Strategi yang mana?

Riley, seorang konsultan strategi bisnis papan atas, sedang bekerja sama dengan Nature's Plate, perusahaan yang dikenal dengan menu berbahan organik dan kaya nutrisi.

Awalnya, perusahaan menikmati kesuksesan berkat penawarannya yang unik, tetapi belakangan ini penjualan mulai merosot.

Setelah dianalisis, manajemen menemukan bahwa penyebab utama penurunan ini adalah keterlibatan pelanggan yang rendah.

Menu saat ini, meskipun sehat, tidak memberikan pilihan yang beragam dan menarik untuk dijelajahi pelanggan.

Menanggapi tantangan ini, Nature's Plate memutuskan untuk mengembangkan mesin rekomendasi yang dipersonalisasi, sebuah alat bertenaga AI, untuk meningkatkan pengalaman pelanggan.

Latihan ini adalah bagian dari kursus

Strategi Artificial Intelligence (AI)

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Latihan ini adalah bagian dari kursus

Strategi Artificial Intelligence (AI)

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The 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.

Exercise 1: Beragam Jenis Strategi Exercise 2: Menentukan arah Exercise 3: Strategi yang mana?

Latihan Saat Ini

Exercise 4: Pilar-Pilar Strategi AI yang Efektif Exercise 5: Menavigasi arus strategi Exercise 6: Pilar strategi AI Exercise 7: Peran seorang AI Strategist Exercise 8: Merekrut AI Strategist Exercise 9: AI strategist yang sukses

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.

Exercise 1: When should one think of AI?Exercise 2: Adapting to the AI revolution Exercise 3: Understanding software paradigms Exercise 4: Uncovering core business drivers Exercise 5: Foundations of a successful AI plan Exercise 6: AI suitability test Exercise 7: Distinguishing AI-friendly scenarios Exercise 8: Setting realistic business goals Exercise 9: AI goals with the SMART lens Exercise 10: A test of time and ambition Exercise 11: Assessing the ROI of an AI initiative Exercise 12: AI's dual-faced ROI Exercise 13: Tracing revenue roots

This 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.

Exercise 1: Building an AI culture Exercise 2: Diverse AI attempts Exercise 3: Exploration & experimentation Exercise 4: Building high-performing AI teams Exercise 5: Finding the gap Exercise 6: The essential trio Exercise 7: Getting the right data Exercise 8: Getting the right mix Exercise 9: Quality saves cost Exercise 10: AI risk assessment Exercise 11: Privacy and ethics Exercise 12: Compliance with regulations Exercise 13: Compliance Exercise 14: AI project lifecycle

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.

Exercise 1: It all starts with a PoC Exercise 2: Decision points for AI diagnostic tool Exercise 3: Evaluating essentials of an AI initiative Exercise 4: Scaling beyond PoCs Exercise 5: Sorting AI scaling obstacles Exercise 6: Scaling an AI system Exercise 7: MLOps Exercise 8: Navigating MLOps Exercise 9: Sorting architectural practices Exercise 10: Barriers to adoption Exercise 11: Assigning roles in AI transformation Exercise 12: Blueprint for AI transformation Exercise 13: It's time to wrap-up!