5 Common Myths About AI Adoption

5 Common Myths About AI Adoption

When AI first came on the scene, only those businesses with cutting-edge tech agendas and well-resourced global enterprises had the budgets, skillsets and breadth of data needed to truly take advantage of it.

Fortunately, as technology and best practice has developed, it’s become far easier for almost all organisations to implement AI and uncover valuable, actionable insight.

But despite this, some of the old perceptions of AI as being complicated, expensive and slow-to-implement still surface. This means that, for many businesses, this technology can still feel out of reach. It’s an unfortunate state of affairs – there’s significant business benefit to be had from adopting AI, sooner rather than later.

So, we want to tackle the most common myths about AI adoption head-on and encourage more businesses to take the first step on their AI journey.

 

Myth 1: AI adoption is expensive

Traditionally, large-scale IT projects have required big investments in both time and money, particularly when data is involved. Where new hardware is required, or there’s significant integration with other systems, businesses expect to spend big.

Combine that with the general hype and futuristic perceptions of AI, it’s easy to see why many businesses would assume it’s beyond their budget.

It’s true that there was a time when AI was only for the Facebooks and Googles of the world, but that’s no longer the case.

Because the tools for developing, training, and monitoring AI are cloud-based, there’s no need for additional hardware. And as long as your data is already in the cloud, there’s little to worry about in the way of integration costs.

Licensing is another area where costs can creep in. BI and analytics software is often license-based, so sharing insights across different business units or departments can be expensive. But with services like AWS, there are no license fees, meaning anyone in the business who needs to can access insights and analytics in the cloud.

 

Myth 2: AI is complicated to integrate

AI is dependent on data – both to train the models and to make predictions going forward. And most businesses will have multiple sources of data, whether it’s from customer-facing systems, backend software, physical equipment or products, or even a third party.

It’s no wonder that AI projects are often perceived as slow, complicated, and expensive.

Fortunately, for many businesses, AI services can sit on top of an existing cloud data platform. If your data is held centrally in a data lake or other cloud storage, there’s no integration required – the data is already in one place, ready to be ingested, analysed, and actioned.

But what if your data isn’t in the cloud? What if it‘s stored in lots of siloed systems across the business?

In that case, it’s a good idea to start with a data migration project, which can be wrapped up in the wider AI implementation. It’s an extra step, but a consultant can help you do this quickly.

Consultants and experts, which are adept at cloud migrations and data analysis, can also help you streamline the whole process. Whether your data is in the cloud or not – take advantage of available expertise to help identify the use cases that will have the best speed-to-value, so you can quickly build a compelling business case for AI.

 

Myth 3: Implementing AI requires in-house technical expertise

It’s true that creating and maintaining AI and ML models does require some data science know-how and technical skill – but you don’t need to have all this expertise in-house.

Toolsets like those from Amazon Web Services (AWS) are very usable, making it easy for anyone in your IT team to give it a go. As more and more businesses expand their in-house data science function, this is often a great starting point.

If this isn’t feasible for your team, or if you get stuck at any stage of the process, an AI consultancy can provide the expertise needed to fill the gap.

Consultancies can provide support across every step of the project – from overall strategy through to model creation and implementation. Maybe you need help identifying use cases and building a business case to get a project off the ground. Or perhaps, using tools like AWS, you’ve built a working model and just need help getting it into production across the business.

Either way, a consultancy is a great option, making AI significantly more accessible to businesses without large data science teams.

 

Myth 4: AI adoption requires perfect data

Data is the fuel for AI and ML models, and these days there’s a lot of it – structured, unstructured, speech, text, images, all with the potential to provide rich insights for the business. And while it’s important to have a good balance of quality and quantity when it comes to data collection, having perfectly formatted data isn’t essential to getting started with AI.

Data needs to be cleansed and standardized in order to be interpreted and analysed – but this is a key step in the development of any data analysis project and there are now multiple tools to help rapidly ingest and standardize data. How the data is formatted will depend on the problem your model is trying to solve. In some cases, you may need to just extract salient elements, so data types are inconsequential – meaning there’s no need for the data to be in any exact format before you get started.

If your team needs help getting data into the best shape and location for the models they’re building, this is another area where a consultancy can help.

 

Myth 5: AI takes a long time to deliver value

Thanks to many years of hype around the potential of AI, it’s easy to feel like AI projects are just technology for technology’s sake. So it’s no surprise that business leaders would want to prove the value of AI quickly – and preferably before investing too much into the project.

Thankfully, the perception that AI takes a long time to deliver value simply isn’t true. It’s much faster now to get a project up-and-running – and therefore, much easier to see the value it’s delivering early on.

A great option, if you really need to prove value fast, is to start small with a specific use case and a core subset of your business data – pertinent to the priorities of your organisation.

In a recent interview for Raconteur’s Future of Data report, Inawisdom CEO Neil Miles explained: “It’s about a sense of discovery – identifying where you can have the best impact. Very quickly businesses can generate insights they never would have been able to do and then decide on where best to invest.”

Keeping a narrow focus makes it easier to measure the impact of AI quickly, identify the best opportunities, and scale-up much more effectively down the line.

 

Adopting AI is simpler, faster and more cost-effective than ever before. By starting with a focused use case, making the most of hands-on tools and calling in experts or consultants where needed, any business can quickly unlock the value of their data with AI.

To take a deeper dive in to the world of data and how businesses are adapting to the global reliance on data analysis to differentiate themselves, read the latest report from Raconteur, The Future of Data.

Jade McFarland
jade@inawisdom.com
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