Intelligence Is Now Free

Automated-thinking is now basically a commodity. So why is realizing value from AI still so tough?

Why Projects Fail When AI Works

Can AI Actually Transform a Business?

Yes! Organizations that have figured this out are seeing real impact. But we all see the other half too - many projects failing to scale beyond pilot. Why?

There are a handful of reasons why any project can fail, this post won’t be a claim to have the one secret.

Related to AI, something I see over and over is an expectation that AI will take a (human-complex) problem all the way to solution. After AI creates something, which might have taken weeks to do manually, there is this this false confidence that the output is ✨ magically ✨ transformational and scalable - changing the way everyone will work.

When Machines Think For Us

What’s missing when we thinking happens for us are the positive side-effects from the time we would have spent empathizing with past events, decomposing variables, etc.

Look - the technology works, and it’s amazing. And, it’s not about proof-reading AI content before it’s published.

By skipping the thinking part, humans miss important secondary learnings that allow us to adapt new information to new problems…something computer-thinking cannot do today either.

This is a large reason why just sticking AI into an existing process and expecting it to “think like a human” rarely returns the results expected.

Contextual Intelligence

The skills mentioned here are often called “contextual intelligence” - the ability to transform complexity into simplicity through merging past events and preferred future state with important current variables (context).

In practice, I’m seeing AI maturity play out in three layers:

1. Information Access & Capabilities (Humans) - creation of tools to speed up tasks, APIs to access data in warehouses, etc. helping speed up what took humans hours or days previously.
2. Orchestration & Scale (AI) - building systems where AI agents work together automatically using information & tools to deliver outcomes
3. Translation (Humans) - often missing - translating outputs from highly scaled, auto-thinking systems into solutions/actions that meet the existing context/need

This last one is critical to realizing value with AI.

Recommendation

Transformation is no longer about access to information or a technology skills gap - it’s a human cognitive skills gap. And ironically, not one that can be solved by users just ‘adopting’ AI.

Organizations looking to rapidly transform need to resist the urge to get caught in technology POCs like measuring AI accuracy. Again, the technology works amazingly well.

The key is adoption and integration . Effort needs to shift towards change management, building a culture of change / critical thinking, and up-skilling employees around skills related to contextual intelligence.