"AI is the new electricity. No industry will go untransformed by ai." Andrew Ng, AI pioneer
Bubble in AI
Some experts now suggest a bubble exists in AI, but in massive funding for large models. Giants like OpenAI spent billions building those models, and investors now question if returns are realistic or if investment is now unsustainable. The acid-test will be consumer adoption over next few quarters.
Enterprise adoption resilient
Despite bubble concerns, experts also believe enterprise adoption of AI and LLMs may stay steady. Focus is shifting toward agentification. Businesses will continue integrating AI because these practical applications remain within reasonable financial limits. Data reveals a disconnect between enthusiasm and impact. McKinsey shows most companies use AI, yet only seven per cent scaled it. MIT research shows most organisations see no returns so far. These reports are an eye-opener insofar as approach of AI integration is concerned.
Check our posts on OpenAI; click here
Co-pilots the first step
Successful AI implementation requires hard engineering. A company cannot simply sprinkle co-pilots onto its operations and expect miracles. Effective implementation involves customising technology to fit business needs rather than relying on generic tools. And that needs CXO commitment, both to building a strong AI team, and to support them for many quarters despite hiccups.
Agentic systems
The future perhaps lies in multi-agentic systems. It seems 2026 will be a pivot year for agentification. This creates systems that work together, moving technology into meaningful and scalable business value. If that picks up in a big way, then there's no looking back.
Summary
AI experts now suggest that while massive investment in large language models may be a bubble, enterprise agentification remains sustainable. Despite current low returns and scaling challenges, true business value lies in custom engineering and agentic systems rather than simple off the shelf tools.
Food for thought
Will autonomous agents justify continued massive investment if current ai returns remain low?
Check our posts on OpenAI; click here
AI concept to learn: Enterprise Agentification
It refers to the shift from isolated AI tools to autonomous, goal-driven
AI agents embedded across enterprise workflows. These agents can plan,
reason, take actions, call tools, collaborate with other agents, and
operate with human oversight. In enterprises, agentification transforms
AI from passive assistance to active execution—handling tasks like
operations, analytics, IT support, compliance checks, and customer
interactions. The focus moves to orchestration, governance, safety
guardrails, and measurable business outcomes rather than standalone
models.
[The Billion Hopes Research Team shares the latest AI updates for learning and awareness. Various sources are used. All copyrights acknowledged. This is not a professional, financial, personal or medical advice. Please consult domain experts before making decisions. Feedback welcome!]
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