Getting to grips with the real costs of AI implementation

Just about every type of business you can think of is investigating how they can maximise the potential of artificial intelligence (if it hasn’t done so already). That’s because there are so many ways in which it can be applied, from chatbots and logistics planning to inventory management and data analytics.

But in the frenzied rush to gain first-mover advantage with this emerging technology, some organisations overlook the cost implications of their technology investment. It’s easy to understand how that can happen: senior leaders often face substantial investor and stakeholder pressure to embrace AI, as it has the potential to transform enterprise value. But that doesn’t change the fact that the return on investment from an AI deployment still has to be calculated and justified like everything else.

In this blog, we’ll explore why a range of hidden and unforeseen costs can make that endeavour challenging.

How unforeseen costs become a problem

One of the main reasons that AI has gained so much traction is its ability to generate efficiencies within businesses, whether that’s saving time, money, labour or resources. However, this potential ROI can often be undermined by the fact that AI projects can be intensive in terms of human capital and the technological power required to execute them. In short, there’s no point deploying AI if the effort to do so is more than what can be saved.

But the extent of the cost implications can run even deeper than that.  If the entire picture and long-term usage of AI isn’t taken into account right from the start, then the expense can quickly mount up, both directly in finance terms, and indirectly in the quality and value of the data and applications involved (or lack thereof).

Exposing the full picture

The range of costs that can inhibit the success of your AI deployment are substantial. The impact of them varies depending on the type of set-up you deploy (i.e. single-model or multi-model), and apply whether you’re making use of an existing large language model or building your own.

Data:

 It takes a lot of effort and processing to make data valuable for an AI deployment. For starters, any AI application will need training data, so that it has a starting point from which it can learn - if this isn’t in place from the beginning, it can be time-consuming and expensive to catch up. This data also needs to be managed, processed and engineered, meaning that a data transformation programme is often required, whether that data is static and dynamic. It’s also important to remember that these costs are continuous throughout an AI deployment.

Operations:

A simple question-and-answer pair within an AI application currently costs around $3. This doesn’t sound like much, but if hundreds of thousands of people are asking five questions each, the operational cost of AI can quickly spiral upwards. These costs can also change rapidly as parameters shift, such as making AI output more specific and refined. As AI use is reviewed and optimised in more detail, cost controlling models will emerge.

Privacy and security: 

AI tools can generate risks of data breaches, especially if sensitive information is put into applications that could be accessed in the public domain. Keeping track of what information is hosted and used where, and how security demands around AI evolve, is therefore critical in order to avoid a costly cyber attack.

Transition: 

The AI landscape is moving so quickly that many businesses jumping on the ‘latest thing’, find themselves with a tech stack that is outdated six months later and is expensive to replace. It’s vital for any business exploring AI to have a full understanding of the market before they start, and be sure that their investment will be relevant long-term.

Resources: 

A good AI deployment will need lots of different elements to come together, such as having the right expert team in place, a supporting infrastructure that’s appropriate, and a solid approach to discovery. Investing in getting this right early on can prevent further financial pain down the line.

Steer clear of hidden AI costs with Ciklum

At Ciklum, we’ve seen many businesses take the wrong approach and find that their AI deployment is unsustainably expensive. We help our partners avoid these pitfalls with an end-to-end AI solution that encompasses discovery, strategy, proof of concept, integration, implementation and maturity. Our AI managed service also keeps your bots maintained, and we can advise on whether your use case is better suited to automation, Robotic Process Automation or machine learning.

So if AI represents a step into the great unknown for the finances of your business, we can help you keep that uncertainty and risk to a minimum. Get in touch with our team today to find out more and discuss your specifics.