Rapid AI Insights: Edition 17

Hello,

Welcome to a new edition of Rapid AI Insights. This week, we highlight a CTO's guide to #AI implementation, the role of #data quality and how to address it, and AI guidance for all levels of l#eadership within an organization. Lastly, a major global city has put in place an action plan for AI and its use. Read on to find out which one!

A CTO’s guide to AI implementation

In an organization, it is the role of the CTO to determine the areas where a new technology like AI can be used and how it should be used. In a recent article, insideBIGDATA put together some things a CTO or an equivalent decision-maker can keep in mind while evaluating AI for a company.

1. Anomaly detection: This is a classic area where AI can be of great use, and the right AI rools can rapidly parse a company’s processes—from error logs, to chat logs, to email—and find anomalies in those datasets. 

1. Threat identification: With AI comes newer and more sophisticated threats. But AI can also be used to safeguard systems from hackers, along with human intelligence. Information security teams can begin using AI-powered tools to identify vulnerabilities within the organization, so you can find them before hackers do. 

1. Implementation testing: During an implementation, it can be useful to  form an interdisciplinary task force made up of team members who can bring multiple perspectives to the table. This incorporates the understanding of stakeholders from legal, IT, compliance, security, sales, marketing, HR and others. 

Read the complete guide here. 

Quality data for and using AI

One point that gets stressed over and over again with the use of AI is that the quality of data used is critical for the success of the model. In the simplest terms, it’s GIGO – Garbage In, Garbage Out. The accuracy, quality, and reliability of the outcomes are inherently connected to the accuracy, quality, and reliability of the underlying data.

While many measures can be taken early on in the data lifecycle to maintain, store, and govern it properly, AI and ML can also help to identify and fix quality issues in data that is already available. 

• Duplicate detection
• Outlier detection
• Missing value imputation 
• Data enrichment 

Read this guide in Data Science Central for a deep dive ensuring data quality. 

AI for all leadership levels

It is, by now, well-established that AI is going to be crucial across organizations and for different functions.  It also remains common that AI projects are picked up but yet fail to make an impact or are abandoned after a pilot. 

Forbes put together advice for AI implementation for leaders at different levels, to avoid pitfalls, maximize returns on technology investments and ensure that AI becomes a competitive advantage. Here are some highlights: 

• CEO and senior management team: Set a strategic direction that prioritizes operational improvements and cost reductions that deliver relatively rapid returns.
• Technology leadership: Don’t let sexy transformation projects distract you too much from the day to day. The operation and resilience of the existing business is a top priority.
• Leaders of business units and functions: Be proactive and engaged and represent the needs of the business, employees and clients. 
• External partners: When choosing partners, seek out providers who have demonstrated technological prowess and deep experience working with clients within the company’s industry.

Rapid AI Update: NYC unveils AI action plan

CNN reported that Mayor Eric Adams unveiled a citywide AI “action plan” that pledged to evaluate AI tools and associated risks, boost AI skills among city employees and support “the responsible implementation of these technologies to improve quality of life for New Yorkers,” according to a statement from the mayor’s office.

The city’s 51-page AI action plan establishes a series of steps the city will take in the coming years to help better understand and responsibly implement the technology that has taken the tech sector and broader business world by storm in recent months.

Read the complete article here. 

About RapidCanvas

RapidCanvas is a no-code AI platform for business users to go from idea to live enterprise AI solution within hours, reducing time to value by over 90%, when compared to traditional AI build-and-deploy processes. RapidCanvas creates out-of-the-box AI solutions tailored to your needs using our proprietary AutoAI technology. Our data science experts work with you to optimize the results to your satisfaction; we combine the efficiency of algorithms with the experience of human experts. RapidCanvas work with leaders in financial services, retail, renewable energy, and manufacturing.