Unlocking AI's Potential: Overcoming Implementation Challenges
Brief news summary
Many organizations struggle to fully utilize AI, as Gartner reports that 30% of generative AI projects don't progress beyond the proof-of-concept stage. Key barriers include poor data quality, insufficient risk controls, and high implementation costs. A major issue is the siloed deployment of AI, which confines its transformative power by isolating problems and hampering effective data integration. This approach exacerbates data quality issues, duplicates efforts, increases costs, and reduces efficiency. SS&C Blue Prism suggests adopting a comprehensive AI strategy, merging AI with automation and orchestration to enhance productivity, foster innovation, and manage costs. By integrating AI with technologies such as robotic process automation (RPA), process mining, and low/no-code development, organizations can optimize workflows. Tools like natural language processing (NLP) and computer vision assist in managing unstructured data, enabling scalable transformation. For instance, an insurance company increased accuracy by automating mailroom tasks, while ABANCA improved customer service by accelerating response times. Strategic AI and automation integration enables organizations to derive greater value, optimize costs, and maintain a competitive edge.Despite the potential of AI, many organizations struggle with its effective implementation. Gartner research indicates that by 2025, 30% of generative AI projects will fail to advance past the proof-of-concept phase due to issues like poor data quality, inadequate risk controls, and high costs. A significant hurdle is the siloed deployment of AI, which often leads to disconnected data and missed opportunities for transformative impacts. This isolation exacerbates data quality problems and inefficiencies, such as duplicated efforts across departments, thus inflating costs and reducing effectiveness. SS&C Blue Prism highlights the need for a holistic AI approach, integrating AI solutions with comprehensive automation strategies to unlock AI’s full potential.
By using technologies like generative AI, machine learning, NLP, and robotic process automation in tandem with process and task mining and low/no-code platforms, organizations can achieve seamless and efficient workflows. This integrated approach can drive substantial improvements in productivity, speed to market, and innovation while maintaining cost control. Examples of successful implementation include an insurance company that, through process automation, achieved 98% accuracy in mailroom tasks, and ABANCA, a Spanish bank, which automated over a thousand tasks to enhance customer service efficiency by 60%. These cases demonstrate that combining AI with automation and orchestration allows businesses to innovate and maintain a competitive edge. By adopting a strategic, integrated AI deployment, organizations can fully leverage AI's benefits, control costs, and surpass competitors.
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Unlocking AI's Potential: Overcoming Implementation Challenges
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