Building an AI Business Case
Create a compelling business case to secure approval for your AI investment
Securing budget approval for AI initiatives requires a well-structured business case that clearly demonstrates value, addresses risks, and aligns with organizational goals. Kowalah’s Business Case template helps you create a comprehensive document that speaks to both technical and business stakeholders.
Why You Need a Strong Business Case for AI
AI investments often face heightened scrutiny due to:
- Perceived hype and uncertainty about outcomes
- Concerns about implementation complexity
- Questions about security, compliance, and ethics
- Competition for limited resources
- Lack of internal expertise to evaluate potential returns
A robust business case addresses these concerns by translating technical capabilities into business outcomes, quantifying benefits, and establishing a clear implementation path.
Template Structure
Kowalah’s AI Business Case template includes these essential sections:
Executive Summary
A concise overview highlighting:
- The business problem being addressed
- The proposed AI solution
- Key benefits and expected outcomes
- High-level cost overview
- Strategic alignment with company objectives
This section helps executives quickly grasp the value proposition and importance of your AI initiative.
Solution Description
A non-technical explanation of:
- Your proposed AI solution(s)
- Key capabilities and benefits
- High-level requirements
- Implementation considerations
This section bridges the gap between technical features and business value, making the solution understandable to all stakeholders.
Problem Assessment
A clear articulation of:
- Current challenges and limitations
- Business metrics impacted by the status quo
- External factors (regulatory, market, competitive) driving change
- Connection to broader organizational objectives
This section establishes the “why now” and urgency by highlighting the cost of inaction.
Impact Assessment
A detailed analysis of the current situation’s effects:
- Tangible impacts (quantifiable costs, lost opportunities, inefficiencies)
- Intangible impacts (customer experience, employee satisfaction, competitive positioning)
This section strengthens your case by demonstrating the full scope of the problem.
Cost Overview
A comprehensive breakdown of:
- Initial costs (licenses, hardware, implementation services)
- Implementation costs (internal resources, training, integration)
- Ongoing costs (subscriptions, maintenance, support)
- Total Cost of Ownership (TCO) calculation
- Return on Investment (ROI) projection
This section demonstrates financial responsibility and sets realistic expectations.
Benefits Analysis
A thorough examination of expected gains:
- Tangible benefits (cost savings, revenue increases, productivity improvements)
- Intangible benefits (improved decision-making, enhanced customer experience)
- Risk reduction (compliance, security, business continuity)
- Strategic advantages (market positioning, innovation capability)
This section makes the case for “why this matters” to the organization.
Initial Project Timeline
A high-level roadmap including:
- Key milestones in the procurement and implementation process
- Critical deadlines
- Resource requirements
- Success measurement points
This section shows thoughtful planning and establishes confidence in execution.
Project Governance
A clear structure for project oversight:
- Executive sponsor
- Project owner
- Core team members (by role and name)
- Extended stakeholders
This section demonstrates organizational readiness and accountability.
Tips for Creating an Effective AI Business Case
Focus on business outcomes, not technology
Focus on business outcomes, not technology
Avoid technical jargon and AI buzzwords. Instead, emphasize how the AI solution will solve specific business problems and deliver measurable outcomes. For example, rather than highlighting “natural language processing capabilities,” focus on how the solution will “reduce customer service resolution time by 45%.”
Quantify everything possible
Quantify everything possible
Decision-makers need numbers to evaluate your proposal. Where exact figures aren’t available, provide reasonable estimates based on industry benchmarks, pilot results, or vendor case studies. Be transparent about your assumptions and methodologies.
Address AI-specific concerns
Address AI-specific concerns
Proactively discuss common AI concerns like data privacy, algorithm bias, explainability, and governance. Show that you’ve considered these aspects and have plans to address them appropriately.
Include a phased approach
Include a phased approach
AI implementations often benefit from starting small and scaling based on results. Consider proposing a phased implementation with clear success criteria at each stage, which can reduce risk and build confidence.
Identify dependencies and risks
Identify dependencies and risks
Be upfront about what could go wrong or delay success. This demonstrates thorough planning and helps set realistic expectations about challenges that might emerge during implementation.
How Kowalah Helps
Kowalah guides you through creating your business case by:
- Providing a structured template aligned with best practices
- Asking targeted questions to help you gather the right information
- Suggesting industry benchmarks when you don’t have specific data
- Helping translate technical features into business benefits
- Offering frameworks for estimating and quantifying both tangible and intangible benefits
Example Business Case
AI-Powered Customer Service Automation - Business Case Example
AI-Powered Customer Service Automation - Business Case Example
Business Case: AI-Powered Customer Service Automation Executive Summary Midwest Financial is facing increasing customer service response times, with average wait times exceeding 15 minutes and resolution times averaging 48 hours. This has led to a 12% decrease in customer satisfaction scores over the past year and is contributing to a 7% customer churn rate. We propose implementing an AI-powered customer service platform that combines chatbot automation with agent augmentation tools. This solution will reduce wait times to under 2 minutes, decrease resolution times by 65%, and allow us to handle 40% more inquiries without adding headcount. The projected three-year ROI is 287% with a payback period of 14 months. This initiative directly supports our strategic objectives of improving customer experience and operational efficiency. Solution Description The proposed solution combines two complementary AI technologies:
Conversational AI Platform: An AI-powered virtual assistant capable of handling up to 70% of routine customer inquiries without human intervention. The platform uses natural language understanding to interpret customer questions and provide accurate, consistent responses across multiple channels (web, mobile, SMS). Agent Augmentation Tools: AI-powered assistant for human agents that provides real-time guidance, automates documentation, and suggests next best actions. This system will reduce average handling time by analyzing customer history and providing immediate access to relevant information.
Implementation will require integration with our current CRM system, customer data migration, and API connections to our knowledge base. The solution offers both cloud and on-premises deployment options, with SOC 2 Type II compliance and GDPR-ready data handling. Problem Assessment Our current customer service approach suffers from several critical limitations:
Scalability Issues: Our team of 45 agents cannot efficiently handle the growing volume of inquiries (currently 28,000 monthly), resulting in unacceptable wait times. Knowledge Management Challenges: Agents spend 35% of their time searching for information across disparate systems, leading to inconsistent customer experiences. Limited Self-Service Options: Our current self-service portal resolves only 15% of inquiries, far below the industry benchmark of 35-40%. After-Hours Support Gaps: We provide limited support outside business hours, creating customer frustration and potential compliance risks for urgent financial matters. Training and Onboarding Inefficiencies: New agents require 8 weeks of training to reach proficiency, with high turnover (23% annually) creating persistent knowledge gaps.
These limitations are increasingly urgent as competitor benchmarking shows that 7 of our 10 main competitors have implemented AI-powered customer service solutions in the past 18 months. Impact Assessment The current situation impacts our business in several quantifiable ways: Tangible Impacts:
650K in unnecessary escalations and repeat contacts 380K in annual costs related to agent turnover and retraining
Intangible Impacts:
Declining Net Promoter Score (from 42 to 34 in the past year) Increased employee burnout and declining job satisfaction Competitive disadvantage as we fall behind industry service standards Missed cross-selling opportunities due to transactional focus
Cost Overview The proposed solution requires the following investment: Initial Costs:
Software licensing: 175,000 Integration development: 45,000
Ongoing Annual Costs:
Annual subscription/maintenance: 125,000 Ongoing training and enhancement: $50,000
Three-Year TCO: $1,820,000 ROI Calculation:
Three-year cost savings: $5,230,000 Three-year ROI: 287% Payback period: 14 months
Benefits Analysis Implementing this solution will deliver substantial benefits: Tangible Benefits:
70% reduction in average wait times 45% reduction in cost per contact 65% decrease in average resolution time 950K annual savings from reduced customer churn $420K annual savings from reduced escalations
Intangible Benefits:
Improved customer satisfaction and loyalty Enhanced employee experience and reduced turnover Consistent service quality across all channels 24/7 availability for basic service needs Improved regulatory compliance through consistent, documented responses Better data collection for continuous service improvement
Initial Project Timeline The implementation will follow this high-level timeline: Phase 1: Selection and Planning (Months 1-2)
Finalize vendor selection Complete security and compliance reviews Establish project governance Develop detailed implementation plan
Phase 2: Initial Implementation (Months 3-5)
Configure core platform Develop initial conversation flows Integrate with CRM and knowledge base Initial agent training
Phase 3: Pilot Launch (Months 6-7)
Limited customer deployment Performance testing and optimization Agent feedback incorporation Knowledge base refinement
Phase 4: Full Deployment (Months 8-10)
Full customer rollout Comprehensive monitoring Advanced feature enablement Post-implementation review
Project Governance Executive Sponsor: Maria Chen, COO Budget Owner: James Wilson, CFO Project Lead: Sophia Rodriguez, VP of Customer Experience Core Team:
Thomas Wright (IT Integration Manager) Anita Patel (Customer Service Director) David Johnson (Data Security Officer) Michelle Kim (Training and Change Management Lead)
Extended Team:
Representatives from Legal, Compliance, and Marketing Agent Advisory Group (rotating membership) Customer Advisory Panel (for feedback collection)
Next Steps
After completing your business case:
- Share it with key stakeholders for feedback and refinement
- Use it to guide your requirements gathering process
- Refer to it during vendor evaluation to ensure alignment with your objectives
- Update it as you learn more through your solution exploration process
Ready to create your AI business case? Start a new chat with Kowalah and select “Help me build a business case” from the suggested prompts.
Requirements Document Template
Ready to document your requirements list? Use our Requirements Document template to clarify what you need this solution to do.