eApproval + AI, 2part. What AI Can’t Automate in eApproval Systems: Business Cases

What AI Can’t Automate in eApproval Systems: Business Cases

AI is transforming eApproval workflows, delivering speed, accuracy, and data-driven insights. However, there are critical areas where human intervention remains indispensable. These limitations stem from AI’s inability to process nuanced context, adapt to dynamic scenarios, or navigate complex strategic decisions. Addressing these areas thoughtfully ensures that businesses strike the right balance between automation and human oversight.

Below are detailed business cases where AI falls short, along with practical examples and actionable suggestions to enhance approval workflows.

1. Handling Non-Standard Requests

AI performs exceptionally well with standardized processes, but requests that fall outside predefined criteria require human judgment.

Example: Custom Client Contracts
- A key client requests a procurement contract with terms that deviate from the company’s standard policy. While AI might flag it as non-compliant, deciding whether to approve involves understanding the strategic value of the client and negotiating terms.
- Why Humans Are Needed: AI lacks the flexibility to evaluate requests in light of broader business objectives and cannot negotiate terms with stakeholders.
Suggestions:
- Use AI to flag non-standard requests and provide contextual data (e.g., past client transactions, profitability metrics).
- Establish workflows where flagged requests are escalated to decision-makers with relevant insights for quicker resolution.

2. Approvals Requiring Cross-Department Collaboration

Complex decisions often involve input from multiple departments with different priorities and objectives.

Example: Product Development Budgets
- A proposed product launch requires budget approvals from R&D, marketing, and finance. Each department has its priorities, such as innovation, promotional campaigns, and cost constraints.
- Why Humans Are Needed: AI cannot mediate competing interests or facilitate a compromise that aligns with the company’s strategic goals.
Suggestions:
- Use AI to compile data from all departments (e.g., projected ROI, historical campaign success rates) to present a unified view.
- Create approval workflows that incorporate collaborative tools for discussions and document sharing among stakeholders.

3. Strategic Investments and Large-Scale Approvals

AI is limited to historical data and cannot fully account for forward-looking strategic considerations.

Example: Capital Expenditures
- A company considers investing in a new production facility. AI can analyze current financials but cannot predict competitive shifts or market opportunities beyond its dataset.
- Why Humans Are Needed: Strategic decisions require business acumen, scenario planning, and alignment with long-term objectives—areas where AI lacks intuition.
Suggestions:
- Use AI for scenario modeling and risk analysis to support decision-making.
- Pair AI-generated reports with leadership discussions to evaluate broader implications.

4. Dynamic or Rapidly Changing Business Scenarios

AI models rely on historical data, making them less effective in managing approvals during sudden disruptions.

Example: Crisis Management Approvals
- A supply chain disruption demands immediate approval for alternate vendors or routes. While AI might prioritize cost efficiency, it may not consider the urgency of maintaining operations.
- Why Humans Are Needed: Real-time problem-solving and dynamic prioritization require human adaptability and decision-making.
Suggestions:
- Integrate AI systems with real-time data feeds (e.g., inventory levels, logistics tracking) to improve responsiveness.
- Design workflows where time-sensitive requests are flagged and routed to key decision-makers.

5. Approvals Involving Intangibles

AI cannot assess qualitative factors like brand reputation, employee morale, or customer sentiment.

Example: Marketing Campaign Approvals
- Approving a high-budget marketing campaign involves predicting brand impact and audience engagement. AI might evaluate metrics like past campaign ROI but cannot gauge creative elements or cultural resonance.
- Why Humans Are Needed: Evaluating intangible factors demands human insight, creativity, and contextual understanding.
Suggestions:
- Use AI to simulate campaign outcomes based on historical data.
- Combine AI insights with human-led creative reviews and strategic alignment sessions.

6. Negotiations with External Stakeholders

AI lacks the ability to negotiate or build trust with vendors, clients, or partners.

Example: Vendor Agreement Approvals
- A vendor submits a contract with pricing and delivery terms that deviate from expectations. While AI can flag discrepancies, negotiating favorable terms requires human interaction.
- Why Humans Are Needed: Effective negotiations involve persuasion, relationship management, and context-specific compromises—none of which AI can perform.
Suggestions:
- Use AI to analyze vendor proposals and suggest negotiation points.
- Equip decision-makers with AI-driven forecasts to strengthen their bargaining position.

7. Evaluating Emerging Trends and Innovations

AI excels at pattern recognition but struggles with uncharted territories or innovative ideas.

Example: Investment in Emerging Technologies
- A company considers adopting an experimental AI solution for customer service. While AI can assess cost implications, it cannot evaluate potential market differentiation or long-term innovation benefits.
- Why Humans Are Needed: Evaluating the potential of emerging trends requires vision, creativity, and strategic foresight.
Suggestions:
- Use AI to compile industry trends and competitor analysis.
- Conduct human-led innovation workshops to assess opportunities in context.

8. Compliance in Grey Areas

AI is effective at flagging violations of clear-cut rules, but it cannot interpret regulatory ambiguities or evolving legal frameworks.

Example: International Expansion Approvals
- Expanding into a new country involves navigating complex trade regulations and local compliance requirements. AI might miss nuances in legal interpretation.
- Why Humans Are Needed: Legal and compliance experts must analyze grey areas and recommend actions tailored to business risks.
Suggestions:
- Use AI to provide an initial compliance checklist.
- Incorporate regular reviews by legal teams to address ambiguities.

9. Complex Resource Allocation Decisions

AI can optimize resource allocation based on data, but deciding between competing priorities often involves trade-offs beyond numerical analysis.

Example: Allocating Budget Between Two High-Priority Projects
- A company must decide whether to fund a marketing campaign or invest in product development. AI might calculate ROI but cannot consider strategic priorities or potential synergies.
- Why Humans Are Needed: Decision-makers weigh long-term objectives, market positioning, and organizational focus—factors outside AI’s scope.
Suggestions:
- Use AI for data-driven recommendations and risk modeling.
- Convene cross-functional committees to make final decisions.

10. Escalated Conflict Resolution

When approval workflows stall due to conflicts or competing interests, AI cannot mediate or resolve disputes.

Example: Disputed Budget Requests
- Two departments submit conflicting budget requests, creating delays in approvals. AI might identify discrepancies but cannot resolve the conflict.
- Why Humans Are Needed: Mediation requires understanding priorities, facilitating compromise, and maintaining relationships—skills AI lacks.
Suggestions:
- Use AI to highlight conflict points and provide data-driven insights.
- Establish conflict resolution protocols involving senior leadership.

Conclusion: A Balanced Approach to AI in Approvals

AI has transformed eApproval workflows, automating routine tasks and enhancing data-driven decision-making. However, businesses must recognize the areas where human judgment is irreplaceable. By using AI as a supportive tool and empowering decision-makers with context-rich insights, organizations can achieve a balanced, efficient, and flexible approval process.

To maximize the benefits of AI:

Integrate AI to handle repetitive, data-intensive tasks.
Build workflows that allow seamless escalation of complex cases to human decision-makers.
Continuously refine AI systems with feedback to ensure alignment with business goals.

By combining AI’s capabilities with human expertise, businesses can not only optimize approval workflows but also navigate complexities with confidence and precision.

Posted

January 16, 2025

AI in Business Operations

chilitask

Tags:

AI in eApproval, AI Limitations, Approval Workflow, Business Automation, Business Decision-Making, eApproval Best Practices, Human Oversight in Automation