Artificial Intelligence (AI) Strategy
Learn how to blend business, data, and AI, and set goals to drive success with an effectively scalable AI Strategy.
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Course Description
Discover the Cornerstones of AI Strategy
You must have heard of various strategies such as business, data, and AI and would be wondering how they are connected. Is there a suggested order that shows which one comes first? Join this course to understand how these intertwined strategies combine to create a robust strategic framework for organizations operating in today's data-driven world. You will also explore the role of an AI strategist in driving successful AI transformation that is well-aligned with strategic business goals.Explore What Makes a Good AI Goal
As you formulate an effective AI strategy, you will start by understanding the difference between AI and traditional software. Such distinction helps build a lens to identify whether AI is even a right fit. You will also learn to set realistic business goals and define the appropriate metrics to define the project's success. As you progress, you will gain insights into assessing whether the projects justify the return on the investments that go into building such sophisticated technology.Getting the Key Strategic Components in Place
You will learn about the different components of a successful AI strategy in detail, starting with fostering an AI culture. Such culture finds its roots in promoting innovation, high-performing teams, and the correct data. As you work through this conceptual course, you will find that while innovation is essential, building a robust risk assessment framework is crucial to get it right.Time to Unlock the Potential by Scaling AI
As you reach the end of this course, you will have all the necessary ingredients to get started. However, it is advised to start small and explore the idea's viability through a proof of concept before making hefty investments for full-scale implementation. You will also review what it takes to build scalable AI systems and the significance of MLOps in scaling them efficiently. Ultimately, the chapter underscores the influence of executive sponsors and AI champions in fostering AI adoption.Training 2 or more people?
Get your team access to the full DataCamp platform, including all the features.In the following Tracks
AI Business Fundamentals
Go To Track- 1
Fundamentals of AI Strategy
FreeThe chapter underpins the intricate relationships between business, data, and AI strategies. It then goes deeper into how an effective AI strategy begins with a clear vision and the role of a focused action plan in driving an organization's strategic objectives. You will also learn the skills that go into making a successful AI strategist, outlining their responsibilities and contributions towards achieving the business goals.
- 2
Designing a Winning AI Strategy
This chapter sharpens the business acumen by distinguishing AI software from traditional software, ensuring the effective use of resources for pertinent business challenges. It further explains the key business drivers in identifying the most impactful AI initiatives and shares how to set the right AI goals. Alongside explaining the significance of ROI, learners will understand the challenges and drivers of assessing ROI.
When should one think of AI?50 xpAdapting to the AI revolution50 xpUnderstanding software paradigms100 xpUncovering core business drivers50 xpFoundations of a successful AI plan50 xpAI suitability test50 xpDistinguishing AI-friendly scenarios100 xpSetting realistic business goals50 xpAI goals with the SMART lens100 xpA test of time and ambition100 xpAssessing the ROI of an AI initiative50 xpAI's dual-faced ROI100 xpTracing revenue roots100 xp - 3
Components of AI Strategy
FreeThis chapter explains different components of a successful AI strategy, such as innovation and building the right culture for high-performing teams. It also underscores the importance of AI literacy, covering the pivotal do’s and don’ts of AI usage. While innovation is essential, understanding the potential AI-associated risks and asking the right questions is crucial to building a robust risk assessment framework for AI.
Building an AI culture50 xpDiverse AI attempts50 xpExploration & experimentation100 xpBuilding high-performing AI teams50 xpFinding the gap50 xpThe essential trio100 xpGetting the right data50 xpGetting the right mix100 xpQuality saves cost50 xpAI risk assessment50 xpPrivacy and ethics100 xpCompliance with regulations50 xpCompliance50 xpAI project lifecycle100 xp - 4
Time for Action
In this chapter, we discuss the role of feasibility workshops and emphasize initiating a focused PoC to gauge AI's potential before a full-scale rollout. We will also highlight what it takes to build scalable AI systems and the significance of MLOps in scaling it right. Ultimately, the chapter underscores the influence of executive sponsors and AI champions in fostering AI adoption.
It all starts with a PoC50 xpDecision points for AI diagnostic tool100 xpEvaluating essentials of an AI initiative50 xpScaling beyond PoCs50 xpSorting AI scaling obstacles100 xpScaling an AI system50 xpMLOps50 xpNavigating MLOps50 xpSorting architectural practices100 xpBarriers to adoption50 xpAssigning roles in AI transformation100 xpBlueprint for AI transformation50 xpIt's time to wrap-up!50 xp
Training 2 or more people?
Get your team access to the full DataCamp platform, including all the features.In the following Tracks
AI Business Fundamentals
Go To Trackcollaborators
Vidhi ChughAI Strategist and Ethicist
Vidhi is an AI Strategist and Ethicist working at the intersection of data science, product, and engineering to build scalable machine learning systems. Listed as one of the "Top 200 Business and Technology Innovators" in the world, Vidhi is on a mission to democratize machine learning and break the jargon for everyone to be a part of this transformation.
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