How to make AI work for your business, today

The productivity gains offered by Artificial Intelligence (AI) are huge. In fact, PwC estimates the global economic value to be upwards of $15.7 trillion over the next decade. This makes AI the biggest commercial opportunity in today’s fast-changing economy.

Applied properly, AI can transform how a business operates, its products and services, and its revenue potential. Yet, many organisations have been slow to respond to the AI revolution and risk being left behind.

As humans and machines collaborate more closely and AI becomes a reality of “doing business”, now is the time for adoption, no matter the size of your organisation, industry or IT budget.

Don’t delay; here’s how you can make AI work for your business, today…

Defining AI

In the broadest definition of the term, AI is a collection of intelligent computer systems which have the ability to sense their environment and action a response, much faster than a human would be able to do (if at all), while constantly learning and refining its decision-making process.

In the past, AI has been the stuff of Hollywood-inspired nightmares. In some cases, it caused the end of human-kind.

While AI application is still in its infancy, its already widely considered to be a game changer, demonstrating its ability to amplify human capabilities and make decision-making faster, easier and more accurate.

In truth, AI represents both a threat and an opportunity. It’s re-writing the rules across industries, making it possible for the start-ups of tomorrow to leapfrog their more established counterparts and become the market leaders before anyone’s even looked up.

Laying the groundwork

No sector is immune to the disruptive forces of AI. However, using the same premise, every business can capitalise on it. The big question is how to leverage it and make it work for your business.

AI, when applied to business, can mean anything. However, in all cases, AI is underpinned by data…and lots of it. That’s why laying the groundwork before you invest in AI is vital.

The first and perhaps the most important step on your AI journey is to make sure your business has “clean” and well-organised data structures, otherwise, any investment will be worthless.

In many organisations, data is still extremely fragmented and this needs to be addressed before AI enters the equation. Duplicate and incomplete data is polluting storage, holding back progress and wasting money.

Data cleansing needs to be a priority, which includes developing a data quality plan across all departments to set expectations for your data and understand the root cause of data quality errors.

It’s also important to make sure data is standardised at the point of entry which can be achieved through creating a standard operating procedure (SOP) and that data is pooled across an organisation in one place to eliminate fragmentation issues.

Targeting your investment

Once you have a solid foundation upon which to build your AI strategy, you’ll next need to decide what you want to achieve in order to target your spending and gain maximum ROI.

As with any business spend, simply throwing money at AI won’t solve any problems. At this point, you must sit down and work out what AI means for your business, seeking points of view from staff on current bottlenecks, competitive pressures and changing consumer behaviour. This will give you the insight to identify operational pain points AI could help address, today and in the future.

Dependent on your sector, the areas of AI’s biggest potential will differ, as will the barriers to overcome. For example, in healthcare AI is already helping to support diagnosis, appointment scheduling and identify pandemics to track and contain its spread, with the longer-term potential of “robot doctors”. However, the biggest hurdle to overcome is how AI uses and protects patient privacy and sensitive health data.

Another example is in the retail sector, where AI offers the opportunity for brands and retailers to provide personalised products and experiences; keep ahead of consumer demand through predictive analytics and drive efficiencies across the supply chain. In future, AI will help the retail industry to create products that anticipate demand before customers know what they want.

“To buy or to build?”

The next step in your AI journey is to decide the best way to build your AI capabilities: to build or buy? The right choice will depend on your in-house capabilities and it may be a hybrid approach will work best for your business.

AI talent is in high demand in the digital economy, meaning hiring inhouse AI talent is expensive. It can also take many months, if not years, to build a well-performing AI solution because a system running off billions of data points takes time to fine-tune.

If you lack internal know-how and need an AI solution in the near-term, buying AI as a service from a vendor can bring immediate financial and customer satisfaction benefits via a market-ready solution that can propel your organisation ahead of the competition.

The golden rule of outsourcing any kind of business function is to be clued up on the current market and value of different offerings so you can strike the best deal. If you go in blind, it’s likely you’ll pay over the odds for something you don’t necessarily need or risk being locked-in.

Thinking longer-term, every business should keep open the possibility of building their own internal AI solutions, particularly when it comes to mission-critical projects. If you consider your company to have an exclusive and highly-value data set, building an in-house AI model will help to protect your competitive edge as off-the-shelf AI models are tested, trained and calibrated using your (and many other companies’) data sets.

However, whether you choose to buy or build AI, it’s important businesses start small and constantly review and learn from their mistakes. In this dynamic environment, AI is evolving at a faster rate than many other commonly used business technologies and as such, strategies, training and vendor relationships need to be a constant state of flux too.

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