Generative Artificial Intelligence, also known as Gen AI, is a byproduct of artificial intelligence technology that can produce various types of content, including text, imagery, audio, and synthetic data.
Many organizations are already realizing the material benefits from Artificial Intelligence (AI), Gen AI, and Machine learning (ML) usage in both decrease in cost and increase in revenue when the algorithms and models are used accurately, meaningfully, and ethically. Organizations have also become aware of the potential risks and downside of AI usage, most notably, inaccuracy and unproven predictability.
Aires’ AI solutions are geared toward creating digital assistants for the users of our system and providing solutions to help end users make data driven decisions in their area of work. Our AI solutions are keeping the human-machine interaction and collaboration with a focus on our core tenets of People, Process, and Technology. Our solutions are not meant to replace humans as their cognitive knowledge and work experience is far more valuable in providing better customer service. Personalization, content summarization, predictive analytics, and conversational communication through chatbots are among other potential applications that we continue to explore and develop our solutions for.
However, it is important and critical to point out that the effectiveness of any smart automation and that of AI or ML technology-based solutions is predicated on the base data quality, consistency and reliability.
SolarWinds, which is a leading provider of simple, powerful, secure observability and IT management software, recently released the findings from their “2024 IT Trends Report”. This report, based on a survey of nearly 700 IT professionals about their views on artificial intelligence (AI), found that despite a near-unanimous desire to adopt AI technology, very few respondents have confidence in their organization’s readiness to integrate AI, pointing to limitations in data and infrastructure and security/privacy concerns.
Security and privacy aren’t the only AI concerns IT professionals have. They also have uncertainty around data quality—and when the business is responsible for the output of its AI systems, quality really matters.
Overall, the industry’s sentiment reflects cautious optimism about AI despite the obstacles. Almost half of IT professionals (46%) want their company to move faster in implementing AI despite costs, challenges, and concerns, but only 43% are confident that their company’s data can meet the increased needs of AI. Moreover, even fewer (38%) trust the quality of data or training used in developing AI technologies.
Of the IT leaders and team members who have had negative experiences with AI, 40% point to algorithmic errors as a key factor. These errors tangibly hold organizations back from AI implementation, too—respondents said that poor data quality is the second most significant barrier (16%) to successful AI integration.
When AI models are trained on poor-quality, unhygienic, biased, or insufficient data, the result can be error-prone AI decisions and low-quality output. Solving the problem of low-quality AI output, of course, starts with its input—a model is only as good as its training data.
Aires, as a technology leader in the global mobility industry, is dedicated to this role by setting continually improving standards. We focus on maximizing human, technological, and environmentally friendly resources to effect innovative change that exceeds our customer’s expectations, further leveraging the potential of Generative AI.
To establish repeatable, predictable and quality data creation, maintenance and its application for ongoing AI and ML initiatives at Aires, it is taking AIM at its data as and when it is generated at its source in multiple systems. Aires successfully handles more than 40,000 moves of its transferees in a year on an average scale. This results in tremendous amount of mobility journey related data that Aires is focusing on to improve its Accuracy, Integrity and Meaningfulness.
A companywide initiative, aptly named Aires AIM (Accuracy, Integrity, and Meaningfulness) is making sure that the foundations of data engineering are continued to be enhanced in their infrastructure to get the most benefits from Aires AI and ML initiatives.
Aires AIM on Data ensures...
- Data Cleansing: Including removing duplicates, correcting errors, and identifying missing values.
- Data validation: Having well-defined quality criteria such as range checks, pattern checks, and uniqueness validations.
- Anomaly detection to find outliers and anomalies in the data and to correct it at its source
- Data provenance for data auditing and governance.
Aires is continuously working on delivering exceptional business value to our clients by providing both “shaper” type of high performing solutions and “taker” type of COTS (commercial off the shelf) solutions that are embedded within Aires’ Technology Suite and will be available through the intuitive user interfaces of solutions like Aires Mylo and Aires MobilityX. These Aires solutions will continue to be effective, reliable and scalable with the focus on AIM at data, ensuring the Accuracy, Integrity and Meaningfulness of data.
For more information on Aires AIM initiative, please contact your Aires representative or get in contact with us here!
Reference:
“What is generative AI? Everything you need to know” by TechTarget. All rights reserved and acknowledged.
“A guide to artificial intelligence in the enterprise” Jan 2024 by TechTarget. All rights reserved and acknowledged.
SolarWinds 2024 IT Trends Report (https://it-trends.solarwinds.com/) All rights reserved and acknowledged.