AI has its Achilles heel: data

AI does not appear when installing tools and software. It takes planning, and most of all, it requires data. A recent study found that getting the right data to create – and understand – AI and machine learning algorithms is the point at which many organizations fall back.

Organizations face difficulties with data silos, explainability, and transparency, a study Find out among 150 data executives commissioned by Capital One and Forrester Consulting. They say internal, cross-organizational, and external data silos have slowed machine learning deployments and outcomes. The majority, 57% of respondents, believe that isolation between data scientists and practitioners prevents deployments, and 38% agree that they need to deconstruct data silos across the organization and partners. More than a third, 36%, say working with large, diverse, and cluttered data sets is a challenge.

Industry watchers agree that data may be the weak point of AI. He says there is a dearth of data to slow progress Ajay MohanDirector, Artificial Intelligence and Analytics at Capgemini Americas. He explains that this literacy is “an understanding of the value of data and how it is processed and used to generate value”. He notes that the problem for many companies is that they “often lack the right resources, such as data scientists, data engineers, or technology-oriented subject matter experts to consider business challenges and the potential of data to unlock solutions to those challenges.”

In addition, it is often difficult to engage in another data-driven activity, or demonstrate business value or return on investment. “This is also a core competency that many end users lack,” Mohan says. Add to this mix “the challenges of leveraging data from different legacy systems and sources that can make developing truly useful AI applications prohibitively expensive.”

With the lack of data knowledge comes data silos that also prevent AI. “Even if companies have the resources to be educated in data, a big challenge for many large companies is operational silos — business functions, geographic teams or other lines of business operating in isolation from peers within the company,” Mohan says. “For example, many large consumer goods companies may operate dozens or hundreds of brands globally, each with dedicated marketing teams and each benefiting from their own technology framework and processes.”

There is also an “intra-corporate lethargy” to traverse the trend of data literacy, communication, and human skills. “They either invest and don’t see the value or they don’t invest enough time and money in data management systems to make it work,” he says. Krishna Tamanachief technology officer at jobshop. “The prerequisite for effective AI is high-quality data – an area that many companies lack.”

At the same time, unlocking the full data power of AI systems can be annoying, introducing biased and misleading information. “We look at each adoption of new AI breakthroughs through the lens of ethical responsibility,” he says. Peter GordonGlobal Head of AI Product for Hogarth around the world. “We are looking closely at how to protect against misuse and damage that can occur from algorithms that have an inherent bias in the training data. This is more due diligence than an issue, but it will rightly hamper rapid adoption.”

Not everyone is lost, and there are some use cases where the data has been successfully leveraged. “There have been some targeted use cases with the right quality of data that are working well,” Tamana says. “One segment that we see is gaining a lot of traction is customer engagement through conversational AI. The current customer engagement landscape leaves a lot to be desired. With AI-powered personalized conversations, we’ve seen better conversions, retention, and brand recall.”

Having a full understanding of the data required to ensure greater accuracy in the output will open doors to more advanced forms of AI. “The next transformation is generative AI — using data to create new images, videos, headlines, music, and even third worlds that have never been seen or heard before,” Gordon says. “Great experience, and the pace at maturity is phenomenal. We are approaching this with enthusiasm, but caution, to make sure it is ready for widespread dissemination.”

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