: Reducing energy consumption in production facilities

Example: Siemens' gas turbine factory uses AI to monitor production, reducing inspection time by 30-50% and improving quality.

4. Retail

AI transforming the shopping experience:

• Inventory Optimization: Ensuring the right products are available at the right time
• Visual Search: Allowing customers to find products using images
• Customer Behavior Analysis: Understanding in-store and online shopping patterns
• Personalized Recommendations: Suggesting relevant products to individual shoppers
• Dynamic Pricing: Adjusting prices based on demand, competition, and other factors

Example: Walmart uses AI-powered robots to scan shelves for out-of-stock items, incorrect prices, and misplaced products, improving inventory accuracy by 30%.

Implementing AI in Your Organization

Strategies for successful AI adoption:

1. AI Strategy Development

Creating a roadmap for AI implementation:

• Business Problem Identification: Focusing on specific challenges AI can address
• Value Assessment: Estimating potential ROI for different AI applications
• Capability Gap Analysis: Evaluating current vs. required technical capabilities
• Prioritization Framework: Selecting high-impact, feasible projects
• Implementation Roadmap: Planning a phased approach to AI adoption

Best Practice: Start with a problem-first approach rather than a technology-first approach, identifying specific business challenges that AI can help solve.

2. Data Readiness Assessment

Preparing the foundation for AI success:

• Data Inventory: Cataloging available data sources and their quality
• Data Governance: Establishing policies for data management and usage
• Infrastructure Evaluation: Assessing current systems' ability to support AI
• Data Collection Strategy: Identifying and addressing data gaps
• Privacy and Compliance: Ensuring adherence to regulations like GDPR

Best Practice: Conduct a comprehensive data audit before beginning AI projects to identify quality issues, gaps, and governance needs.

3. Build vs. Buy Decision

Determining the right approach for your organization:

• Custom Development: Building proprietary AI solutions tailored to specific needs
• AI Platforms: Leveraging cloud-based AI services (AWS, Azure, Google Cloud)
• Industry Solutions: Implementing specialized AI applications for your sector
• Hybrid Approaches: Combining custom elements with existing platforms
• Vendor Evaluation Criteria: Assessing potential partners and solutions

Best Practice: Consider starting with pre-built solutions for common applications while developing custom approaches only where they provide significant competitive advantage.

4. Organizational Readiness

Preparing your team for AI adoption:

• Skills Assessment: Identifying current capabilities and gaps
• Talent Strategy: Developing plans for hiring, training, or partnering
• Change Management: Preparing the organization for new ways of working
• Cross-Functional Collaboration: Breaking down silos between business and technical teams
• Executive Sponsorship: Securing leadership support for AI initiatives

Best Practice: Create a Center of Excellence (CoE) for AI that brings together technical expertise, business knowledge, and change management capabilities.

Overcoming AI Implementation Challenges

Addressing common obstacles to successful AI adoption:

1. Data Quality and Integration

Tackling the foundation of AI success:

• Challenge: Inconsistent, incomplete, or siloed data limiting AI effectiveness
• Solutions:
  • Implement data quality management processes
  • Develop data integration strategies for legacy systems
  • Create synthetic data for training when real data is limited
  • Establish ongoing data governance practices
  • Leverage data preparation tools to streamline cleaning and transformation

Best Practice: Allocate 60-80% of AI project resources to data preparation, as this is typically the most time-consuming and critical aspect of implementation.

2. Talent and Expertise Gaps

Addressing the AI skills shortage:

• Challenge: Limited availability of AI specialists and data scientists
• Solutions:
  • Upskill existing technical staff through targeted training
  • Partner with universities and research institutions
  • Leverage managed AI services to reduce expertise requirements
  • Consider acqui-hiring (acquiring companies for their talent)
  • Develop a hybrid team of specialists and generalists

Best Practice: Create cross-functional teams that pair AI technical experts with domain experts who understand the business context.

3. Integration with Legacy Systems

Connecting AI with existing infrastructure:

• Challenge: Difficulty incorporating AI into established technology ecosystems
• Solutions:
  • Implement API-based integration approaches
  • Use robotic process automation (RPA) as a bridge to legacy systems
  • Develop microservices architecture for gradual modernization
  • Create data abstraction layers to simplify access
  • Consider containerization for AI deployment

Best Practice: Start with standalone AI applications that deliver value independently before attempting deep integration with core systems.

4. ROI Measurement and Justification

Demonstrating the business value of AI:

• Challenge: Difficulty quantifying and attributing benefits of AI investments
• Solutions:
  • Establish clear baseline metrics before implementation
  • Define both leading and lagging indicators of success
  • Implement A/B testing to isolate AI impact
  • Create comprehensive ROI models that include indirect benefits
  • Start with projects that have clear, measurable outcomes

Best Practice: Develop a balanced scorecard for AI initiatives that includes efficiency metrics, quality improvements, revenue impact, and strategic value.

Ethical and Responsible AI

Ensuring AI implementation aligns with organizational values:

1. Bias Detection and Mitigation

Addressing algorithmic fairness:

• Challenge: AI systems perpetuating or amplifying existing biases
• Approaches:
  • Diverse and representative training data
  • Regular bias audits of AI systems
  • Transparent documentation of model limitations
  • Human oversight of AI decisions
  • Ongoing monitoring for unexpected outcomes

Best Practice: Implement a bias impact assessment process for all AI systems, especially those making decisions that affect customers or employees.

2. Transparency and Explainability

Making AI decisions understandable:

• Challenge: "Black box" AI systems making unexplainable decisions
• Approaches:
  • Selecting models that balance performance with explainability
  • Implementing tools for post-hoc explanation of complex models
  • Creating user-friendly interfaces that communicate AI reasoning
  • Documenting model limitations and confidence levels
  • Establishing processes for contesting automated decisions

Best Practice: Match the level of explainability to the risk and impact of the decision being made—higher stakes require greater transparency.

3. Privacy and Data Protection

Respecting user data in AI applications:

• Challenge: Balancing data needs with privacy concerns
• Approaches:
  • Privacy by design in AI development
  • Data minimization principles
  • Anonymization and pseudonymization techniques
  • Clear consent mechanisms for data usage
  • Regular privacy impact assessments

Best Practice: Develop a comprehensive data ethics framework that goes beyond compliance to establish principles for responsible data use in AI.

The Future of AI in Business: 2019 and Beyond

Emerging trends that will shape business AI in the coming years:

1. AI Democratization

Making AI accessible to more organizations and users:

• Low-code/no-code AI development platforms
• Pre-trained models for common business applications
• AI-as-a-Service offerings reducing implementation complexity
• Automated machine learning (AutoML) simplifying model development
• Industry-specific AI solutions requiring minimal customization

Strategic Implication: AI will become a standard business tool rather than a specialized technology, with competitive advantage shifting from AI possession to AI application.

2. Human-AI Collaboration

Evolving relationship between employees and AI systems:

• AI augmentation rather than replacement of human workers
• New roles emerging at the intersection of human and machine capabilities
• Collaborative intelligence frameworks for optimal task allocation
• Reskilling initiatives to prepare workforce for AI partnership
• Human-in-the-loop systems combining automation with human judgment

Strategic Implication: Organizations will need to redesign workflows and roles to capitalize on the complementary strengths of humans and AI.

3. Edge AI

Moving intelligence to the point of interaction:

• AI processing on local devices rather than in the cloud
• Reduced latency for time-sensitive applications
• Enhanced privacy by keeping data local
• Lower bandwidth requirements for IoT implementations
• Greater resilience with less dependence on connectivity

Strategic Implication: Edge AI will enable new use cases in environments with connectivity limitations or real-time requirements.

4. Federated Learning

New approaches to AI model development:

• Training models across distributed devices without centralizing data
• Enhanced privacy by keeping sensitive data on local devices
• Ability to leverage larger, more diverse datasets
• Reduced data transfer and storage requirements
• Compliance advantages for regulated industries

Strategic Implication: Federated learning will help organizations overcome data silos and privacy concerns that currently limit AI development.

Conclusion: Building an AI-Ready Organization

As we look toward 2019 and beyond, the organizations that will gain the most value from AI will be those that:

1. Focus on business outcomes rather than technology implementation
2. Invest in data infrastructure as the foundation for AI success
3. Develop a balanced talent strategy combining hiring, training, and partnerships
4. Implement responsible AI practices that align with organizational values
5. Create a culture of experimentation that embraces continuous learning

The most successful AI implementations start not with the question "How can we use AI?" but rather "What business problems can AI help us solve?" By maintaining this problem-first mindset and taking a strategic approach to implementation, organizations can move beyond the hype to realize tangible business value from artificial intelligence.

Remember that AI is not a destination but a journey—one that requires ongoing adaptation, learning, and refinement as both the technology and your organization evolve.

This article was written by Nguyen Tuan Si, a technology strategist specializing in helping organizations develop and implement effective AI strategies across various industries.