AI lacks core human abilities despite rapid advancement
Welcome to this exploration of what's missing from AI systems. Despite remarkable progress, today's AI - particularly Large Language Models - lack several critical capabilities that limit their effectiveness in enterprise settings. This presentation addresses these gaps and provides pragmatic solutions for organizations of various sizes.
Establish your credibility as "The AEM Guy" and digital transformation expert. Use hand gestures to emphasize each missing element. Notice audience reactions to identify which limitations resonate most with this group. Prepare to spend more time on those areas during Q&A
Why This Is Important
The Business Impact of AI Limitations
Understanding AI limits saves money and prevents disasters
Understanding what AI can and cannot do is critical for business success. Without this knowledge, organizations risk: 1) Wasting millions on solutions that cannot deliver promised results, 2) Making strategic decisions based on hallucinated data, 3) Creating reputational damage from AI systems that produce biased or harmful outputs, and 4) Missing opportunities to implement genuinely valuable AI solutions that work within known constraints. This presentation equips you with practical knowledge to navigate these challenges effectively.
Begin with a brief poll about failed AI implementations. Ask: "Has anyone here invested in an AI solution that didn't deliver what was promised?" Use the responses to emphasize the real-world cost of misunderstanding AI capabilities. For executive audiences, focus on ROI and risk mitigation.
The "AI" Misnomer
Terms create unrealistic expectations
"AI" implies intelligence but LLMs just predict words
The term "AI" itself is misleadingly broad, suggesting human-like intelligence when it merely refers to technologies like Large Language Models that predict word sequences without true comprehension. Companies use "AI" as a marketing buzzword, even when machine learning is minimally involved, creating inflated expectations that exacerbate implementation challenges.
Begin by addressing the terminology issue head-on. This frames the entire discussion in terms of realistic capabilities rather than sci-fi expectations. Ask audience members for examples of "AI washing" they've encountered in product marketing. This establishes your credibility as someone focused on substance over hype.
The Fundamental Flaws in AI
Despite impressive headlines about AI capabilities, significant limitations continue to hinder their potential and create risks for organizations.
AI's capabilities consistently fall short of the hype surrounding them. Organizations often invest based on theoretical possibilities rather than practical implementations, creating exposure to disappointment and wasted resources. Understanding these limitations is crucial for developing effective AI implementations that truly meet organizational needs.
Reference recent headline-making AI announcements and contrast with implementation realities. If possible, mention specific examples relevant to the audience's industry. Ask: "Has anyone experienced gaps between AI promises and delivery?" Use their examples to illustrate your points.
The Memory Problem
Groundhog Day: no persistent knowledge retention
No retention across sessions prevents knowledge building
AI systems lack persistent memory across interactions. Each conversation starts fresh with minimal retention of previous exchanges beyond what's explicitly included in the prompt. This creates a perpetual "Groundhog Day" effect where systems can contradict themselves across sessions without any awareness of inconsistency. For organizations seeking to build institutional knowledge through AI, this fundamental limitation presents a significant obstacle to developing the kind of relationship-building that characterizes effective human interactions.
Demonstrate the memory problem with a simple example: "If I told ChatGPT my name yesterday and ask it today what my name is, it won't know." Connect this to organizational challenges like customer service continuity and knowledge management. Mention RAG and vector databases as partial solutions, but emphasize their limitations.
Hard knowledge dates create confident but outdated answers
AI systems have binary knowledge boundaries, with understanding stopping at a specific cutoff date. Unlike human professionals who continuously update their knowledge, these systems will confidently respond based on whatever version of reality existed in their training data, regardless of how the world has changed since. This creates a particularly problematic situation for rapidly evolving fields or any context where current information is essential. For organizations in dynamic industries, these outdated "facts" presented with complete confidence can lead to dangerously misguided decisions.
Check what major LLMs' current knowledge cutoff dates are before the presentation. Prepare industry-specific examples of significant changes since those cutoff dates. Ask the audience to consider what critical information in their field has changed recently that AI systems might miss.
Understanding
Modern web architectures create AI comprehension challenges
AI cannot comprehend headless websites and JS applications
Modern headless architecture separates content from presentation, making it difficult for AI to understand context. AI systems struggle with SPAs and JS-rendered content, creating significant limitations for AI-based analysis. This architectural mismatch means AI systems often have a fundamentally flawed understanding of modern web content, leading to incorrect interpretations and problematic responses.
This is a technical slide, so gauge audience understanding. For technical audiences, go deeper into architecture implications. For business audiences, focus on the practical consequences. Use simplified diagrams to explain the challenge if the audience seems confused.
Structured Data Solutions
Bridge the human-AI comprehension gap
Machine-readable formats improve AI understanding
To improve how AI systems interact with content, organizations should implement: - **JSON-LD**: Creates explicit relationships between data elements using established schemas, making content machine-readable while maintaining human presentation - **llms.txt Standard**: Functions like robots.txt but for AI systems, providing critical context about website purpose, structure, and appropriate use - **AI-Friendly Information Architecture**: Combining these approaches creates dual-channel content that serves both human visitors and AI systems, ensuring consistent information delivery
For technical audiences, be prepared to discuss implementation details. For business audiences, focus on benefits and resource requirements. Have examples of well-implemented structured data ready to share, particularly if relevant to the audience's industry.
Regulatory Considerations
Complex and evolving compliance landscape
Regulations vary significantly by sector and region
Organizations must navigate multiple overlapping regulatory frameworks: - **Data Privacy**: GDPR in Europe, CCPA in California, and global equivalents impose strict requirements on AI data processing - **Sector-Specific**: Financial services (SR 11-7), healthcare (HIPAA), and legal/professional services face additional compliance hurdles - **Emerging AI Regulations**: The EU AI Act, Colorado AI Act, and similar legislation create new requirements for AI applications These frameworks emphasize transparency, human oversight, and explainability—principles often at odds with generic cloud AI services.
Research regulatory developments specific to the audience's industry before presenting. Be careful not to present yourself as offering legal advice. Position this as "areas to discuss with your compliance team" rather than specific recommendations. Allow extra time for this slide if regulatory professionals are in the audience.
The Rise of Agentic AI
Autonomous systems require greater control
As AI becomes more autonomous, control becomes critical
AI is rapidly evolving from passive tools to active agents with increasingly autonomous capabilities: - **Agentic Systems**: Modern AI increasingly operates as an independent actor, making sequences of decisions with minimal human oversight - **Model-Context-protocol (MCP)**: AI systems now generate and execute their own code, expanding capabilities but also creating unpredictable behaviors - **Agent-to-Agent (A2A) Architectures**: Multiple AI agents now collaborate without human mediation, forming complex emergent behaviors - **Critical Business Risk**: These developments create a rapidly growing need for robust, reliable systems under organizational control As AI evolves from passive tool to active partner, businesses that lack control over their AI infrastructure face exponentially increasing risk.
This slide bridges between AI limitations and the solutions that follow. Emphasize that agentic capabilities have real business utility but require proportionally greater control mechanisms. Have 1-2 examples ready of automation improvements from agentic systems alongside potential risks. Use concrete examples like "an AI agent deciding to email your entire customer database without approval" to illustrate why control matters.
Local Deployment of Foundation Models
Complete control and data sovereignty
Public LLMs cannot be controlled by businesses
Public cloud-based LLMs leave businesses with no control over model behavior, content filtering, or update schedules. When providers change their models, your applications can break without warning. Local deployment provides several critical advantages: - **Complete Control**: Organizations select specific models and manage versions according to their schedules - **Data Sovereignty**: Train with proprietary data without third-party exposure - **Reduced Dependencies**: Eliminate reliance on external providers whose terms, pricing, and capabilities may change unexpectedly - **Fixed vs. Variable Pricing**: Convert token-based costs into predictable capital investments - **Enhanced Compliance**: Implement governance frameworks tailored to specific regulatory contexts This approach requires greater technical expertise and infrastructure investment but provides unmatched control.
Be honest about the technical and financial requirements of local deployment. Have approximate cost figures ready for different organization sizes. If the audience includes smaller organizations, acknowledge that this approach may not be feasible for them and preview the pragmatic pathways slide. Emphasize the critical business risk of building on platforms you cannot control - cite examples of API changes that broke applications or content policy shifts that affected business use cases.
Foundation Models as Better Building Blocks
Higher-quality training data reduces hallucinations
Domain-specific training improves accuracy and relevance
Business-focused foundation models employ transformer architectures similar to consumer models but with critical differences: - **Higher-Quality Data**: Trained on business-relevant datasets rather than the entire internet - **Customization Potential**: Can be further trained with organization-specific information - **Reduced Hallucinations**: Domain-specific training grounds the model in factual information - **Value Alignment**: Can be specifically trained to embody organizational ethical frameworks and priorities This layered approach creates AI that truly understands organizational terminology, processes, and values.
Use an analogy like "Foundation models are like prefabricated housing components - they give you a starting structure that you can customize to your specific needs." Be prepared to discuss which foundation models are most appropriate for different use cases. Have examples of successful domain-specific implementations if possible.
Realistic Assessment
Significant investment beyond software costs
Hardware investments range from $3,000-$15,000 per user
Implementing AI requires substantial investment: - **Hardware**: $3,000-$15,000 per knowledge worker for inference; $60,000+ for training machines - **Expertise**: Specialized AI knowledge remains both essential and expensive - **Data Preparation**: Most organizational data requires significant work before AI training - **Ongoing Maintenance**: Models need regular monitoring, updating, and retraining These costs create particular challenges for smaller organizations with limited resources.
Have specific cost examples ready for different implementation approaches. Be prepared to discuss ROI calculations and payback periods. If finance professionals are in the audience, they may ask detailed questions about cost structures and depreciation. Emphasize that many organizations underestimate the total cost of ownership.
Conclusion
Enhanced capabilities through synergy
Human-AI partnership outperforms either alone
The future of organizational AI lies in thoughtfully integrating these technologies to enhance human capabilities rather than replace them. By acknowledging current limitations while working to overcome them, organizations can build AI implementations that genuinely advance their missions. This requires realistic expectations, appropriate investment, and continuous alignment between AI capabilities and business requirements.
End with an inspiring note about the potential of human-AI collaboration. Avoid both excessive hype and excessive pessimism. Invite audience members to connect after the presentation to discuss their specific challenges. Have business cards or a QR code ready for follow-up connections.
This presentation has covered the key limitations of current AI technologies: - **Terminology Problems**: AI creates unrealistic expectations - **Hallucinations**: AI confidently fabricates incorrect information - **Data Issues**: Training data limitations perpetuate biases - **Knowledge Cutoffs**: AI lacks current information - **Cultural Biases**: Systems reflect specific worldviews - **Technical Limitations**: Memory and comprehension challenges \n\nThe solutions we've explored include: - **Structured Data**: Making content AI-readable - **Local Deployment**: Controlling your AI infrastructure - **Domain-Specific Models**: Building targeted AI capabilities - **AI Content Architecture**: Creating new organizational roles - **Pragmatic Implementation**: Aligning approach with resources \n\nBy implementing these solutions strategically, your organization can navigate AI's limitations while leveraging its genuine strengths.
This final slide reinforces key takeaways and ensures the audience leaves with actionable insights rather than just concerns. Emphasize that understanding limitations is the first step to effective implementation. Invite audience members to request the full slide deck via email for reference after the presentation. End with an invitation to continue the conversation about their specific challenges.
