The Essential Ingredient For New Tech To Succeed

Every new technological innovation seems to unearth the same question throughout history.

Is this the end for humans?

It’s a tale as old as time. From the creation of the printing press to the attack of AI chatbots, it seems like we’re constantly on the brink of destruction.

There’s a huge plot twist here though.

It’s easy to think machines can do it all. But the real magic happens when humans are in the loop. If you’re worried about robots taking over, don’t be.

Let’s unpack how humans (including you) are the essential ingredient it for tech adoption success.

What is the ‘human in the loop’?

If this was one of those Marvel films, this would be the time when the superior spandex laden superhero appears to save the day.

You might have heard of the concept ‘human in the loop’.

It’s commonly used to describe the essential human involvement required with any technology. Did you think all of those cool tools worked on their own?

If you haven’t, the term refers to human input into the development, training, and operation of AI systems. It’s about collaboration between man and machine, not one or the other. I believe this is the best way to work with these tools.

That’s why when I’m asked “Will x take my job?”, I reply “It depends”.

It depends if you’re building a human in the loop (HITL) with AI assisted tasks, and the answer is – you should!

The HITL approach leverages that collaborative approach I mentioned to improve accuracy, reliability, and adaptability of tech tools. You (the human) are the key ingredient in working with any technology. If you’re human skills suck, AI and other tolls won’t help you much.

As humans, we provide key context.

Tools like Generative AI can do many wonderful things but it can’t apply those contextually.

Not right now, anyway.

So, if you’re sitting their worried about AI taking your job – Don’t.

Until SkyNet rises and starts building Terminators, you have a clear place in the flow of work.

Why do we need ‘humans in the loop’ with technology?

Maybe you’re not quite sold on this concept.

Here’s where humans enhance the tech partnership:

1. Accuracy and reliability
2. Context and understanding
3. Ethics and accountability
4. Continuous improvement
5. Trust and adoption

Without you, technology can’t benefit from any of this.

That means it’s not much use in the long-term.

Humans and AI collaboration case studies

Talk is cheap without action.

To honour that, here are 3 examples where a human in the loop with AI tools creates performance improvements for both.

🩺 Healthcare

When it comes to medical imaging, like the scans doctors use to spot things like tumours, AI is a powerful tool.

But it’s not perfect on its own.

That’s where human expertise comes in. Radiologists work alongside AI to double check and refine anything a AI tool discovers. This partnership ensures that diagnoses are spot-on because, let’s face it, in medicine, there’s no room for error.

This has been common practice for sometime and generative AI models have only enhanced this partnership.

🌾 Agriculture

In the farming world, Hummingbird Technologies is a great example of human and AI teamwork.

They use drones and satellites to collect images of crops, but it’s the human experts who make sense of this data. Initially, data scientists manually annotated images to train their AI models. Later, they outsourced this task to a dedicated HITL workforce, allowing their in-house team to focus on model development and optimisation.

This approach not only sped things up but also made the predictions more reliable, helping farmers make better decisions.

Win!

🚗 Self -driving cars

Probably once of the most talked about innovations this decade.

They’ve not quite landed yet.

With self-driving cars, accuracy is everything, and that’s where humans come in. Developers use a HITL to process the massive amounts of data these cars generate like video and sensor inputs.

Human annotators review and correct the AI’s work, especially in tricky situations where the AI might miss something.

This collaboration is critical to making sure these cars are not just smart but safe on the roads. If you’ve seen any of the horror stories where these innovations have gone wrong, you know how important this is.

The irreplaceable human element

I get its hard to see this when social media is ablaze with inflated stories.

Most people use Gen AI tools for creation. That’s less than 5% of their potential in my eyes. In reality, they’re overall potential is greatly untapped.

I compare the current state of AI use for work to giving a Ferrari to a 5 year old. People don’t have the skills, experience or know-how to use it effectively.

That will change.

We’re talking years here not days. I keep going back to this image from Oliver Wyman with the scaling model for AI adoption and ROI. Time is on your side.

Humans are here to stay

I read the same fear-filled headlines you do.

I don’t believe them, and you don’t have too either. Want to get the real answer to all of this? Then take time to experiment and research. I think you’d be surprised by what you discover.

I’ve written extensively on where my fellow industry practitioners will always add value no matter the technological innovation.

I see the same case for most industries.

That’s not to say I’m blind or foolish to the fact that some industries, and thus jobs will be reshaped. This is the nature of life.

5 ways to bring ‘humans in the loop’ in your technology projects

This mostly won’t happen overnight.

Here’s how you can bring an intelligent human approach to your collaboration with technology.

1/ Start Small

Involve humans in tasks like data labelling and quality control. In L&D, this could mean using human reviewers to validate AI-generated content or training materials.

Your goal is to ensure that the information you feed tools to enhance your work is accurate and relevant. Getting this right from the beginning is of the utmost importance.

Start as you mean to go on, as ‘they’ say.

2/ Leverage human expertise for continuous improvement

Hopefully a no brainer but I have to call it out just in case.

Use feedback loops to review and refine AI outputs. You’re sitting on a wealth of data with AI generated outputs. Get clear on what’s good, bad and downright ugly. Make the improvements needed.

Take a page from the book of our friends in product.

Introduce retros to each AI-assisted project to scale the performance of your collaborations. Good work takes time. Its not about getting it perfect from the start.

3/ Focus on contextual decision-making and independent thinking

The keyword here is ‘contextual’.

AI is not so good with this, not unless you’re awesome with prompting context rich tasks to tools. Sadly, most people don’t do this.

Instead, encourage fellow humans to apply their judgment in situations where context is key. AI can suggest solutions, but humans should make the final call, especially in nuanced scenarios like personalised learning paths.

You have the context. AI is like the Robin to your Batman with decisions.

4/ Ensure practical ethical oversight

You and I aren’t going to solve the full scope of the ethical dilemmas with AI.

Yet, we can establish meaningful guidelines and checks to prevent the list of issues we’d rather avoid in AI outputs.

Human oversight is crucial for ensuring that AI recommendations align with your ethical standards. It can boiled down to what goes in is what comes words. In other words, crap data inputs = crap data outputs.

Focus on quality not quantity.

5/ Invest in digital intelligence

If you’ve read my work for sometime, you’ll know digital intelligence is one of the most underrated skills I endorse.

We live in an increasingly digital world at work but many people can barely operate their email app. It’s concerning.

It’s a no-brainer once more, but you must provide your team with the necessary support to effectively collaborate with AI tools.

This should focus on understanding how AI works, its limitations, and how to leverage it to enhance their work rather than replace it. After all, with great power comes great responsibility.

Final thoughts

• Humans (you) are the secret ingredient to successful technology collaboration.
• The human touch remains irreplaceable in making contextual decisions and providing ethical oversight with AI.
• Start small, leveraging human expertise for continuous improvement, and promoting contextual decision-making are key ways to bring humans into the loop with technology.
• Embrace digital intelligence and investing in teams so they can effectively collaborate with AI tools to enhance their work rather than replacing it.

The future is always human-powered!

Before you go… 👋

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