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How Do You Know You Are Ready For AI?

There is no doubt that Artificial Intelligence (AI) is transformative for businesses, but it’s not just for the tech giants. AI plays a different role in different industries, from market insights, to process automation, to user experience. As the amount of data collected increases, the demand for systems and tech to ingest, process, and distribute that data has risen. This has driven AI solutions to now be more affordable than ever. But is AI crucial to a business’s success? 

Yes!

Machine learning (ML) and AI are now more than just buzzwords. In the near future, if AI initiatives are not in your business, you will lose business. Today, the AI trailblazers like Google and Amazon, have reaped the benefits, offering next-level, customizable, innovative services for clients and customers. For businesses that move forward with AI, it will stretch across multiple business units performing different functions. Processes can be automated to increase sales efficiency, inventory and fraud detection can happen in real-time, and many others. 

Whether you are just starting out with your AI initiatives or your company hasn’t started that journey, there are a few important things to think about to assess the “AI Readiness” of a company. 

It is important to note that these AI initiatives need to be driven by the line of business and kept in check by a task force with members from different business units. This team should be comprised of CEOs, CTOs who can oversee the process and infrastructure components as well as Engineering Managers, and legal advisors or can oversee the data and ethics components respectively. 

Below we have listed the categories for assessing AI readiness as well as posed questions to allow you to gauge how “ready” your business is.

Process

Like any business transformation, you need to have a clear understanding of the problem and business initiatives to help organize and prioritize your strategy. This identification of AI initiatives within a business is a joint effort between the line of business, engineering, and data science teams. Once the key goals and initiatives are identified and financial constraints are taken into account, a company has a solid foundation with direction.

For review: Have you achieved alignment between the line of business and the engineering teams?

Data

A lot of companies have data or data collection processes in place, to have useful machine learning (ML) models, you need high quality annotated training data. ML models can only be as good as the quality of data you train them with so cleaning and preparing the data is an important part of the ML process. Other things to think about when assessing the quality of the data is it’s relevance to the business initiatives you are working on as well as assessed for any potential biases

For review: Do you have a clear process for achieving quality data that can feed into your machine learning models?

Infrastructure

Another important aspect of assessing AI readiness is the technology infrastructure and company framework. The technology infrastructure consists of the technical platforms a company uses, data ingestion and cleaning tools, and overall system architecture. The company framework includes the people who make up the data science and business development teams. Both aspects that make up this infrastructure are equally important. You need a system that is designed for Big Data analytics that can support the scale and real-time processing you need. And you also need to have an organized team with the manpower and leadership direction to maintain the system. 

For review: Do you have the right technology and the teams to support it?

Ethics

AI is a powerful tool, and with great power comes great responsibility. The technology is advancing at such a rapid pace that it is pivotal to have a framework in place to assess and address the ethical implications of AI. Some important things to think about when designing this framework are reliability and bias of data and algorithms, privacy and security of personal data, transparency in design, and overall accountability. In order to effectively leverage the power and benefits of AI, we must also mitigate the risks it presents ethically. 

For review: Is there clear oversight and methodology for ensuring that AI projects are aligned with the ethical guidelines as defined by the enterprise?


Checklist Assessment:

The above categories all play an integral part in successfully implementing and reaping the benefits of AI. 

Do you have clear answers to the questions posited above?  If so, you are on the right track to making AI a reality in your enterprise.  If not, it is essential to understand the areas of weakness and move quickly to address them.  Companies that are unable to proactively prepare and move towards AI risk becoming irrelevant over the next three to five years.


The above categories offer key points to think about whether you have already started your AI journey or you are looking to in the near future. However, there are some areas that can be more problematic than others. Specifically, in the infrastructure technology for AI and machine learning initiatives. Although AI is a tool for the business, one of the biggest challenges enterprises face today is finding a robust messaging system that allows them to connect and enable Artificial Intelligence. 

Pandio, built on Apache Pulsar, delivers more reliability and durability with less data loss than other distributed messaging solutions such as Apache Kafka. With hundreds of models running across different units, the pandio infrastructure automates your AI. It’s neural network looks at all the messaging within your system with a granular approach. Pandio learns if it’s making the right decisions and makes changes to constantly improve your system performance. Additionally, our innovative storage solution turns the old model of data storage used by messaging solutions like Apache Kafka on its head. Instead of purchasing storage space for all the data, you might use Pandio uses a tiered-storage option instead. Meaning you can keep data within your Pulsar cluster for as long as your data retention policy says you need to, and then offload that data to a cheaper storage option. Not only does this help you decrease data storage costs, but the platform’s other benefits mean you can keep scaling your data and your system without having to hire more people.

As the amount of data explodes, human capital continues to be in short supply, and companies continue to move towards AI to gain a competitive advantage, we believe that the ability to make data available for AI Models will be a competitive differentiator to companies that are serious about operationalizing these technologies.

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