Simply Explained : Adopting Artificial Intelligence in Business

“Artificial Intelligence” is currently a buzz-word frequently used in business.

But what exactly is it?

Credits to

We normally think that artificial intelligence are machines that are bound to gain consciousness and take over the world someday. But, fret not — we are miles away from having conscious machines taking over the world; let alone conscious machines.

Artificial Intelligence in the Real World

What is within the current reach when it comes to artificial intelligence (AI) are simply systems made up of algorithms and computers that are capable of processing data quickly. With good hardware and good software (i.e., algorithms), AI systems are capable of processing data very quickly and finding patterns in them. Given the right data, they can better inform business decision makers.

At Neurolabs, we recently worked with a client to help them gain insights into their data and build an AI product for their sales department. To ensure that what we are building would be useful and generate productivity gains to our client, we came up with specific use cases for the product. The solution we provided them with, covers two main use cases:

  1. Providing deeper insights on customers and products — which helps their sales team members make better decisions when making a sale.
  2. Providing recommendations on what products to sell to customers — which helps sales team members make quick decisions when communicating with customers.

Indeed, AI is capable of being very smart and quick at helping people make decisions.

Difficulties in Artificial Intelligence

But wait — what’s the catch here?

There are a lot of difficulties to building AI systems. McKinsey and Company identified many hurdles that businesses tend to go through when building AI systems. Here are some of the top hurdles explained, with suggested solutions.

1) Legacy infrastructure and systems

Data is crucial to building AI systems. Old data management infrastructure might be restricting businesses from adopting AI — the good old’ data silos.

Data silos refer to data that is controlled by one department only and isolated from the rest of the organisation. This poses an issue to building artificial intelligence systems — they are only as good as the amount and quality of data that you train your algorithms with.


Consolidate your data management infrastructure and systems. Audit your company’s data collection and management of data. Evaluate data from different departments and find ways to merge your databases. Having more data from different departments will not only provide a better overview of how your company is performing, but also build better algorithms.

And, be sure to look into building an infrastructure and governance system that ensures silos don’t build up again.

2) Costs of AI Adoption

Adopting AI is not cheap. Let’s consider human resource costs first. Talent is like rare Pokémons — difficult to spot, but when you do find them, you want to catch them all. In fact, the UK government recently published a paper to outline a strategy to nurture more AI talent.

Even if you do find talent, it comes with a big price tag. The average annual base pay for AI roles listed on Glassdoor is around 110,000 USD a year (translating to roughly 88,000 pounds or 98,000 euros). Rare and hardly affordable.

On top of that, your business will need to invest in infrastructure and processing power, both needed to build AI algorithms.


Technology companies (like Amazon) are building products which ease the process of building AI for your business. As these services are designed in such a way that everything runs in the cloud, you won’t need to invest much in new hardware.

These services also help reduce your human resource cost. But this does not mean that you don’t need talent at all; it just means that not as much AI talent is needed for your business.

Here are some examples of services that make AI adoption more affordable:

  • Amazon’s Sage Maker
  • Microsoft Azure Artificial Intelligence/Machine Learning Service
  • Google Cloud AI/ Machine Learning Engine

Best of all, they streamline the workflow of building AI algorithms as well — which comes in very handy for your newly hires.

3) AI Strategy

Wait — AI strategy? Two buzzwords together? Indeed, they are buzzwords. But, they are buzzwords that go well together.

Without an AI strategy — your business is at risk of investing in technologies and talent without delivering sustainable value to your business. AI strategy is vital — in fact, a survey by McKinsey and Company quoted that the lack thereof is the largest barrier to AI adoption.

AI strategy ensures that these systems are fully beneficial and integrated into your business practices. Strategic planning for AI involves :

  • Acquiring, tweaking and building internal infrastructure to build AI systems. This could be broken down to human resource related (i.e., organisational culture) and cyber infrastructure.
  • Understanding the potential of these technologies and mapping them to prioritised business cases.
  • Establishing procedures and management structures to allow for managing scope, building AI products/running AI projects and evaluating them before deployment.
  • Identifying and containing the relevant risks and impacts of AI in your business.

Here’s an illustration of what AI planning is essentially alike :

Credits to Gartner Incorporated.

AI strategy is crucial as it not only ensures that value is generated in your business through AI, but also ensures transformation in the organisation happens seamlessly. This ensures business continuity and proper integration into your business’ workflow.

Now that you understand what AI exactly is and what you need to do to adopt it, it is vital for your business to capitalise on this growth.

Adoption of AI is due to produce a lot of productivity gains for your company — in fact, Price Waterhouse Cooper recently projected that over the next decade, AI will contribute approximately 230 billion pounds to the United Kingdom alone!

To ensure that the return of investment on adopting AI is maximised for your business, make sure to strategise before embarking on the transformation journey.

At Neurolabs, we have AI talent to help not only build AI technology for you, but also map AI strategies — ensuring that AI technology is fully leveraged in your business. For more information on the technical side of the solutions we build for our clients, check out our website.

This was proudly written by one of our interns, Hiroki Hirayama.

Using the power of synthetic data to democratise Computer Vision.