Defining Enterprise AI
Enterprise AI refers to the application of artificial intelligence technologies to address critical business needs at an organizational level. The key focus areas of enterprise AI include:
- Improving efficiency through business process automation
- Enhancing supply chain and manufacturing operations
- Offering data-driven insights for strategic decision making
- Personalizing customer interactions to improve satisfaction
According to a McKinsey survey, the adoption of AI across industries could contribute an additional $13 trillion to the global economy by 2030. This highlights the transformational potential of enterprise AI for businesses.
Types of Enterprise AI Solutions
Enterprise AI solutions come in many forms tailored to business requirements:
- AI Software Platforms: End-to-end development platforms like IBM Watson, AWS SageMaker, Microsoft Azure Machine Learning that allow building, deploying and managing AI models.
- AI Applications: Out-of-the-box solutions focused on specific use cases like Salesforce Einstein for sales process enhancement, Pega Customer Decision Hub for customer analytics, Appen for data annotation.
- Conversational AI: Intelligent chatbots and virtual assistants like Kore.ai, Observe.ai, Yellow.ai that enable natural language interactions.
- Robotic Process Automation: Automating repetitive clerical tasks across departments using AI-powered software robots from Automation Anywhere, UiPath and Blue Prism.
- AI Business Intelligence: Augmented analytics tools like ThoughtSpot, Sisense Fusion tap AI to gain insights from enterprise data.
These enterprise AI tools integrate with existing business systems while relying on proven ML techniques such as deep learning, NLP, reinforcement learning, graph analytics among others under the hood.
Real-World Examples of Enterprise AI Applications
- General Motors saves $1 billion annually using computer vision AI to detect manufacturing defects with 99% accuracy.
- JPMorgan Chase leverages AI for predictive analytics to forecast mortgage risk scenarios and minimize loan defaults.
- Coca Cola’s AI-based demand planning boosted forecast accuracy by 10% leading to a 25% reduction in inventory costs.
- Starbucks applies reinforcement learning to optimize staff scheduling across 30,000 stores worldwide.
These examples highlight how enterprises employ specialized AI across key functions:
Enterprise AI Applications and Use Cases
We will now explore the popular categories of enterprise AI applications with real-world examples of use cases and business impact across sectors:
Process Automation
AI-driven automation focuses on augmenting repetitive, high-volume tasks typically performed by humans to improve efficiency and consistency.
Use Cases
- Data Entry: Extracting information from forms and documents
- IT Support: Mechanizing incident ticket handling
- Finance: Automating invoicing and reimbursement processing
According to Deloitte, the banking sector can save ~100 million hours in time per year through automation. AI techniques like optical character recognition (OCR), natural language processing (NLP) and robotic process automation (RPA) are pivotal here.
Benefits
- 30-50% improvement in process efficiency
- 80% reduction in processing costs
- High accuracy exceeding 95%
- Freeing staff capacity for value-add work
Enhanced Decision Making
Identifying business metrics to focus decision support AI is key as opposed to taking a generic approach.
Use Cases
- Predictive Maintenance: Forecast equipment failures, reduce downtimes
- Logistics Planning: Optimize shipment routing, warehouse utilization
- Fraud Analytics: Detect financial crime risks in real-time
American Airlines leverages predictive maintenance algorithms, saving $75 million annually. AI evaluates 2 million data points per aircraft daily!
Benefits
- 10-25% improvement in critical metric like revenue or risk reduction
- Measurable ROI within 6-9 months for analytics use cases
- Improved business agility and accelerated decision velocity
Enhanced Customer Experiences
AI helps uncover hidden insights from customer data to tailor interactions and recommendations.
Use Cases
- Chatbots for Customer Support: Address common queries 24/7
- Recommendation Systems: Suggest contextual upsell / cross-sell options
- Customer Segmentation: Define micro-segments to personalize campaigns
Sephora’s AI-powered recommendation engine witnessed a 30% increase in app users translating to over $1 billion in incremental revenue.
Benefits
- 10-30% increase in customer experience metrics like NPS and CSAT
- 15-25% improvement in sales conversion and order value
- Stronger customer loyalty, measured through metrics like repeat purchases and retention rates
The appetite for enterprise AI continues rising. According to Gartner, 75% of enterprises will shift from piloting to operationalizing AI by 2024, as CIOs ramp up AI investments.
However, multiple challenges lurk in the journey of AI adoption…
Challenges in Enterprise AI Adoption
Despite growing CXO-level interest and strong use case potential, enterprises grapple with adoption roadblocks:
Data Complexities
- Fragmented data silos with bottlenecks in consolidating cross-functional data
- Inconsistent data collection mechanisms and lack of labeling standards
- Meeting rigorous data security, privacy and regulatory norms
30-50% of AI projects stall at PoC stages due to data management issues as per Gartner.
Integrating core enterprise data from CRM, ERP and HR systems remains cumbersome. Most legacy systems lack critical mechanisms to enable real-time data access.
Further, the distributed nature of organizational data flows leads to labeling inconsistencies which significantly slow down supervised ML algorithm training.
Talent Shortages
The supply-demand gap for AI skills persists. Per LinkedIn’s 2022 Emerging Jobs report, demand for AI specialists and Machine Learning engineers rose by 74% and 66% respectively from 2018 levels.
- Only 22% of AI decision makers believe they have the right talent to meet objectives.
- 67% cite lack of skilled workers as the key adoption barrier.
The skill gap spans both data science and engineering domains across levels, from associates to senior leadership.
Attracting and nurturing internal AI talent is hence a rising boardroom priority.
Algorithmic Bias
Historic biases in data coupled with lack of diversity in ML teams increase the risk of biases creeping into AI algorithms which leads to below outcomes:
- Flawed business decisions due to skewed model recommendations
- Reputational damage and erosion of customer trust
Debiasing techniques and emphasis on responsible AI across the model development lifecycle are vital.
Lack of Model Governance
The lack of standardized mechanisms for continuous model evaluation leads to below issues:
- Deterioration in model performance beyond initial benchmarks
- Data drift over time further exacerbates the decay
Gartner notes only 23% of AI models make it from PoCs to full-scale production deployments owing to under-estimated governance needs.
Inadequate Cloud Infrastructure Strategy
Absence of firm cloud adoption for AI prompts below challenges:
- Heavy reliance on internal infrastructure driving up latency and costs for AI workloads
- Lack of mechanisms to scale compute on demand hampers experiment velocity
- Compliance and security gaps increase with fragmented environments
Best Practices for Enterprise AI Success
We will now share proven recommendations across pillars like data, talent, governance and infrastructure to drive enterprise AI effectiveness:
Establish Data Readiness
- Appoint central data stewards responsible for enforcing policies, monitoring quality
- Audit existing data environments, pinpoint accessibility gaps via surveys
- Introduce data schema standards for labeling, storage and security
- Identify bottlenecks slowing real-time data availability and address them
Data governance is a key enabler. One global bank achieved $700 million in savings over three years per McKinsey by establishing a central data office focused on driving analytics ROI.
Institutionalize Responsible AI
- Integrate bias testing into model development cycles
- Diversify AI / analytics teams to minimize homogeneous thinking
- Appoint internal councils to assess model risks continuously
- Formalize Human-in-the-Loop review mechanisms before deployments
One recommendation is for enterprises to embrace Responsible AI (RAI) frameworks like the one from Capgemini to structure adoption. RAI emphasizes addressing bias, accountability, transparency and ethics through the lifecycle.
Strengthen Model Governance
Core tenets of the governance roadmap should cover:
- Central lifecycle management from model prototyping to deployment
- Continuous testing harness to prevent performance decay
- DevOps rigour via MLOps for smooth handoffs between teams
- Automated dashboards on model KPIs like accuracy, data drift
Nurture Internal AI Talent
Leading companies boost in-house capabilities via:
- Partnerships with AI academies like Udacity, Coursera to re-skill employees
- Sabbaticals for AI upskilling paired with retention bonuses
- Lateral entry at senior levels combined with coaching / mentor programs
- Dedicated career advancement blueprints to cement the talent pipeline
Chick-fil-A’s AI upskilling initiative covered over 110,000 restaurant employees, amplifying customer satisfaction and sales per store.
Modernize Infrastructure
Technology overhaul should focus on:
- Adopting hybrid / multi-cloud models to tap specialized AI cloud services
- Revamping data architectures for increased availability and scalability
- Ensuring compliance, security and data sovereignty
- Testing resilience via failover simulations
Cloud investments strongly complement AI success. IDC notes that organizations able to scale AI applications efficiently across hybrid environments have a 64% higher five-year ROI compared to peers.
How Does Enterprise AI Differ from Consumer AI?
While both consumer AI and enterprise AI leverage technologies like ML and NLP, some vital differences exist:
Purpose and Application
Enterprise AI performs narrowly focused roles aligned to business environments, priorities and data. For instance, an AI model predicting mortgage defaults handles bank transaction data.
Whereas consumer AI interacts directly with individual users to enrich daily experiences. A chatbot assisting online shopping journeys exemplifies this.
Hence application scope varies significantly.
Target Audience
Enterprise AI users comprise professionally trained employees and IT teams across departments like operations, finance, marketing.
In contrast, consumer AI serves a broader demographic like online shoppers, mobile app subscribers, smart device owners who need intuitive interactions.
Key Requirements
Enterprise AI demands rigorous performance on metrics like risk reduction,Eighty percent drop in errors drive ROI here. whereas consumer AI pursues wider qualitative goals like customer delight.
Enterprise AI also needs stringent access control, audit trails demonstrating regulatory compliance which consumer AI may lack.
Lastly, enterprise AI requires trust and transparency ensuring recommendations align to business policies and human oversight.
Data Profile
Enterprise AI often handles sensitive internal data like financials, requiring additional data minimization, anonymization and encryption measures.
Whereas consumer AI utilizes comparatively less risky data from user behaviors to fuel recommendations.
Volume and variety also differ with enterprise data spanning diverse siloed sources. Data preparation complexity increases here.
Evaluation Criteria
Enterprise AI success hinges on tangible returns like boosted sales conversions, faster processing times, improved predictive accuracy and so on.
For consumer AI, the metrics are more indirect like online session times, mobile retention rates, customer satisfaction scores indicating affinity.
Drawing parallels between metrics and tweaking models is therefore harder for consumer AI.
The Road Ahead
Enterprise AI adoption is poised for massive headroom despite current barriers.
Backed by a maturation of capabilities, proven ROI and CXO sponsorship, seventeen percent yearly growth is forecasted through 2027 per Statista.
New data storage paradigms like data lakes, lately model serving tools and responsible AI guardrails will tackle existing bottlenecks.
As multi-billion-dollar investments from technology giants like Microsoft, Google and Amazon reinforce enterprise AI, the stage is set for industry leaders to gain significant competitive advantage in the coming decade by harnessing AI’s potential.
