Remember the promises from early 2024? AI was going to automate entire job categories. Every business would have AI agents handling complex tasks. Customer service would be fully automated within months.

Now that we’re approaching year end, let’s do something the tech industry rarely does: check whether the predictions came true.

What Actually Delivered

Coding Assistants

This is probably the clearest win of 2024. GitHub Copilot, Amazon CodeWhisperer, and similar tools have genuinely improved developer productivity. Studies show 25-35% faster code completion for routine tasks.

The important caveat: these tools help experienced developers write code faster. They don’t replace developers or help inexperienced ones avoid mistakes. The productivity gains are real but narrower than the marketing suggested.

Document Summarisation

Summarising long documents – contracts, reports, meeting transcripts – works well enough to be useful. I use these tools daily and they save genuine time.

The limitation: they summarise, they don’t analyse. Getting the main points from a 50-page document is easy. Understanding the strategic implications still requires human judgement.

Enterprise Search

The ability to ask questions of corporate knowledge bases in natural language has improved dramatically. Microsoft Copilot, Google’s Search-like interfaces, and AI consultants Sydney like Team400 have made finding information significantly easier.

This solves a real problem. Most enterprises have information scattered across dozens of systems, and employees waste hours hunting for documents they know exist somewhere.

Voice and Transcription

Speech-to-text has reached the point where it’s genuinely useful for most accents and contexts. Real-time transcription of meetings is now reliable enough to trust.

This enables downstream applications – meeting summaries, searchable audio archives, accessibility features – that have genuine value.

What Underdelivered

Autonomous AI Agents

The vision: AI agents that can complete complex multi-step tasks with minimal human oversight. Book travel, conduct research, manage projects, negotiate with vendors.

The reality: agents work in tightly constrained environments but struggle with real-world complexity. They fail unpredictably, require constant oversight, and can’t handle exceptions well.

We’re probably 2-3 years from agents that can reliably handle open-ended business tasks.

Full Customer Service Automation

Many companies rushed to deploy AI chatbots expecting to dramatically reduce support staff. Some succeeded for simple enquiries. Most discovered that customers hate chatbots that can’t actually help.

The pattern: AI handles tier-1 enquiries well, escalation paths are messy, and customer satisfaction often drops. The best implementations use AI to augment human agents, not replace them.

Creative Content Generation

Yes, AI can generate text, images, and video. No, it hasn’t replaced creative professionals. The output is acceptable for certain applications (social media filler, rough drafts) but lacks the distinctiveness that makes creative work valuable.

More concerning: the homogenisation effect. When everyone uses the same AI tools, output starts looking the same. Brands are realising AI-generated content undermines differentiation.

Enterprise Data Analysis

The promise: natural language queries against databases, instant insights from complex data, democratised analytics.

The reality: works great for simple queries, struggles with complex analysis, and produces confidently wrong answers often enough to be dangerous. Professional data analysts are still essential.

The Quiet Successes

Some less-hyped applications actually delivered:

Fraud detection: AI systems identifying suspicious transactions have improved significantly. Banks report 15-25% better detection rates.

Quality control in manufacturing: Computer vision for defect detection is genuinely good now. Several Australian manufacturers have quietly achieved meaningful productivity gains.

Predictive maintenance: Using sensor data to predict equipment failures before they happen. The ROI is clear and measurable.

Medical imaging: AI assistance for radiologists is improving diagnostic accuracy. This is careful, regulated deployment that actually works.

These applications share common traits: well-defined problems, clean data, clear success metrics, and human oversight.

The Lessons

What separated the successes from the disappointments?

Specificity beats generality. Narrow tools for specific tasks outperformed general-purpose AI. A fraud detection system that’s excellent at one thing beats an agent that’s mediocre at everything.

Augmentation beats replacement. AI working alongside humans delivered more value than AI trying to replace humans. The human-AI combination is greater than either alone.

Boring problems beat exciting ones. The most successful applications were incremental improvements to existing processes, not revolutionary transformations.

Data quality determined outcomes. Applications with access to high-quality, well-structured data succeeded. Those dependent on messy enterprise data struggled.

What This Means for 2025

Based on 2024’s results, I’d advise caution about:

• Agent-based systems handling complex, open-ended tasks
• Full automation of human-facing processes
• AI replacing skilled knowledge workers
• Generic AI tools outperforming specialised ones

And optimism about:

• AI augmenting specific professional workflows
• Continued improvement in search and summarisation
• Narrow AI for well-defined business problems
• AI as component in larger systems, not standalone solution

The Hype Cycle Position

Where are we on Gartner’s famous hype cycle? I’d say we’re entering the trough of disillusionment for general enterprise AI, while specific applications are reaching the plateau of productivity.

This is actually good news. The trough is where serious work happens. Inflated expectations get replaced by realistic assessments. Vendors who can’t deliver get filtered out. Sustainable business models emerge.

The organisations that succeed in 2025 will be those that learned from 2024 – investing in what works while avoiding the traps.

Final Thought

2024 was the year AI stopped being purely theoretical for most enterprises. Real money was spent, real projects were delivered, and real lessons were learned.

The technology is impressive but not magical. The organisations that treat it accordingly – as a useful tool requiring careful implementation – will do well. Those still chasing the hype will continue to be disappointed.

Calibrate your expectations to what AI actually delivered, not what it promised. That’s the foundation for 2025 planning.