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How to Get your Organization AI Ready

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According to a recent survey, companies implementing AI have increased 20% over the last four years. But on the flip side, about 63% of organizations are still yet to deploy this technology. So, what on earth could be holding them back? Is it a budget shortage, lack of talent, or data strategies preventing them from being AI ready?

When it comes to the adoption of artificial intelligence, it’s easy for managers to get caught up in the hype. Everything moves so fast it’s easy to put the cart before the horse. Once there’s talk of AI and robotic automation in the office, everyone wants to implement it as soon as possible and relieve their workers from mundane, repetitive tasks. But don’t be too quick to jump the gun just yet. Some of the smartest companies are just getting started laying the foundation that will make AI and automation tools more useful and efficient. Below are the 5 stones that make up a solid foundation for the adoption of AI.

5 Tips to Being AI Ready

1: Ensure your budget reflects the importance of AI

The board and decision makers are excited to incorporate AI into their corporate strategy. That’s all well and good. But does your budget allocation match this enthusiasm? According to research, there are some very confusing statistics around AI budget. While most companies list Business AI as a top 3 business priority, the same research shows that these companies place spending forecasts for Ai way down on the list of budget priorities.

There’s no way such a situation is going to work. If you’re speaking to IT experts, they’ll tell you that AI projects have incredibly high costs upfront. From the project team of data scientists and engineering to project managers and data infrastructure investments, all these will be high value high cost. As such, your budget needs to reflect the importance of AI to your strategy and placed among the top priorities; not at the very bottom or as an afterthought.

2: Get your data house in order

So, you’ve identified and qualified all the right use cases. You’ve also got the infrastructure ready and all the people you need to deploy all the different phases of your AI project. But is your data house in order? Except for experimental algorithms and online learning, most AIs today are dominated by machine learning that relies on historical data. As such, data is crucial to successful AI deployment.

To get your data ready for AI deployment, you have to answer the following questions. One, is your data attainable? The data needs to be readily available, easily accessible and highly aligned. Next, does it have the predictive power necessary to determine the outcome variables? And finally, is the data suitable for operations? If the answer is yes to all three, then your data house is AI ready.

3: Switch up your hiring practices

Here’s a strategy we’ve seen in numerous companies. Some large companies try setting up a small lab to support their first AI project. They then proceed to staff the lab with junior data scientists. While this might look cheap and attractive at first, it almost always ends in failure. So, what’s the most viable solution?

It’s simple really; you have to hire a mix of talent covering and ever lengthening list of professional titles. Larger enterprises have already learned this lesson, sometimes the hard way. That’s why you’ll find well rounded teams that include data scientists, project managers, machine learning engineers, data specialists and many more. The only major problem companies might face is the demand for these titles because supply is astoundingly low.

4: Be more agile

We’re not talking about speed or flexibility here. In the past, software engineers used to build large systems according to the waterfall method. Don’t retrace methods that were in use over 3 decades ago. Instead, try and follow the agile method applied by today’s engineers.

The agile method works by building, testing, and releasing small pieces of functional performance early on and regularly thereafter. Many AI projects are invariably a return to waterfall. However, the waterfall method pushes any benefits to the end of the project while the agile method could lead to ROI from some parts more quickly. The Agile approach to AI could also help find issues earlier on in the project saving you both time and money.

5: Appreciate the scope of the training data challenge

If you’ve been reading up on AI, then you probably already know about this pain point. Machine learning algorithms require incredibly large volumes of training data. People outside the field of data science literally have no idea just how much data an organization needs to successfully deploy AI.

As such, data scientists are expected to annotate and label training data sets in house and all by themselves. This leads to being enmeshed in a never ending task that doesn’t really put their talents to good use. The wasted effort translates to missed deadlines or worse – dead end projects. So, what’s the solution? Well, you either plan to bring in all the required resources to train your own data or go outside. The required resources include a large labor pool, specialized technology, and proper management skills.

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AI Ready Conclusion

That wraps up our segment on how to get your organization ready for AI deployment. But keep in mind that this list is by no means exhaustive. Implementing artificial intelligence in your processes is a very long and intensive process that requires a lot of care and resources. So don’t be too hasty in setting up the system. Likewise, ensure you implement agile practices as you proceed to catch any problems and pitfalls early on.

If you work in sales and marketing and your company isn’t making use of artificial intelligence, you could be losing out on thousands of dollars in sales every year. To outperform the competition in this AI rich environment, it is of the utmost importance for businesses to start developing deployment strategies while they still have time.

There’s already plenty of research that shows why businesses which commit to getting their workforce to collaborate with AI have more chances of success and long term viability to survive upcoming AI disruptions across various industries. Join the artificial intelligence bandwagon today with AI Sales Assistant systems like Veloxy Engage and Veloxy Mobile which use AI to provide sales teams with relevant information for their next task depending on factors like where they are at the moment and the context of their current objective.

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Samir Majumdar

Samir Majumdar

Samir is the CEO and Co-founder of Veloxy. After spending 20+ years creating corporate systems, boosting revenue, and eliminating inefficiencies, Samir started Veloxy to help sales professionals shorten sales cycles, accelerate pipelines, and close more deals.

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