AI, ML, and Y.O.U.
Adopt artificial intelligence and machine learning to make smarter decisions and increase your competitiveness.
Once confined to the fevered imaginations of sci-fi writers, artificial intelligence (AI) and machine learning (ML) have in recent years become valuable tools for enterprises.
Driving this increase in adoption has been the rise of unstructured data, and with it, the ability for enterprises to use advanced analytics in order to make smarter decisions.
The three cornerstones of advanced analytics are:
- Predictive analytics, which utilizes vast amounts of unstructured data in order to forecast things like supply chain needs, sales trends, and potential disruptions
- AI models to identify efficiencies, optimize logistics, and deliver better service to customers
- ML models to derive lessons from analytics
In the following pages,
we’ll be diving deep into advanced analytics and how you can adopt tools like AI and ML to gain a competitive advantage. Specifically, we will be walking your through:
Understanding the Future of AI
Before looking at where AI is going, it’s useful to start with how enterprises are already putting the technology to use.
In general, there are currently four motivations for adopting AI:
to efficiently make business decisions, predict customer wants and needs, and guide the creation of new products.
such as booking direct and cost-effective travel, crunching vacation hours, and managing the use of resources better.
Automation of tasks
like data entry, basic natural language processing, and updating records.
by way of intelligent chatbots that can answer questions while reducing the need for always available support teams.
In coming years, it’s expected that an increasing number of enterprises will be embracing AI due to at least one of these motivations.
Automation in particular is expected to see a sharp increase in usage.
Boiled down, this forecast means that going forward AI usage will likely increase as enterprises look to optimize operations, reduce overhead, and stay abreast of the competition. In fact, the same IDC and Forrester research also predicts that AI will in many ways become the next user interface, with more than 50% of user touches being enhanced by computer vision, speech and natural language processing, and AR/VR within the next five years.
One thing to note about these and other forecasts, however, is that for many enterprises—especially those relying on legacy systems—advances in AI will remain out of reach.
Even today, 1990s tech is still pervasive, which means as AI grows in usage, there will likely be a growing divide between those organizations that are able to adopt it and those that are not. That makes starting the AI adoption process now—even if it’s just with little steps like chatbots and simple automation—increasingly important.
To learn more about how you can kick start your own AI adoption, check out our free eBook on the subject.
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Examples of Industries
Impacted by ML
If AI is about putting data analytics to work, ML is about training algorithms to make decisions.
This training requires a substantial amount of data—so much data, in fact, that storing it and then applying analytics and ML models to the amount needed was outside the budgets of most enterprises until the public cloud arrived.
As more and more enterprises have adopted the cloud or hybrid platforms, though, usage of ML has ramped up. For an idea on how it’s being used, let’s look at three industries: healthcare, finance, and retail.
Americans spend trillions of dollars on healthcare each year. With that amount of money moving around, the industry is ripe for fraud.
Through ML, healthcare providers and insurance companies are able to repeatedly run models in a sea of data. They are then able to apply the learning from those models to build out systems to flag potential fraudulent activity.
On a more positive note, ML also helps healthcare researchers predict things such as specific regions that may be susceptible to particular strands of flu, or areas where unhealthy pollutants will increase in the environment.
Given the millions of credit card transactions that happen around the world on a minute-by-minute basis, keeping up with potential errors or crimes such as identity theft means being able to identify and address problems close to instantly.
ML models can arm credit card providers with the ability to determine irregularities based on things like a customer’s previous purchasing habits, locations of purchases, and even times of day purchases are made.
As a result, it’s often possible for credit card companies to realize a customer’s information has been stolen and take preventative measures before the customer has—and they can do it without slowing down the flow of legitimate transactions.
Online clothing retailers such as Stitch Fix are using ML models to develop popular combinations of clothing based on data. They are then able to provide customers with outfits based on their interests, seasonality, fit, and more.
Not only does this deliver more choices to customers, it also helps the company bring personal styling service—traditionally a time-consuming process reserved for higher incomes—to a much wider customer base.
AI Adoption Challenges
But just because an enterprise is in a position to make the adoption doesn’t mean they’ll be able to implement AI technology successfully.
While there are a number of reasons AI adoption fails, four culprits often stand out:
- Storage issues AI requires access to a lot of data in order to be effective. While the public cloud has made storing the necessary information more cost-effective, enterprises relying on cloud-native tech stacks on-premises or at a co-location can easily misjudge the amount of storage they need to successfully implement AI.
- Data quality Results from AI are only as good as the data used to arrive at them. Without proper data processing and pipelines in place, actionable data will be hard to come by and AI models will fail.
- Data organization AI needs to know where to look within data. A failure to properly organize data with sourcing and tagging will only send AI models into a sea of information with no sense of direction.
- Technical maturity Adopting any new technology requires a solid understanding of your own technical maturity. AI is no different. Going into the AI adoption process without knowing what you’re trying to achieve and how you can get there with the technology will only lead to false starts and frustration.
* A Note on AI and ML Bias
While it’s tempting to think of AI and ML as objective technologies, the fact of the matter is that their models are created by humans. The more complex the models, the greater the possibility for bias—not just statistical, but societal.
At the moment, most AI and ML models are not built to address and correct biases in things like income, race, and geography. This can put an enterprise at risk, so it’s critical that AI and ML models are constantly examined and reassessed after they’ve been deployed.
An additional step an enterprise can take is to create an AI behavior forensive, privacy, and customer trust team to monitor models and how they’re being implemented in order to reduce brand and reputational risk.
Overcoming Obstacles to AI & ML
It has been estimated that approximately 90% of ML models dreamed up by data scientists never actually make it into production.
While the percentage for AI initiatives is likely lower, many enterprises still encounter roadblocks on their way to putting the technology to work. The leading causes of this problem are twofold—
- The first cause is that IT teams are often unfamiliar with the software and specialized hardware necessary to deploy AI and ML models.
- The second, and more pervasive cause, is a disconnect between IT and data science. For the most part, IT tends to focus on making things available and stable, while data scientists like to experiment and break things.
While solutions such as Kubeflow exist to help enterprises navigate software and hardware problems, getting IT and data scientists on the same page takes a more holistic approach. This, again, is where understanding your technical maturity before attempting to adopt AI and ML can be critical.
With a thorough assessment of technical maturity, an enterprise can come to agreement on:
- What they are trying to achieve with AI and ML
- How they can address competing business priorities
- A comprehensive roadmap to AI and ML adoption
In other words, IT and data scientists can enter the process on the same page, with each department knowing their role and what their counterparts need to accomplish the enterprise’s goals.
Getting Your Organization Ready for AI and ML
Adopting AI and ML is a process. We’ve already covered the importance of going in knowing your technical maturity, now let’s look at some of the other steps.
Step 1: Identify a use case
Your first AI and ML projects should be at a scale that is achievable. It should also make an immediate and tangible impact in order to bolster the business case for adopting AI and ML.
Step 2: Clean and manage your data
Identify which data sets are suitable for AI or ML workloads, then analyze those sets to determine what information is valuable and what can be discarded.
To cleanse your data, set up scripts or workflows to standardize the flow of incoming data. You will also need to locate gaps in your data and, if need be, work with a third party to fill those gaps.
Step 3: Find the right partner
Most enterprises adopting AI and ML will need help. While bringing in talent is always a possibility, working with a partner is often more cost-effective—especially for those just starting their AI and ML journey.
When looking for a partner, make sure they are able to:
- Help you identify the right project to get your feet wet
- Walk you through and implement the cleansing and management of your data
- Help you ensure you have the right hardware and infrastructure solutions in place to get the most out of AI and ML
Get Started Today
There’s no time like the present when it comes to adopting AI and ML.
To learn more about how you can get started on your journey, download our free eBook The Enterprise Guide to Kicking Off the AI Adoption Process. For help successfully adopting AI and ML, schedule a call with our experts.
Effectively putting data to work in your organization takes more than simply hiring data scientists to crank out models.
Editor's Note: This blog post was originally published on July 2, 2018 and has been updated for the newest trends and information.
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