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Nama: Atnan Ari Anderson

NPM: 2231096

Role Data Collector

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Nama: Delvin Jason

NPM: 2231115

Role: Data Scientist

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Nama: Kelvin

NPM: 2231191

Role: Data Scientist

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Tujuan bisnis dalam konteks implementasi Artificial Intelligence (AI) mengacu pada sasaran strategis yang ingin dicapai perusahaan dengan memanfaatkan teknologi AI. AI membantu perusahaan untuk mengotomatisasi tugas-tugas berulang, menganalisis data dalam jumlah besar, dan mendukung pengambilan keputusan yang lebih cerdas dan cepat.

Jenis-jenis Tujuan Bisnis yang Didukung oleh AI

1. Otomatisasi Proses (Process Automation)

Contoh: Sistem chatbot yang menjawab pertanyaan umum pelanggan secara otomatis di situs web.

2. Peningkatan Efisiensi Operasional (Operational Efficiency)

Contoh: Algoritma prediksi permintaan untuk memprediksi persediaan barang di toko retail, menghindari kekurangan stok atau overstock.

3. Personalisasi Layanan (Personalization)

Contoh: Rekomendasi produk di platform e-commerce seperti Amazon, di mana AI mempelajari perilaku pelanggan dan menyarankan produk yang sesuai.

4. Peningkatan Pengambilan Keputusan (Improved Decision Making)

Contoh: Analisis prediktif yang digunakan oleh perusahaan asuransi untuk menentukan premi polis berdasarkan risiko pelanggan.

5. Pengembangan Produk dan Layanan Baru

Contoh: Penggunaan AI dalam industri otomotif untuk mengembangkan mobil otonom (self-driving cars).

Contoh Studi Kasus Perusahaan Menggunakan AI untuk Mencapai Tujuan Bisnis

Netflix - Personalisasi Konten: Netflix menggunakan AI untuk menganalisis pola tontonan penggunanya dan memberikan rekomendasi film atau serial yang dipersonalisasi. Sistem ini belajar dari kebiasaan pengguna, seperti genre yang sering ditonton atau waktu pengguna menonton konten, dan menyajikan rekomendasi yang sesuai. Personalisasi ini membantu Netflix meningkatkan engagement dan retensi pelanggan.

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Nama: Albet Novendo

NPM: 2231088

Role: Backend Developer

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Konsep tujuan bisnis dalam konteks implementasi Artificial Intelligence (AI) mencakup berbagai aspek yang bertujuan untuk meningkatkan efisiensi, produktivitas, dan kualitas layanan. AI berfungsi sebagai alat yang dapat membantu perusahaan dalam mencapai tujuan strategis mereka dengan cara yang lebih efektif dan inovatif.

Jenis-Jenis Tujuan Bisnis yang Didukung oleh AI:

1. Otomatisasi Proses
AI memungkinkan otomatisasi tugas-tugas rutin dan berulang, seperti entri data dan pemrosesan faktur, sehingga mengurangi beban kerja karyawan dan meningkatkan efisiensi operasional.

2. Peningkatan Efisiensi Operasional
Dengan kemampuan analisis data yang cepat dan akurat, AI dapat membantu perusahaan mengidentifikasi area yang memerlukan perbaikan dan mengoptimalkan proses bisnis untuk meningkatkan produktivitas.

3. Personalisasi Layanan
AI dapat digunakan untuk menganalisis perilaku pelanggan dan preferensi mereka, sehingga memungkinkan perusahaan untuk menawarkan pengalaman yang lebih personal dan relevan kepada konsumen.

4. Pengambilan Keputusan yang Lebih Baik
AI mampu menganalisis data besar untuk memberikan wawasan yang mendalam, membantu manajemen dalam membuat keputusan yang lebih cerdas dan berbasis data.

5. Meningkatkan Layanan Pelanggan
Dengan penggunaan chatbot dan sistem dukungan berbasis AI, perusahaan dapat memberikan layanan pelanggan 24/7, menjawab pertanyaan secara real-time, dan menyelesaikan masalah dengan cepat.

Contoh Studi Kasus:

Telkomsel - Automated Customer Support: Telkomsel menggunakan AI untuk membantu mengatasi masalah banyaknya pelanggan yang complain dan mengharapkan response yang cepat. Untuk itu, telkomsel berkerja sama dengan kata.ai untuk pembuatan Veronika, AI Customer Support milik Telkomsel.

Referensi:

https://digitaltransformation.co.id/pengertian-artificial-inteleggence/

https://scs.telkomuniversity.ac.id/kecerdasan-buatan-dan-masa-depan-bisnis-memanfaatkan-ai-untuk-meningkatkan-otomatisasi-dan-keuntungan/

https://www.vads.co.id/berita/tujuan-ai-dan-perannya-dalam-dunia-bisnis-digital/

https://www.pointstar.co.id/artificial-intelligence/manfaat-artificial-intelligence-untuk-bisnis/

https://lpkia.ac.id/aplikasi-artificial-intelligence-penunjang-bisnis/

https://kata.ai/case-studies/veronika

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Conversational AI: The ultimate Solution for AI-powered Customer Experience

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Tujuan Utama dari AI untuk mendukung bisnis dalam mencapai efisiensi dan produktivitas yang lebih tinggi melalui otomatisasi dan analisis data yang canggih. AI memungkinkan perusahaan untuk mengotomatisasi proses manual, mengambil keputusan berbasis data, serta memberikan layanan yang lebih cepat dan personal. Dalam konteks bisnis, tujuan implementasi AI bisa berupa pengurangan biaya operasional, peningkatan kualitas layanan, hingga inovasi produk baru. AI tidak hanya membantu dalam meningkatkan efisiensi, tetapi juga memungkinkan bisnis untuk merespons pasar dengan lebih cepat dan tepat

Jenis-Jenis tujuan bisnis yang didukung oleh AI :

Otomatisasi Proses: AI digunakan untuk mengotomatisasi tugas-tugas rutin seperti pengelolaan inventaris, prediksi permintaan, dan pemrosesan data. Contoh aplikasi AI dalam hal ini adalah di sektor retail, di mana algoritma AI membantu memprediksi lonjakan permintaan untuk mengoptimalkan stok barang

Peningkatan Efisiensi Operasional:

AI dapat menganalisis data operasional untuk mengidentifikasi area yang memerlukan perbaikan. Misalnya, prediksi pemeliharaan untuk peralatan dapat mengurangi downtime dan meningkatkan produktivitas.

Personalisasi Layanan: AI juga mendukung personalisasi, terutama dalam industri e-commerce dan perbankan, di mana AI digunakan untuk menganalisis preferensi pelanggan dan memberikan rekomendasi produk atau layanan yang sesuai

Contoh Studi Kasus Perusahaan yang Menggunakan AI untuk Mencapai Tujuan Bisnis

1. Netflix:

Tujuan: Personalisasi Layanan

Implementasi AI: Netflix menggunakan algoritma pembelajaran mesin untuk menganalisis perilaku tontonan pengguna dan memberikan rekomendasi film atau serial yang disesuaikan. Hal ini telah meningkatkan pengalaman pengguna dan waktu tonton, serta mendorong retensi pelanggan

referensi :

1. https://bif.telkomuniversity.ac.id/manfaat-ai-dalam-otomatisasi-proses-bisnis-untuk-perusahaan/

2. https://aici-umg.com/article/manfaat-ai-untuk-bisnis/

3. https://recostream.com/blog/recommendation-system-netflix

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Currently, almost every user of streaming platforms expects that they will be able to understand their preferences and tastes and will suggest a movie or series that they will most likely enjoy. In other words - we are very used to the recommending algorithm.

That is why the largest platforms such as Disney +, Hulu, Amazon Prime or Netflix invest in the development of recommendation systems based on artificial intelligence.

Let's take a closer look at the content-based recommendation engine that is considered by many to be the best on the market. Of course, we are talking about the Netflix recommendation system.

We will answer the following questions:

How does Netflix's recommendation system work?

What is the Netflix algorithm?

How does Netflix's recommendation system collect and use user data?

How effective are Netflix's recommendations?

How do other entertainment sites use recommendation engines?

What is the Netflix algorithm?

The statistics speak for themselves, Netflix is the absolute leader among various streaming services.

In the year 2020 alone, Netflix gained over 37 million new subscribers, of which as much as 80% came from outside the United States. For example, the competing Hulu platform had only 36 million users in total.

Much of this success can be attributed to the recommendation engine, which constantly suggests new and interesting content while keeping users' attention.

Netflix currently offers thousands of movies and shows. Too much choice, however, may turn out to be overwhelming to most undecided users.

With over 15,000 different movies and series in the Netflix catalog, it is almost impossible for users to find the movies they like and find interesting by themselves.

This is where the personalized recommendation engine comes in handy.

This system is based on an algorithm that uses artificial intelligence and machine learning to offer users content tailored to their tastes and interests.

Beginnings: Netflix Prize 2006

The history of the Netflix recommendation system is over 20 years long.

In 2000, the company introduced the first groundbreaking personalized movie recommendations. In turn, in 2006, it created the Netflix Prize, a competition whose aim was to create a system of personalized recommendations based on artificial intelligence, in which the main prize was $ 1 million.

Although no one finally managed to obtain satisfactory results and a sufficiently low standard deviation of the set of recommended content, the participants' effort was not wasted.

It inspired thousands of teams around the world to further work and develop artificial intelligence algorithms, seeing the potential that this technology has for the entertainment industry.

Many experiences gained with Netflix Prize also contributed to the improvement of its recommendation engine, which in turn made it one of the best systems of its kind on the market.

Source: NETFLIX system design, Medium

The layout of the recommendations is not accidental.

From the user's point of view, the first contact with the recommendation system takes place on the Netflix home page, where groups of films arranged in horizontal rows are displayed.

Each level includes movies and series from different categories:

Selected for you - a list of movies and series selected specifically to meet the preferences of a given user based on their browsing history,

Trending - a collection of content that is currently the most popular on the entire website,

Similar to - a list of content similar to a series or movie that a user has clearly liked recently. The system confirms this, e.g. based on the time and frequency of watching episodes of a given series,

News - newly added, interesting content that the website wants to promote to a larger number of potentially interested users.

Source: Netflix Binging on the Algorithm

The arrangement of the thumbnails in rows is also not accidental.

Research has shown that users focus the most on the top left-hand side of the screen, so materials that are most likely to be selected by users are placed to the left of the recommendation rows.

All offered models provide Netflix users with a wide selection of movies and series tailored to their interests, from which they will surely be able to choose something for themselves.

How does Netflix's recommendation system work?

Netflix's recommendation system is the result of the work of hundreds of engineers over the last 20 years and a great example of the possibilities of artificial intelligence and machine learning in practice.

Netflix's recommendation engine consists of several important parts, each focusing on a different type of data and aspect of user behavior.

Netflix uses various models for filtering the available data, but based on published documents and the results obtained, as well as other similar recommendation engines, we can assume that it uses the 2 most important models:

Content based filtering- it compares data on individual items, e.g. descriptions, comments or reviews,

Collaborative Filtering- a model that tries to find the relationship between the behavior and preferences of individual users.

Netflix then sorts the filtered data using all sorts of rankings. Each of them corresponds to one of the categories (rows) of recommendations displayed on the main page of the platform.

So we can distinguish the following types of content-based recommendation rankings used by Netflix:

Personalized Video Ranking (PVR)

It is a general algorithm that segregates the content available in the catalog according to a given feature (e.g. Sci-Fi movies, romantic comedies, etc.), matching it with the personal preferences and tastes of a given user.

Top-N Ranking

Similar to the PVR ranking, however, it only takes into account the top rankings of individual users, which determine the most popular materials in a given category.

Ranking of interesting content for further viewing

This algorithm checks which content a given person has turned on but has not finished watching. The system then calculates the probability that a particular user will want to come back to them. It does this by analyzing the range of data available about it, as well as elapsed time since viewing, drop-off point, used device, etc.

Popular movies ranking

The algorithm analyzes what types of content is popular at the current time. It takes into account various types of time trends and other information that may indicate that content of a given type will be more willingly selected at this time. The most important predictors in the ranking of popular films include:

Important world events (for example, the coronavirus pandemic contributed to the record popularity of a movie "Contagion"),

Holidays and other occasions (for example, in February, during Valentine's Day, romantic comedies are very popular)

Big Data: what data does Netflix use?

Netflix uses a lot of data from various sources to determine the likelihood of watching a given title.

This data applies to both the content in the catalog and information about individual users and their behavior and preferences.

The most important types of collected data include:

user interactions with the website (browsing history and ratings issued);

correlation data on the relationships and similarities between the tastes and preferences of individual user groups;

information about the content available in the Netflix library, such as genre, length, language, actors, etc.

Furthermore, Netflix also uses additional information to better personalize the experience of its users.

The most important types of information include:

Time of the day when individual users use the website,

The type of device on which users view the content,

Average viewing length.

When talking about the types of data used by the Netflix system to generate content recommendations, it is also worth mentioning about completely new solutions.

Netlix's Latest Algorithm: Recommendations based on movie covers

The platform has recently introduced a completely new, innovative recommendation algorithm based on analyzing graphics.

Research shows that if a user on an entertainment platform does not find something interesting within 90 seconds, they will likely lose interest and look for another activity.

With such a short time to arouse interest, images become the most effective way to draw users' attention to interesting titles as quickly as possible.

The new Netflix algorithm, recently added to its recommendation engine, provides its subscribers with unique thumbnails of movies and series tailored to their tastes.

Netflix creates several thumbnail versions for each series or movie. Then, based on the data about users, it presents them with the one they will most probably find the most interesting.

Source: Netflix Binging on the Algorithm

In the example above, we can see how a Pulp Fiction movie can be offered to Netflix users with different thumbnails depending on whether the person has a lot of movies with Uma Thurman or John Travolta in their browsing history.

This makes it much more likely that a given user will pay attention to this movie.

This solution also applies to movie trailers and series..

A good example is the popular series House of Cards. If a user has a preference for movies and series with a strong female character, they will be presented with a trailer featuring mostly Robin Wright, starring in Claire Underwood.

In turn, those who like dramas of a political nature will see the main trailer with Kevin Spacey in the lead role.

Statistics show that Netflix's algorithm guarantees that even 90% of users will like the trailer and will be interested in watching the first episode.

How does Netflix know our taste in movies so well and what we want to watch?

Netflix uses artificial intelligence and machine learning algorithms to generate content recommendations.

The problem, however, is to offer movies and series to users about whom the system still has little information.

Due to this, Netflix asks each new subscriber to select the titles they would like to watch. These titles are used as the first step to personalized recommendations.

Later, as users continue to watch, over time, the recommendation engine begins to understand their preferences and tastes better, adjusting the list of featured content based on the data collected about them..

Does all of this pay off and does Netflix's recommendation system actually benefit the company? Of course.

It is estimated that over 80 percent of the TV shows people watch on Netflix are discovered through the platform's recommendation system.

This means that the vast majority of viewers' decisions on the platform are directly influenced by the recommendation engine and its artificial intelligence algorithms.

What's more, according to the latest data, Netflix's recommendation engine saves the company over $ 1 billion annually.

Many streaming platforms such as Hulu and Disney + have their content-based recommendation systems, but it is the Netflix engine that turns out to be the most effective.

Statistics show that 47% of US residents prefer to use Netflix (with an impressive retention rate of as much as 93%). By comparison, Amazon Prime ranks second with just 14%, while Hulu and Disney + showed 13.6% and 13% respectively.

How Netflix predicts movie and TV series trends

An advanced product recommendation engine collecting terabytes of data on traffic on the site gives Netflix one more very interesting and unique advantage over its competitors.

It can predict trends much more effectively and produce original series based on the real preferences of users..

Original Netflix content is positively received by viewers in 93% of cases.

It is estimated that a television program on TV has only a 35% chance of success. This clearly shows that Netflix's choices for scripts that will enter a production phase are not random..

The platform relies on a huge amount of data on the current trends and tastes of its users in order to predict the potential success of the original series produced by it.

This is another advantage over classic TV that Netflix gets due to the implementation of artificial intelligence.

A great example of the effectiveness of this approach is the great success of the Netflix series Stranger Things. Again, the algorithm was in a way able to predict its success.

Based on the behavior of millions of users, Netflix was able to create a profile of people who might like the series and determine their number. Then, it was these people who received recommendations in the first place immediately after logging into the platform.

Recommendation engine in other streaming services

Other entertainment sites on the web, similarly to Netflix, can boast of impressive and surprisingly effective recommendation engines based on artificial intelligence and machine learning.

Spotify is undoubtedly such a service.

Spotify is a digital music, podcast and video service that provides you with access to millions of songs and other content from creators around the world.

The Spotify engine, like Netflix, combines elements of various strategies and methods of data filtering used by models of various types to create one effective and comprehensive solution..

This system is responsible for creating a unique "Discover this week" playlist. It consists of 30 songs whose selection was based on the personal preferences of each user.

As it turns out, this playlist brings incredible benefits to the service. Within the five years since its launch, listeners have listened to over 2.3 billion hours of music offered to them via personalized playlists between July 2015 and June 25, 2020.

Another well-known site with an impressive recommendation engine is YouTube. YouTube recommendations are generated by Google Brain, which was recently released as an open source solution under the guise of TensorFlow.

The system consists of two neural networks responsible for various types of data filtering.

Similarly to Netflix and Spotify, the former filters user data to look for links between their tastes, while the latter sorts videos based on their characteristics and data, taking into account their ratings, number of views, comments, description and tags.

YouTube's recommendation system has undergone many modifications throughout the history of the website's operation.

In 2011, the main ranking aspect was simply the total number of views.

As of 2012, total watch time has been the most influential factor in video recommendations.

In turn, since 2015, this audience engagement has been the major factor taken into account by YouTube's algorithms.

The effectiveness of YouTube's recommendation engine leaves no room for doubt. According to experts, most of the video traffic on the platform is generated by the recommendation system and not by organic search results.

Summary

Advanced recommendation engines can process terabytes of data to improve user experience and deliver business-relevant analytical data. It is the comprehensive use of data analytics and the use of artificial intelligence in practice that is one of the main reasons for Netflix's success.

Netflix's record results clearly show that taking care of user experience, personalization and machine learning are currently one of the most important trends in supporting and developing online activities.

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Netflix Algorithm: Everything You Need to Know About the Recommendation System of the Most Popular Streaming Portal

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How does Netflix understand users' tastes and preferences so well? The answer is a content-based recommendation system based on artificial intelligence.

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Tujuan bisnis adalah sasaran yang ingin dicapai oleh perusahaan untuk meningkatkan kinerja, profitabilitas, atau daya saing. Dalam konteks implementasi AI (Artificial Intelligence), AI digunakan sebagai alat untuk membantu perusahaan mencapai tujuan-tujuan tersebut dengan cara yang lebih efisien, inovatif, dan skalabel. AI memungkinkan perusahaan untuk memanfaatkan data yang besar (big data) dan kompleks untuk mengotomatisasi proses, meningkatkan efisiensi, mengoptimalkan keputusan, serta menghadirkan pengalaman yang lebih personal bagi pelanggan.

Jenis-Jenis Tujuan Bisnis yang Didukung oleh AI

1. Otomatisasi Tugas Rutin dan Berulang
Dengan AI, tugas-tugas manual seperti data entry, penanganan dokumen, atau layanan pelanggan dapat diotomatisasi. Ini mengurangi biaya tenaga kerja dan mempercepat proses.

Contoh: Perbankan menggunakan chatbot AI untuk menangani pertanyaan nasabah dan transaksi sederhana secara otomatis, sehingga mengurangi beban call center.

2. Penghematan Biaya Operasional
AI dapat membantu mengoptimalkan penggunaan sumber daya dan mengurangi biaya operasional dengan memanfaatkan analitik prediktif dan otomatisasi.

Contoh: General Electric (GE) menggunakan AI untuk memantau performa mesin-mesin besar dan mendeteksi kebutuhan perawatan sebelum terjadi kerusakan, sehingga menurunkan biaya perbaikan dan downtime.

3. Peningkatan Efektivitas Pemasaran
AI memungkinkan analisis perilaku konsumen secara mendalam dan memberikan rekomendasi pemasaran yang lebih tepat sasaran.

Contoh: Platform e-commerce menggunakan AI untuk membuat kampanye pemasaran yang dipersonalisasi berdasarkan kebiasaan belanja pengguna, sehingga meningkatkan tingkat konversi.

4. Pengambilan Keputusan yang Lebih Cepat dan Akurat
AI memungkinkan perusahaan mengambil keputusan strategis lebih cepat dengan memproses data dalam jumlah besar secara real-time dan mengidentifikasi pola-pola penting.

Contoh: Goldman Sachs menggunakan AI untuk analisis pasar keuangan, sehingga mereka dapat membuat keputusan investasi yang lebih cepat dan lebih akurat.

Contoh Studi Kasus Lain Perusahaan yang Menggunakan AI untuk Mencapai Tujuan Bisnis

1. Walmart – Optimalisasi Stok dan Rantai Pasokan
Walmart menggunakan AI dalam rantai pasokan untuk memperkirakan permintaan pelanggan secara akurat. Algoritma prediktif AI memproses data historis penjualan, tren musiman, dan faktor eksternal lainnya untuk memastikan ketersediaan barang yang tepat di waktu yang tepat, mengurangi stok berlebih dan kekurangan barang.

2. BMW – AI untuk Pengembangan Mobil Otonom
BMW menggunakan AI untuk mengembangkan teknologi mobil otonom (self-driving cars). AI memungkinkan pengolahan data dari sensor kendaraan secara real-time untuk mengenali objek di jalan, memahami lingkungan, dan mengemudi secara aman. Ini mendukung tujuan bisnis BMW dalam mengembangkan kendaraan masa depan yang inovatif.

3. Airbnb – Pengoptimalan Harga dengan AI
Airbnb menggunakan AI untuk mengotomatisasi sistem penetapan harga (dynamic pricing). AI menganalisis data pasar lokal, tingkat permintaan, dan harga pesaing untuk menyesuaikan harga sewa properti secara otomatis. Dengan demikian, Airbnb membantu pemilik properti memaksimalkan pendapatan sambil meningkatkan keterjangkauan bagi penyewa.

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Kelompok 1: Pengumpulan Data dalam AI

    Kelompok 1:
        Menjelaskan proses pengumpulan data dalam proyek AI.
        Mengidentifikasi berbagai sumber data (internal dan eksternal) yang umum digunakan dalam proyek AI, seperti data transaksi, data media sosial, atau data sensor.
        Memberikan contoh studi kasus pengumpulan data dari dunia nyata yang digunakan dalam penerapan AI.

Kelompok 1: Visualisasi Data dalam Proyek AI

    Kelompok 1:
        Menjelaskan pentingnya visualisasi data dalam siklus hidup proyek AI.
        Mengidentifikasi alat dan teknik visualisasi data yang umum digunakan, seperti Matplotlib, Seaborn, dan Plotly.
        Memberikan contoh visualisasi data sederhana yang dapat membantu dalam analisis data awal.

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Nama: Wira Erlangga Pratama

NPM: 2231093

Role: UI/UX Designer & Frontend Developer

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Nama: Jesen Jeverlino

NPM: 2231052

Role: UI/UX Designer & Frontend Developer

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Konsep Tujuan Bisnis dalam Konteks Implementasi AI: Tujuan bisnis dalam konteks implementasi AI adalah untuk meningkatkan efisiensi operasional, mengurangi biaya, meningkatkan kepuasan pelanggan, dan menciptakan nilai tambah melalui inovasi produk dan layanan. AI digunakan untuk memproses data besar, menganalisis tren pasar, dan membuat keputusan yang lebih cepat dan akurat.

Jenis-Jenis Tujuan Bisnis yang Dapat Didukung oleh AI:

Otomatisasi Proses: Mengurangi tugas-tugas manual dan meningkatkan produktivitas.

Peningkatan Efisiensi Operasional: Mengoptimalkan proses bisnis dan mengurangi biaya operasional.

Personalisasi Layanan: Menyesuaikan produk dan layanan berdasarkan preferensi pelanggan.

Pengambilan Keputusan Berdasarkan Data: Membantu manajer membuat keputusan yang lebih cerdas dengan analisis data yang lebih akurat.

Contoh Studi Kasus Perusahaan yang Menggunakan AI untuk Mencapai Tujuan Bisnis:

Amazon: Menggunakan AI untuk rekomendasi produk berdasarkan riwayat pembelian pelanggan, yang meningkatkan penjualan dan kepuasan pelanggan.

Tesla: Mengembangkan teknologi otomatisasi kendaraan otonom yang meningkatkan keselamatan dan efisiensi dalam sistem transportasi.

JPMorgan Chase: Menggunakan AI untuk mendeteksi penipuan dengan menganalisis pola transaksi dan mengidentifikasi aktivitas mencurigakan.

referensi:

Transformasi Bisnis Melalui Kecerdasan Buatan (AI)

Contoh, Aplikasi, dan Penggunaan AI | IBM

Proyek Coding AI: Contoh dan Studi Kasus 📍 - Artificial Intelligence Center Indonesia

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Tujuan bisnis dalam konteks AI (Artificial Intelligence) merujuk pada sasaran yang ingin dicapai oleh suatu perusahaan dengan memanfaatkan teknologi AI. Berikut adalah beberapa tujuan bisnis yang umum dalam konteks AI:

1. Meningkatkan Efisiensi Operasional: AI dapat digunakan untuk mengotomatisasi tugas-tugas rutin dan mengurangi waktu yang dibutuhkan untuk menyelesaikan pekerjaan, sehingga meningkatkan produktivitas dan efisiensi operasional.

2. Pengambilan Keputusan yang Lebih Baik: Dengan menganalisis data besar, AI dapat memberikan wawasan yang lebih mendalam dan mendukung pengambilan keputusan yang lebih cepat dan lebih tepat.

3. Peningkatan Pengalaman Pelanggan: AI dapat digunakan untuk memahami preferensi pelanggan dan memberikan rekomendasi yang dipersonalisasi, sehingga meningkatkan kepuasan dan loyalitas pelanggan.

4. Inovasi Produk dan Layanan: Dengan memanfaatkan AI, perusahaan dapat mengembangkan produk dan layanan baru yang lebih inovatif dan sesuai dengan kebutuhan pasar.

5. Pengurangan Biaya: Dengan mengautomasi proses dan mengoptimalkan penggunaan sumber daya, AI dapat membantu perusahaan mengurangi biaya operasional.

6. Keamanan dan Risiko: AI dapat digunakan untuk mendeteksi dan mengatasi ancaman keamanan siber lebih cepat, serta membantu dalam manajemen risiko.

7. Peningkatan Analisis Data: AI memungkinkan analisis data yang lebih kompleks dan mendalam, memberikan pemahaman yang lebih baik tentang tren dan pola yang mungkin tidak terlihat sebelumnya.

8. Mendukung Keberlanjutan: AI dapat membantu dalam mengoptimalkan penggunaan sumber daya dan mengurangi limbah, mendukung inisiatif keberlanjutan perusahaan.

9. Adaptasi terhadap Perubahan Pasar: Dengan kemampuan untuk menganalisis data secara real-time, AI membantu perusahaan merespons perubahan pasar dengan lebih cepat dan efektif.

Dalam menerapkan tujuan-tujuan ini, penting bagi perusahaan untuk memiliki strategi yang jelas dan memahami bagaimana teknologi AI dapat diintegrasikan dengan proses bisnis yang ada. Pengukuran kinerja dan evaluasi hasil juga sangat penting untuk memastikan bahwa tujuan bisnis yang diinginkan tercapai.

Contoh Tujuan Bisnis Grammarly Business dalam Konteks AI

1 Meningkatkan Kualitas Konten

Tujuan: Meningkatkan kualitas tulisan pengguna dengan memberikan saran yang lebih baik dan lebih tepat waktu.

Implementasi: Menggunakan algoritma AI untuk menganalisis teks dan memberikan rekomendasi real-time tentang pilihan kata, struktur kalimat, dan tata bahasa. Ini membantu penulis dan kreator konten menghasilkan tulisan yang lebih menarik dan efektif, yang pada gilirannya dapat meningkatkan tingkat keterlibatan audiens.

2. Mengurangi Waktu Penyuntingan

Tujuan: Mengurangi waktu yang dibutuhkan penulis untuk menyunting dan merevisi konten.

Implementasi: Dengan AI yang memberikan saran otomatis, penulis dapat menghemat waktu yang biasanya dihabiskan untuk mengoreksi kesalahan dan menyempurnakan konten. Ini meningkatkan produktivitas tim, memungkinkan mereka fokus pada tugas kreatif yang lebih strategis.

3. Meningkatkan Kepuasan Pelanggan

Tujuan: Meningkatkan kepuasan pelanggan dengan menyediakan alat yang dapat membantu mereka menghasilkan konten berkualitas tinggi.

Implementasi: Dengan fitur AI yang canggih, Grammarly Business dapat memberikan pengalaman pengguna yang lebih baik, menjadikan pelanggan merasa lebih percaya diri dalam tulisan mereka. Hal ini berpotensi meningkatkan retensi pelanggan dan mendukung penjualan produk premium.

4. Mencegah Plagiarisme dan Konten Monoton

Tujuan: Mengurangi risiko plagiarisme dan konten yang membosankan dengan memberikan alternatif kata dan frasa.

Implementasi: Fitur deteksi plagiarisme dan rekomendasi variasi bahasa membantu penulis menghasilkan konten yang orisinal dan menarik. Ini sangat penting bagi perusahaan yang ingin mempertahankan citra merek yang kuat dan menghindari masalah hukum terkait plagiarisme.

5. Mendukung Tim Kreatif dengan Data Analitik

Tujuan: Memberikan wawasan berbasis data kepada tim pemasaran dan kreatif untuk memahami tren konten dan preferensi audiens.

Implementasi: Menggunakan analitik yang didukung AI untuk melacak performa konten, Grammarly Business dapat membantu perusahaan mengidentifikasi jenis konten yang paling berhasil, sehingga memungkinkan penyesuaian strategi pemasaran yang lebih tepat.

6. Meningkatkan Penjualan Produk Premium

Tujuan: Meningkatkan penjualan produk premium dengan menawarkan nilai tambah yang lebih besar kepada pengguna.

Implementasi: Dengan menyediakan fitur AI yang lebih canggih dalam paket premium, Grammarly Business dapat menarik lebih banyak pengguna untuk beralih dari versi gratis ke premium. Contohnya, penawaran fitur seperti analisis gaya penulisan yang lebih mendalam dan integrasi dengan platform bisnis lainnya dapat meningkatkan nilai produk.

7. Memperluas Basis Pelanggan di Segmen Bisnis

Tujuan: Menarik lebih banyak pelanggan di segmen bisnis dengan menyesuaikan fitur yang sesuai dengan kebutuhan korporat.

Implementasi: Grammarly Business dapat menawarkan alat khusus untuk tim, seperti kolaborasi dalam penyuntingan konten dan pengaturan gaya bahasa yang konsisten, sehingga menjadi pilihan ideal bagi perusahaan yang membutuhkan alat penulisan yang andal.

Grammarly Business memanfaatkan AI untuk mencapai berbagai tujuan bisnis yang tidak hanya meningkatkan kualitas konten tetapi juga memberikan nilai tambah bagi pengguna. Dengan fokus pada efisiensi, kepuasan pelanggan, dan dukungan untuk tim kreatif, Grammarly dapat meningkatkan posisi kompetitifnya di pasar alat bantu penulisan.

Referensi :

1. https://www.techtarget.com/searchenterpriseai/feature/6-key-benefits-of-AI-for-business

2. https://kledo.com/blog/ai-dalam-bisnis/

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As organizations increase their use of artificial intelligence technologies within their operations, they're reaping tangible benefits that are expected to deliver significant financial value.

Eighty-seven percent of organizations believe AI and machine learning will help them grow revenue, boost operational efficiency and improve customer experiences, according to research firm Frost & Sullivan's "Global State of AI, 2022" report.

Those are only a few of the benefits AI can deliver.

Here are 12 advantages the technology brings to organizations across various industry sectors.

1. Better decisions

Organizations increasingly use AI to gain insights into their data or, in the business lingo of today, to make "data-driven decisions." As they do that, they're finding they do indeed make better, more accurate decisions instead of ones based on individual instincts or intuition tainted by personal biases and preferences.

As an example, Kavita Ganesan, an AI adviser, strategist and founder of the consultancy Opinosis Analytics, pointed to one company that used AI to help it sort through the survey responses of its 42,000 employees. The technology analyzed narrative responses and presented summarized findings -- an approach that let company officials effectively understand what workers wanted most rather than offering them options to rank via check-the-box choices.

2. Efficiency and productivity gains

Efficiency and productivity gains are two other big benefits that organizations get from using AI, said Adnan Masood, chief AI architect at UST, a digital transformation solutions company.

Masood said AI lets organizations handle tasks at a volume and velocity that's simply not possible for humans to match -- whether they're using AI for search or to analyze data for insights, create software code or execute specific business processes.

AI not only works at a scale beyond human capacity, Masood noted, but it removes time-consuming manual tasks from workers -- a productivity gain that lets workers perform higher-level tasks that only humans can do. He pointed to the use of AI in software development as a case in point, highlighting the fact that AI can create test data to check code, freeing up developers to focus on more engaging work.

This lets organizations minimize the costs associated with performing mundane, repeatable tasks that can be performed by technology while maximizing the talent of their human capital.

Here are the key benefits AI brings to businesses.

3. Improved speed of business

As fast as business moves in this digital age, AI helps it move even faster, said Seth Earley, author of The AI-Powered Enterprise and CEO of Earley Information Science. "It's all about speeding up the clock of the enterprise," he said. AI essentially enables shorter cycles and cuts the time it takes to move from one stage to the next -- such as from design to commercialization -- and that shortened timeline, in turn, delivers better and more immediate ROI.

4. New capabilities and business model expansion

Executives can use AI for business model expansion, experts said, noting that organizations are seeing new opportunities as they deploy data, analytics and intelligence into the enterprise.

For example, autonomous vehicle companies could use the reams of data they're collecting to identify new revenue streams related to insurance, while an insurance company could apply AI to its vast data stores to get into fleet management.

5. Personalized customer services and experiences

AI analyzes and learns from data to create highly personalized and customized experiences and services, said Brian Jackson, principal research director at Info-Tech Research Group.

He said the most visible examples of this come from the consumer world, as streaming services, such as Netflix, and retailers use intelligent systems to study buying patterns, individual consumer data and larger data sets to determine what each customer prefers at any given time to suit their personal style, interests and needs.

However, AI delivers that personalization in numerous other areas, such as in healthcare, where it customizes treatments, and in work environments to support an employee's individual requirements.

6. Improved services

AI creates interactions with technology that are easier, more intuitive, more accurate and, thus, better all around, said Mike Mason, chief AI officer with consultancy Thoughtworks.

He cited a real estate site that employs generative AI so users can refine their property listing searches through conversational queries rather than clicking through boxes.

"The AI understands an unstructured query, and it understands unstructured data," Mason explained. In other words, the technology can analyze a user's request even when it's given in plain, conversational language; analyze all the descriptive elements within each listing, including narrative notes added by real estate agents; and present the user with a finely tuned and highly accurate list of properties that meets their requirements.

7. Improved monitoring

AI's capacity to take in and process massive amounts of data in real time means organizations can implement near-instantaneous monitoring capabilities to alert them to issues, recommend actions and, in some cases, initiate a response, according to experts.

For example, AI can use information gathered by devices on factory equipment to identify problems and predict needed maintenance. This prevents disruptive breakdowns and costly maintenance work performed because it's needed rather than scheduled.

AI's monitoring capabilities can be effective in other areas, such as in enterprise cybersecurity operations where large amounts of data need to be analyzed and understood.

8. Better quality and reduction of human error

Organizations can expect a reduction of errors and stronger adherence to established standards when they add AI technologies to processes. Furthermore, when AI and machine learning are integrated with a technology such as robotic process automation, which automates repetitive, rules-based tasks, the combination not only speeds up processes and reduces errors, but can also be trained to improve upon itself and take on broader tasks.

As a result of that error reducing and higher quality, "AI improves the value prop[osition]," Earley said.

The use of AI in financial reconciliation, for example, delivers nearly always error-free results, whereas that same reconciliation when handled, even in part, by human employees is prone to mistakes.

9. Better talent management

Companies are using AI to improve many aspects of talent management, from streamlining the hiring process to rooting out bias in corporate communications. Moreover, AI-enabled processes not only save companies in hiring costs, but also can affect workforce productivity by successfully sourcing, screening and identifying top-tier candidates. As natural language processing tools have improved, companies are also using chatbots to provide job candidates with a personalized experience and to mentor employees. Additionally, AI tools can gauge employee sentiment, identify and retain high performers, determine equitable pay, and deliver more personalized and engaging workplace experiences with less requirements on boring, repetitive tasks.

10. More innovation

As workers at all levels become more comfortable and confident working with AI, experts said they're starting to use AI tools to help them be more creative and more innovative.

In her book, Ganesan described how a restaurant chain used the technology to power a mobile app that enables customers to craft their own cocktails based on their mood and food choices, with the tool concocting the recipe and then sending it to a human bartender to mix and serve.

Other industries use AI to support R&D activities, such as in the healthcare space for drug discovery work and the consumer product goods sector for new product creation.

11. Increased profitability

As they use AI in more areas of the enterprise -- from personalizing services to aiding in risk management to supporting innovation -- organizations will see improved productivity, reduced costs, higher efficiency and possibly new growth opportunities.

Taken together, that can deliver higher profitability, said Sreekar Krishna, U.S. leader of AI at professional services firm KPMG. "On the revenue side, it's potential is exponential: You can keep growing as long as you're bringing value to customers."

12. Industry-specific improvements

In addition to the benefits listed above, AI can fuel the following industry-specific improvements:

Retailers can use AI to better target their marketing efforts, develop a more efficient supply chain and better calculate pricing for optimal returns. At retail companies, AI helps predict customer requirements and appropriate staffing levels.

The pharmaceutical sector can use the technology to perform drug discovery data analysis and predictions that can't be done with conventional technologies.

The financial industry can use AI to strengthen its fraud detection efforts.

It's important to remember that, as companies find ways to use AI for competitive advantage, they're also grappling with challenges. Concerns include AI bias, government regulation of AI, management of the data required for machine learning projects and talent shortages. In addition, financial gains can be elusive if the talent and infrastructure for implementing AI aren't in place.

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Business use of AI is increasing rapidly. What are companies getting in return? Learn about the benefits of AI from leading experts in the field.