Studies show that nearly half of business AI projects are abandoned midway through.

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A study found that nearly half of new business artificial intelligence projects are abandoned midway through.

A recent study conducted by international law firm DLA Piper, which surveyed 600 key executives and decision makers from global corporations, highlights the key challenges businesses face in integrating AI technologies. Despite AI's promising potential to revolutionize various sectors, the journey to successful implementation is fraught with obstacles. This article explores these challenges and provides expert commentary on navigating the complex landscape of AI integration.

Orna Kleinman, Managing Director of SAP's R&D Center in Israel. (Credit: Shai Ezekiel)

The study revealed that while more than 40% of organizations fear their core business models will become obsolete unless they adopt AI technologies, nearly half (48%) of companies that have implemented AI projects Started work they were forced to stop or roll back. The main reasons for these failures are data privacy (48%), data ownership issues and inadequate regulatory frameworks (37%), consumer concerns (35%), the emergence of new technologies (33%), and employee concerns. Included. (29%).

According to Ark Feingold, president and chairman of Commit, a technology company that advises large enterprises on the implementation of AI-powered tools and provides them with AI-based technology solutions, “The survey results are surprising, if not conservative. According to our industry knowledge, the number of companies that have started exploring the implementation of AI tools and ultimately decided to hold off on implementation is over 50 percent.”

However, contrary to the survey findings, Feingold believes that the reasons organizations abandon AI projects are fundamentally different.

“One of the main reasons is the gap between the capabilities of AI-powered tools in their current state of development and the processes these organizations want to streamline, some of which are not yet adequately addressed by tools available on the market. “It's relatively easy to identify the gap even in the early stages of the process,” he said.

“Another reason that has held back the migration of AI-powered tools is the difficulty in integrating multiple areas such as data, cybersecurity, and user interface,” he added. “This is something we at Commit have been doing regularly for many years in other contexts, but people who are not experienced in this area can have difficulties.”

Feingold explained that customer service and support are areas where AI is currently making the most promising improvements and efficiencies.

“Many organizations are already implementing chatbots and using AI to provide efficient and fast customer response,” he said. “However, the situation is still not encouraging when it comes to software development tools, and as a result, their implementation is also lacking. There is a gap that I hope will be highlighted in the coming months and years. will be reduced.”


Orna Kleinman, managing director of SAP's R&D center in Israel, emphasized the fundamental importance of responsive, relevant and reliable data in business AI, where the stakes are significantly higher.

“The consequences of bias, errors, or 'fraud' within a business AI model can be disastrous for a company, resulting in lost revenue, reputational damage, or even society itself,” warned Kleinman. may be affected.” They need to be sure that their data is handled responsibly and securely and that relevant data is taken into account. Generative AI tools must respect and observe data privacy, data ownership, and data access restrictions by design, and only operate in areas where express consent has been given.”

The three “R's” – Relevance, Reliability, and Responsibility

Kleinman emphasized that the three “R's”—relevance, reliability, and responsibility—are the foundation of reliable AI for the business world.”

Based on the study's findings and expert insights, Kleinman points to several strategies that emerge for businesses to successfully integrate AI.

“Developing a clear AI strategy that outlines the vision, objectives, and specific use cases with clear KPIs,” he said. “This strategy should be integrated into the broader business plan to ensure alignment and coordination. Equally important is investing in data governance. Establishing a strong data governance framework addresses privacy and ownership concerns.” removes, including implementing clear policies for data collection, storage and use, and ensuring compliance with relevant regulations.”

Kleinman and Feingold emphasized the importance of fostering collaboration between different departments within an organization. According to him, cross-functional teams can provide diverse perspectives and expertise, leading to more innovative and effective solutions.

“In addition to strategic alignment and data governance, choosing the right AI vendor is also critical. Enterprises should navigate a complex landscape of technology providers, ensuring they meet their specific needs,” Feingold added. Choose a partner capable of fulfilling

According to Feingold, “Google, AWS, and Microsoft platforms are well equipped to deal with data privacy issues.”

“Businesses must recognize that privacy concerns in the context of AI should not be so dire and prevent them from exploring this technology.” He added. “Cloud providers are adept at managing these concerns. The same is true of regulation, which, as it evolves and changes, leaves enough room for businesses to operate without undue risk.”

In navigating the complex landscape of AI integration, enterprises face many challenges and decisions. From aligning AI initiatives with strategic objectives to fostering a culture of innovation, the journey to successful implementation requires careful planning and collaboration.

As Kleinman and Feingold highlight, the stakes are particularly high in the realm of business AI, where the consequences of mistakes can be damaging to revenue, reputation, and even society. As businesses continue to grapple with these complexities, one thing remains clear: AI adoption must be driven by a commitment to transparency, accountability, and ethical practices.

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