Memahami paradigma perangkat lunak

OmnyFusion Corp., sebuah perusahaan pengembang perangkat lunak pionir, sedang mentransisikan sebagian sistem perangkat lunak tradisional mereka menjadi sistem bertenaga AI.

Tim TI telah melakukan audit internal dan menyusun daftar perangkat lunak yang dapat dimodernisasi serta ditingkatkan menggunakan algoritma AI tingkat lanjut.

Sebagai konsultan untuk OmnyFusion Corp., Anda perlu mengklasifikasikan proses saat ini berdasarkan apakah proses tersebut berpotensi menjadi lebih efisien dengan penggunaan AI atau tidak memiliki ruang untuk menggunakan AI dalam peningkatan proses.

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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: Different types of strategies Exercise 2: Charting the course Exercise 3: Which strategy?Exercise 4: Pillars of an Effective AI Strategy Exercise 5: Navigating the strategic water Exercise 6: The pillars of AI strategy Exercise 7: Role of an AI Strategist Exercise 8: Onboarding an AI strategist Exercise 9: A successful AI strategist

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: Kapan sebaiknya memikirkan AI?Exercise 2: Beradaptasi dengan revolusi AI Exercise 3: Memahami paradigma perangkat lunak

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Exercise 4: Mengungkap pendorong bisnis inti Exercise 5: Dasar-dasar rencana AI yang sukses Exercise 6: Uji kelayakan AI Exercise 7: Membedakan skenario yang sesuai untuk AI Exercise 8: Menetapkan tujuan bisnis yang realistis Exercise 9: Tujuan AI dengan lensa SMART Exercise 10: Uji waktu dan ambisi Exercise 11: Menilai ROI dari sebuah inisiatif AI Exercise 12: Dua sisi ROI dalam AI Exercise 13: Melacak akar pendapatan

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!