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According to PMI<\/a> South Asia’s Playbook for Project Management in Data Science and Artificial Intelligence Projects report<\/a>, going by the estimate that 55 per cent of DS projects either do not get completed or fall short of their objectives, organisations will collectively waste $54 billion in 2023.
“We estimate that at least $11 billion of this amount can be directly attributed to poor project management practices in AI<\/a> projects. Hence, we infer that effective project management practices can save up to approximately 21 per cent of the total wastage in AI projects<\/a> in 2023,” it said.
A collaborative effort between PMI<\/a> South Asia and the Centre of Excellence<\/a> (COE), Data Science and Artificial Intelligence<\/a> (DS\/AI), of the National Association of Software and Services Companies (NASSCOM<\/a>), the report strives to present a framework with recommendations on resources that organisations can use to build capability for DS\/AI projects and a best practices toolkit to apply to different project stages.
Sunil Prashara, President and CEO, PMI, said, “As DS\/AI projects continue to infiltrate different industries, this playbook will be an important tool for the change-makers managing these projects as they look to maximize the technology’s potential.”
Srini Srinivasan, Managing Director, PMI South Asia, added, “The playbook is an attempt to peer behind the veil of romantic mysticism often associated with AI projects to see how they work, and what makes them tick.”
Lack of “fit for purpose” project management practices<\/strong>
The study indicates that there is a lack of “fit for purpose” project management practices for this nascent field. “A majority of organisations are applying traditional software development methodologies<\/a>, including Agile, in their existing forms to DS\/AI projects, the report said.
However, it said, these projects are fundamentally different from software development projects, thus presenting organisations with some insurmountable challenges.
The research highlights three principal challenges in the current DS\/AI project management practices as “Business teams over-estimate the potential of DS\/AI solutions; Organisations struggle with defining and measuring the project success; and Data preparation typically takes the largest proportion of the time.”
Commenting on the report findings, Snehanshu Mitra, CEO, Centre of Excellence, Data Science & AI, NASSCOM<\/a>, said, “As more balanced views emerge on the utopian versus dystopian future of AI, the focus is now shifting to ‘how’ to develop AI solutions.”
He further said, “AI projects are significant cost and time-intensive, and traditional project management frameworks may not be capable of negotiating the complexities of the workflow. Additionally, we need to explore if a uniform framework can guide the development of DS and AI solutions across organisations (service companies, startups, and GCCs) and use cases.”
Project management practices will help better harness the potential<\/strong>
The report further said that the lack of maturity in project management practices has led to overdependence on high performing talent.
“With increased project management maturity in DS\/AI projects, the dependence on the extraordinary performance of a few “wizards” in the organisation will come down. This will allow DS\/AI solutions to scale up and enable bigger teams to work collaboratively toward more impactful solutions,” it said.
Hence, the study suggests the needs of the hour are project management practices that will help better harness the potential and promise of DS\/AI technologies.
It said professionals who are working in this field will attest to this requirement. “In a survey conducted at a major data science conference in 2018, as many as 85 per cent of data scientists said they believed adopting a better process would improve their results. Our research backs up this contention, with a majority of study participants expressing a strong need for practices that are tailored to DS\/AI projects,” the report stated.
The reason why this playbook caters to both DS and AI projects is that the report views both DS as well as AI projects to be very similar and interconnected as they need similar workflows and foundational knowledge.
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88%的组织报告AI差距实践项目:报告
根据PMI南亚的剧本在数据科学与人工智能项目项目管理报告,估计55%的DS项目没有完成或达不到他们的目标,组织将在2023年集体浪费540亿美元。
根据采购经理人指数南亚的项目管理在科学数据和剧本人工智能项目报告,估计55%的DS项目没有完成或达不到他们的目标,组织将在2023年集体浪费540亿美元。
“我们估计,至少110亿美元的金额可以直接归因于贫困项目管理实践人工智能项目。因此,我们推断,有效的项目管理实践可以节省大约21%的总损耗人工智能项目在2023年,”它说。
之间的合作采购经理人指数南亚和卓越中心(COE),数据科学人工智能(DS / AI)、国家软件和服务公司协会(行业协会),报告力求给一个框架建议,组织可以使用的资源来构建能力DS /人工智能项目和最佳实践工具,适用于不同的项目阶段。
总裁兼首席执行官Sunil Prashara PMI,说,“作为DS /人工智能项目继续渗透到不同的行业,这个剧本将变革者管理这些项目的一个重要工具,因为他们看起来技术的潜力最大化。”
董事总经理的Srini Srinivasan, PMI南亚,补充道,“面纱背后的剧本是为了同伴浪漫神秘主义通常与人工智能相关的项目看到它们是如何工作的,以及是什么让他们做出选择。”
缺乏“适用”的项目管理实践
研究表明,缺乏“适用”这个新兴领域的项目管理实践。“大多数组织应用传统的软件开发方法以现有形式,包括敏捷、DS /人工智能项目,报告说。
然而,它说,这些项目是完全不同的从软件开发项目,因此给组织和一些不可逾越的挑战。
这项研究强调了三个主要挑战在当前DS /人工智能项目管理实践”业务团队高估的潜力DS /人工智能解决方案;与定义和衡量项目成功组织斗争;和数据准备通常花费的时间比例最大。”
首席执行官评论报告发现,Snehanshu Mitra,卓越中心,数据科学与人工智能,行业协会说:“更平衡的观点出现在乌托邦与反乌托邦的未来人工智能,焦点已经转到如何发展人工智能的解决方案。”
他进一步说,“人工智能项目是重要的成本和耗时,和传统的项目管理框架可能不是谈判的复杂性工作流的能力。另外,我们需要探索如果一个统一的框架可以指导DS和人工智能解决方案的开发组织(服务公司、初创公司和厂家)和用例”。
项目管理实践将有助于更好的利用潜力
该报告进一步表示,缺乏成熟的项目管理实践,导致过度依赖高表演人才。
”在DS /人工智能项目中项目管理成熟度增加,依赖少数的非凡表现“向导”组织将下来。这将允许DS /人工智能解决方案,扩大规模,使更大的团队协作工作更加有效的解决方案,”该公司表示。
因此,研究表明小时项目管理实践的需求,这将有助于更好的利用潜力和DS /人工智能技术的承诺。
它说,专业人士在这个领域工作将证明这个要求。“在一项调查在2018年的一个大型数据科学会议上,多达85%的数据科学家表示,他们认为采用更好的过程改善的结果。我们的研究支持了这一争论,多数的研究参与者表达了强烈的需要实践,针对DS /人工智能项目,”报告表示。
为什么这个剧本迎合DS和人工智能项目报告视图DS和人工智能项目非常相似的和相互关联的,因为他们需要类似的工作流和基础知识。
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