In a recent episode of our Device Squad podcast: AI in the Enterprise, my colleagues Steve Brykman, Michael Golub and I discussed everything from AI’s evolution to its value in the enterprise to potential hurdles companies face implementing AI. We covered a lot and it is definitely worth a listen! However, in this blog post I want to elaborate more on the key steps to implementing enterprise AI, including adoption hurdles, and recommendations on how organizations should prepare for implementing AI-specifically given what we’ve learned helping clients with their AI initiatives recently.
As discussed in our podcast, plenty of organizations across multiple industries have already invested heavily in AI and are reaping its benefits, while many others have just begun the journey. If you’re one of those organizations that has yet to dip its toe in the water, you’re in luck. It’s still very early in the Enterprise AI story, and now you can learn from the challenges and lessons of others.
But before we share the key steps to take when implementing AI, let’s take a moment to look at some of the challenges:
Lack of Data Access. Data is the ingredient most critical to AI readiness. Nearly every organization has tons data across disparate systems. The challenge is ensuring you can access your data at a granular level. For example, if you are a manufacturing company with years of data spread across different systems (e.g. ERP, MRP, CRM, etc.) you will need to be able to access this data-and more importantly the right data-with granularity down to the daily transactional data level (e.g. SKU locations, orders, customer information, etc.) in order to leverage AI successfully.
So, step number one: be sure your data collection and storage mechanisms are easily accessible and can support highly granular data.
Lack of Infrastructure. Not surprisingly, Machine Learning (ML) and Deep Learning (DL) require serious processing power. Most companies turn to cloud computing and massively-parallel processing (MPP) systems to solve this challenge, but these are short-term solutions. Here’s why: as data volumes continue to grow, and ML and DL drive the automated creation of increasingly complex algorithms, the bottleneck will continue to slow progress.
So, in addition to making sure you have the right data, you need to be sure the data is ready for AI algorithms to digest, and that you have the ability to process the algorithms quickly.
However, we should also note that GPU—not CPU—advancements are what’s behind ML’s recent proliferation. Utilizing different processing architectures that scale more easily than traditional servers. Not surprisingly, Google and FB are both investing in custom GPUs to optimize performance for this specific type of process (Google calls them TPUs or “TensorFlow Processing Units”).
Lack of Talent. Until now, only a handful of organizations were willing to invest in developing the skills necessary to implement ML-given the relatively few AI use cases in the enterprise. But with the explosion of interest in the last few years, all this has changed. Data science courses on AI development have become prevalent and are generally over-subscribed.
Now that we’ve highlighted the three primary challenges organizations have faced when it comes to implementing AI, here are the key steps we recommend for implementing AI in your enterprise. In short, don’t be caught lacking!
In summary, before your organization can start leveraging the value of AI, make sure you’re ready to overcome the challenges of implementing AI—lack of IT and security infrastructure, lack of talent and AI knowledge, and the biggest barrier-accessing the data itself.
If you haven’t given AI much thought, now is a great time to start. Many industry-leading companies have successfully implemented and deployed AI and mobile solutions-by first developing strategies to leverage these emerging technologies, and ensuring they were aligned with business drivers. Are you fascinated by all that Machine Learning can do for your business, but don’t know where to start? Our Emerging Technologies Kickstart may be just the boost your business has been looking for. To let Propelics help your organization develop a strategy aligned with your business drivers and guaranteed for success, simply reach out. We’d love to get you started.
Necessary cookies are absolutely essential for the website to function properly. These cookies ensure basic functionalities and security features of the website, anonymously.
|cookielawinfo-checbox-analytics||11 months||This cookie is set by GDPR Cookie Consent plugin. The cookie is used to store the user consent for the cookies in the category "Analytics".|
|cookielawinfo-checbox-functional||11 months||The cookie is set by GDPR cookie consent to record the user consent for the cookies in the category "Functional".|
|cookielawinfo-checbox-others||11 months||This cookie is set by GDPR Cookie Consent plugin. The cookie is used to store the user consent for the cookies in the category "Other.|
|cookielawinfo-checkbox-necessary||11 months||This cookie is set by GDPR Cookie Consent plugin. The cookies is used to store the user consent for the cookies in the category "Necessary".|
|cookielawinfo-checkbox-performance||11 months||This cookie is set by GDPR Cookie Consent plugin. The cookie is used to store the user consent for the cookies in the category "Performance".|
Functional cookies help to perform certain functionalities like sharing the content of the website on social media platforms, collect feedbacks, and other third-party features.
Performance cookies are used to understand and analyze the key performance indexes of the website which helps in delivering a better user experience for the visitors.
Analytical cookies are used to understand how visitors interact with the website. These cookies help provide information on metrics the number of visitors, bounce rate, traffic source, etc.
Advertisement cookies are used to provide visitors with relevant ads and marketing campaigns. These cookies track visitors across websites and collect information to provide customized ads.
Other uncategorized cookies are those that are being analyzed and have not been classified into a category as yet.