Cracking the AI code: The recipe for successful human-AI collaboration

Cracking the AI code: The recipe for successful human-AI collaboration

You’ve probably come across those relatable AI memes circulating across the universe of cyberspace right now. “To replace creatives with AI, clients will have to accurately describe what they want. We’re safe, people.”

While the terms “creatives” and “clients” are often swapped for programmers, product managers, and anyone whose job is threatened by AI, the message remains unchanged: rubbish in, rubbish out. We cannot harness AI’s full potential without providing it with a clear and precise brief.

Baking the cake: Understanding what we really want

In 2023, we find ourselves drowning in a sea of data. Estimates vary, but some suggest that we generate as much as 328.77 million terabytes of data per day. It’s a classic case of quantity over quality, and some experts even liken large language models to “ticking time bombs” due to the sheer volume of data.

In this context, it’s crucial to acknowledge that not all data is valuable, and not all data can help us make informed decisions. Before we jump onto the AI bandwagon, we need to understand what we truly desire. Let’s liken this to baking a cake.

To create a cake worthy of a Hollywood handshake, we need three key elements:

The cake: The desired outcome or answer.

The ingredients: The data itself.

The recipe: The program that brings everything together.

What we have here, beyond a very worthy Victoria Sponge, is essentially a procedural program. We possess raw data, the eggs and flour, and we understand the steps needed to achieve our desired outcome. In isolation, AI lacks this recipe. It relies on the outcome we request (the cake) and comes up with its own calculations to bake something edible.

As history has shown, this comes with mixed results.

The benefits of using structured data

At Tiger, we rely chiefly on structured data to empower our clients in making strategic decisions. For example, we delve into deterministic data, which is more accurate as it’s provided by customers. This includes metrics like call length, the number of user licenses, or customer waiting times.

In the telecommunications industry, structured data is far more valuable than unstructured data, which is susceptible to errors. Imagine asking AI to analyse this month’s call data, including unstructured data such as speech. If there’s even one misinterpretation of a regional accent, the results received could be gobbledegook.

At best, this is inconvenient. At worst, it could lead to misguided decisions with catastrophic results, especially in fields like healthcare or insurance.

Deterministic outcomes

Of course, we can apply human intelligence to unstructured data – for instance, gauging employee satisfaction from their tone of voice and general demeanour in video calls. But this is time-consuming and cannot be done at scale.

We use structured data to help our clients make key business decisions, including:

Adoption of tools: quantifying user licences shows senior teams where they could cut costs

Call waiting times: identifying peak times helps our clients to allocate staff resource

Preferred channels: counting how many customers interact via telephone versus live chat shows managers where to invest.

Missed calls: a missed call is a missed sale or opportunity.

This gives our clients deterministic outcomes based on the data available. As humans, we offer the roadmap to get from A to B – how can this data guide us in the overall direction of the business?

To get back to basics, human intervention is indispensable before we can harness the full potential of AI. While having the data is essential, understanding what we want from it is equally crucial. Do we aim to cut costs, enhance customer service, retain staff, or achieve all three?

The value of AI

As time and technology progress, we anticipate more applications of AI in the telecommunications sector. For example, while Tiger doesn’t directly deal with network outages, we already witness AI being utilised to track network congestion and ensure business continuity.

AI’s real potential shines when it comes to processing data at speed, especially unstructured data. While human data mining techniques can be applied to structured data, the merits of voice or facial recognition will become increasingly evident in the years to come. This can aid our clients in areas such as fraud detection, from video to voice calls (with some considerations for regional accents).

The challenges

However, it’s essential not to solely rely on AI or make it the sole guiding principle for business decisions. Ethical considerations, such as how natural language interfaces handle private conversations, are paramount.

This holds particular significance in fields like insurance, finance, or healthcare. We should anticipate increased regulation concerning AI and data privacy in the coming years, and we should be prepared to address customer concerns. Already, two out of five consumers are worried about AI.

This is why we cannot underestimate human intervention. Allowing humans to take the reins ensures that:

We gain truly deterministic outcomes based on knowing what we want from our data.

We apply an empathetic approach to unstructured data, such as reading emotions during video calls.

We respect privacy concerns and keep customers informed about how data is being used.

There’s no denying that AI will empower us to make better decisions in the future. However, just as AI might overlook gluten-free flour or other data nuances, we must not underestimate the importance of human intelligence.

Reach out to our team at hello@tiger.io to discover how Tiger Prism can dive deep into your data, offering far-reaching benefits.