Kamis, 25 Juli 2019

MILLENIALS KILL EVERYTHING by Yuswohady

WASPADAI MILENIAL

Satu lagi dampak evolusi ekonomi pergeseran perilaku ... hari ini beberapa store Giant tutup mungkin bangkrut.... yuuukkkk baca analisa berita dibawah ini👇🏿👇🏿(tulisan yg menarik)

How Millennials Kill Everything
Oleh : Yuswohady

Judul tulisan ini bakal menjadi judul buku baru.
Mudah-mudah buku ini bisa keluar dalam 2-3 bulan ke depan.

Coba googling dengan kata kunci “millennials kill”, maka Anda akan mendapati betapa millennial adalah “pembunuh berdarah dingin” yang membunuh apapun.

Di halaman pertama hasil pencarian Google saya menemui judul-judul menyeramkan seperti ini:
“RIP: Here Are 70 Things Millennials Have Killed”
“Things Millennials Are Killing in 2018”
“Millennials Kill Again. The Latest Victim? American Cheese”
“Millennials Are Killing the Beer Industry”
“How Millennials Will Kill 9 to 5 Job?”

Bahkan ada situs yang menulis:
“The Official Ranking of Everything Millennials Have Killed.”

Di dalamnya peringkat produk dan layanan yang paling cepat “dibunuh” oleh milenial.

Ada dalam urutan peringkat itu produk-produk seperti:
berlian di urutan 29;
golf di urutan 23;
department store di urutan 20;
sabun batang di urutan 15;
kartu kredit di urutan 10; dan
bir di urutan 5.

Millennials Kill Everything New

Kenapa milenial bisa menjadi “pembunuh berdarah dingin” bagi begitu banyak produk dan layanan?

Karena perilaku dan preferensi mereka berubah begitu drastis sehingga produk dan layanan tersebut menjadi tidak relevan lagi, alias punah ditelan zaman.

Contohnya golf. Tren dunia menunjukkan, sepuluh tahun terakhir viewership ajang-ajang turnamen golf bergengsi turun drastis setelah mencapai puncaknya di tahun 2015.

Tahun lalu bahkan turun drastis 75%. Porsi kalangan milenial yang menekuni olahraga ini juga sangat kecil hanya 5%.

Olahraga elit ini memang digemari kalangan Baby Boomers dan Gen-X, namun tidak demikian halnya dengan milenial.

Celakanya, semakin surut populasi Baby Boomers dan Gen-X, maka semakin tidak populer pula olahraga yang lahir sejak abad 15 ini. Dan bisa jadi suatu saat akan puhah.

Yang sudah kejadian sekarang adalah departement store.

Tahun lalu kita menyaksikan departement store di seluruh dunia termasuk di Indonesia (Matahari, Ramayana, Lotus) pelan tapi pasti mulai berguguran.

Sumber penyebabnya adalah milenial yang bergeser perilaku dan preferensinya.

Pertama karena mereka mulai berbelanja via online. Kedua, milenial kini tak lagi heboh berbelanja barang (goods), mereka mulai banyak mengonsumsi pengalaman (experience/leisure).

Mereka ke mal bukan untuk berbelanja barang, tapi cuci mata, nongkrong dan dine-out mencari pengalaman penghilang stress.

Pasar properti beberapa tahun terakhir seperti diam di tempat.

Alih-alih semua pelaku berharap ini hanya siklus “bullish-bearish” biasa yang nanti akan naik dengan sendirinya, saya curiga ini adalah kondisi “bearish berkelanjutan” sebagai dampak terbentuknya “new normal” perekonomian kita yang melesu dalam jangka panjang.

Mungkin biangnya bisa berasal dari pergeseran perilaku dan preferensi milenial.

Beberapa kemungkinannya:
Milenial mulai menunda nikah,
menunda punya rumah, dan
menunda punya anak.

Belum lagi minimalist lifestyle yang kini banyak diadopsi milenial mendorong mereka memilih rumah ukuran mini.

Program KB yang sukses membuat late Baby Boomers dan Gen-X membentuk keluarga kecil dengan dua anak.

Dengan jumlah anggota keluarga yang kecil, maka anak-anak mereka (milenial) cenderung menempati rumah orang tua dan sharing dengan sesama saudara. So, tak perlu beli rumah baru lagi. Ini yang menjadi biang kenapa market size properti cenderung mandek.

Tak hanya itu, tempat kerja pun nantinya pelan tapi pasti bisa “dibunuh” oleh milenial.

Bagi Baby Boomers dan Gen-X bekerja rutin tiap hari masuk kantor dari jam 8 pagi sampai 5 sore (“8-to-5”) adalah sesuatu yang lumrah. Namun tak demikian halnya dengan milenial.

Milenial mulai menuntut fleksibilitas dalam bekerja. Bekerja di manapun dan kapanpun bisa asal kinerja yang dikehendaki tetap tercapai.

Kini mereka mulai menuntut pola kerja: “remote working”, “flexible working schedule”, atau “flexi job”.

Survei Deloitte menunjukkan, 92% milenial menempatkan fleksibilitas kerja sebagai prioritas utama.

Tren ke arah “freelancer”, “digital nomad” atau “gig economy” kini kian menguat.

Kerja bisa berpindah-pindah:
tiga bulan di Ubud,
empat bulan di Raja Ampat,
tiga bulan berikutnya lagi di Chiang May.

Istilah kerennya: WORKCATION (kerja sambil liburan).

Apa dampak dari millennial shifting tersebut terhadap kantor-kantor yang masih menerapkan working style ala Baby Boomers dan Gen-X? So pasti kantor-kantor jadul itu akan ditinggalkan angkatan kerja yang nantinya bakal didominasi milenial. Kantor itu akan punah dan melapuk.

Millennials will kill everything!!!
Have we change the way we do our business in order to face the millenials way of life

Beberapa prediksi menarik :

1. Bengkel perbaikan kendaraan akan hilang.

2. Mesin yang menggunakan bensin memiliki 20.000 bagian. Motor yang menggunakan listrik hanya memiliki 20 bagian.

Mobil listrik dijual dengan garansi seumur hidup dan hanya diperbaiki oleh dealer. Hanya perlu waktu 10 menit untuk mencopot dan mengganti motor yang menggunakan listrik.

3. Motor listrik tidak diperbaiki di dealer tetapi akan dikirim ke bengkel perbaikan regional yang akan memperbaikinya menggunakan robot.

4. Motor listrik anda yang gagal fungsi akan ditunjukkan oleh lampu yang menyala, lalu anda akan pergi ke tempat yang menyerupai mesin cuci mobil, dan mobil anda akan ditarik sementara anda ngopi dan motor mobil anda akan diganti dengan yang baru.

5. SPBU atau pomp bensin akan hilang.?

6. Meter parkir akan digantikan oleh meter dispenser listrik.

Banyak perusahaan akan memasang stasiun isi ulang listrik; sebenarnya hal itu sudah dimulai.

7. Kebanyakan pabrik kendaraan (yang cerdas) telah mengalokasikan uangnya untuk mulai membangun pabrik yang hanya membuat mobil listrik.

8. Industri batu bara akan hilang.

Perusahaan minyak dan gas akan hilang. Pengeboran minyak akan hilang. Ucapkan selamat tinggal kepada OPEC.

9. Rumah2 akan menghasilkan dan menyimpan energi listrik pada siang hari dan akan menggunakan serta menjualnya listriknya ke grid.

Grid akan menyimpan dan menyalurkannya ke industri yang banyak menggunakan listrik. Apakah anda sudah melihat atap Tesla?

10. Bayi sekarang hanya akan melihat mobil pribadi di musium. Masa depan mendekati kita lebih cepat daripada yang bisa kita tangani.

11. Pada tahun 1998 Kodak memiliki 170.000 pegawai dan menjual 85% foto kertas di seluruh dunia.

Hanya dalam beberapa tahun model bisnis mereka hilang dan mereka bangkrut. Siapa yang mengira itu akan terjadi?

12. Apa yang terjadi pada Kodak dan Polaroid akan terjadi di kebanyakan industri dalam 5-10 tahun yang akan datang... dan kebanyakan orang tidak melihat itu akan terjadi.

13. Apakah pada tahun 1998 anda mengira bahwa 3 tahun setelahnya anda tidak akan pernah lagi mem-foto menggunakan film?. Dengan telepon cerdas sekarang, siapa yang masih memiliki kamera dengan film?.

14. Kamera digital ditemukan tahun 1975. Barang pertama hanya memiliki 10.000 piksel, tetapi mengikuti hukum Moore.

Dengan perkembangan teknologi yang eksponensial, barang yang semula mengecewakan menjadi super dan menjadi mainstream hanya dalam waktu yang singkat.

15. Hal itu terjadi lagi (tapi jauh lebih cepat) dengan kecerdasan buatan (artificial intelligence), kesehatan, kendaraan listrik, pendidikan, pencetakan 3D, agrikultur dan lapangan pekerjaan.

16. Lupakan buku 'Kejutan Masa Depan', ucapkan selamat datang kepada Revolusi Industri keempat.

17. Software telah dan akan terus mengacaukan banyak industri tradisional dalam 5-10 tahun mendatang.

18. Uber (seperti halnya Gojek di Indonesia) hanya piranti software, mereka tidak memiliki mobil, tapi sekarang mereka adalah perusahaan taksi terbesar di dunia! Tanyakan pada supir taksi apakah dulu mengira hal itu akan terjadi.

19. Airbnb sekarang adalah perusahaan hotel terbesar di dunia, walaupun mereka tidak memiliki properti apapun.

Tanyakan pada hotel Hilton apakah dulu mereka mengira hal itu akan terjadi.

20. Artificial Intelligence: komputer akan menjadi lebih baik secara eksponensial dalam hal memahami dunia.

Tahun ini, komputer mengalahkan pemain game terbaik kdi dunia, 10 tahun lebih cepat daripada yang diharapkan.

21. Di USA, pengacara muda sudah tidak memiliki pekerjaan.

Berkat Watson IBM, anda bisa memperoleh nasehat hukum dalam hitungan detik (saat ini untuk hal-hal dasar), dengan akurasi 90% dibandingkan 70% akurasi yang dilakukan manusia.

Jadi jika anda belajar hukum, berhentilah segera. Kebutuhan pengacara akan berkurang 90%, hanya spesialis serba tahu yang masih akan tetap bertahan.

22. Watson telah membantu perawat dalam mendiagnosa kanker, 4 kali lebih akurat dibandingkan perawat manusia.

23. Facebook sekarang memiliki software pengenal pola yang dapat mengenali wajah jauh lebih baik daripada manusia. Pada tahun 2030 komputer akan lebih cerdas daripada manusia.

24. Kendaraan otomotif: Pada tahun 2018 mobil tanpa supir pertama sudah muncul.

Dalam waktu 2 tahun ke depan seluruh industri akan dikacaukan.

Anda tidak akan ingin memiliki mobil lagi karena anda akan memanggil mobil dengan telepon anda, mobil itu akan muncul di lokasi anda, dan mengantarkan anda ke tempat tujuan anda.

Tidak perlu bingung memarkir mobil itu, anda hanya akan membayar jarak tempuh dan anda dapat tetap produktif selama berkendara. Anak-anak jaman sekarang tak pernah punya sim dan tak pernah memiliki mobil.

26. Hal itu akan mengubah kota-kota kita, karena kita hanya perlu mobil 90-95% lebih sedikit. Kita dapat mengubah lahan2 parkir menjadi taman-taman kota.

27. Sekitar 1,2 juta orang meninggal karena kecelakaan tiap tahun, termasuk yang disebabkan mengendarai sambil mabuk.

Sekarang kita memiliki satu kecelakaan tiap 60.000 mil; dengan kendaraan tanpa supir angka itu akan turun menjadi 1 kecelakaan tiap 6 juta mil. Ini akan menyelamatkan jutaan nyawa tiap tahunnya.

28. Kebanyakan perusahaan mobil tak diragukan lagi akan menjadi bangkrut.

Perusahaan mobil tradisional hanya mencoba pendekatan evolusioner dan hanya berusaha membuat mobil yang lebih baik, sementara perusahaan teknologi (Tesla, Apple, Google) melakukan pendekatan revolusioner dan membangun komputer di atas roda.

29. Lihat apa yang dilakukan Volvo sekarang; tidak ada lagi mesin pembakaran internal di kendaraan mereka mulai tahun ini untuk model 2019, semua menggunakan listrik atau hybrid, dengan maksud nantinya akan melenyapkan pula model2 hybrid.

28. Banyak ahli-ahli teknik di Volkswagen dan Audi takut terhadap Tesla, dan memang seharusnya begitu.

Lihat semua perusahaan yang menawarkan mobil listrik. Beberapa tahun lalu keberadaan mereka tidak bisa dirasakan.

29. Perusahaan asuransi akan mengalami kesulitan masif; tanpa kecelakaan, biaya akan menjadi lebih murah. Model bisnis asuransi mobil akan hilang.

30. Real estate akan berubah. Jika orang bisa bekerja pulang-pergi, mereka akan tinggal di tempat yang JAUH untuk hidup di lingkungan yang HARGA lebih terjangkau dan lebih menyenangkan.

31. Mobil listrik akan menjadi mainstream pada tahun 2030 an. Kota menjadi tidak berisik karena semua mobil baru akan menggunakan listrik.

32. Kota juga akan memiliki udara yang lebih bersih.

33. Listrik akan menjadi sangat murah dan bersih.

34. Produksi listrik tenaga surya telah mengalami kurva pengembangan eksponensial selama 30 tahun, tetapi sekarang anda dapat melihat dampak perkembangan tersebut.

Dan sekarang perkembangan itu sedang dipacu lebih kencang lagi.

35. Perusahaan energi fosil sedang berusaha mati2an membatasi akses ke grid untuk mencegah munculnya pesaing yang muncul dari instalasi listrik tenaga surya di rumah-rumah, tetapi upaya itu tidak akan bisa berlanjut - teknologi tidak akan bisa dibendung.

36. Kesehatan: Harga Tricorder X akan diumumkan tahun ini.

Banyak perusahaan akan membuat perangkat medikal (yang disebut 'Tricorder' dalam film Star Trek) yang bekerja dengan telepon anda, yang akan memindai retina anda, sampel darah anda, dan napas anda.

Lalu dia akan menganalisa 54 bio-marker yang akan meng identifikasi hampir semua jenis penyakit. Saat ini sudah terdapat lusinan aplikasi telepon untuk tujuan kesehatan.

*SELAMAT DATANG MASA DEPAN*

*...dia sebenarnya sudah datang beberapa tahun yang lalu...siap tidak siap sangat mungkin terjadi.....*

Kamis, 18 Juli 2019

ECONOMIST : JOBS

The rich world is enjoying an unprecedented jobs boom



May 23rd 2019

Everyone says work is miserable. Today’s workers, if they are lucky enough to escape the gig economy and have a real job, have lost control over their lives. They are underpaid and exploited by unscrupulous bosses. And they face a precarious future, as machines threaten to make them unemployable.

There is just one problem with this bleak picture: it is at odds with reality. As we report this week (see Briefing), most of the rich world is enjoying a jobs boom of unprecedented scope. Not only is work plentiful, but it is also, on average, getting better. Capitalism is improving workers’ lot faster than it has in years, as tight labour markets enhance their bargaining power. The zeitgeist has lost touch with the data.

Just the job

In America the unemployment rate is only 3.6%, the lowest in half a century. Less appreciated is the abundance of jobs across most of the rich world. Two-thirds of the members of the oecd, a club of mostly rich countries, enjoy record-high employment among 15- to 64-year-olds. In Japan 77% of this group has a job, up six percentage points in six years. This year Britons will work a record 55bn hours, on current trends. Germany is enjoying a bonanza of tax revenue following a surge in the size of its labour force (see article). Even in France, Spain and Italy, where joblessness is still relatively high, working-age employment is close to or exceeds 2005 levels.

The rich-world jobs boom is partly cyclical—the result of a decade of economic stimulus and recovery since the great recession. But it also reflects structural shifts. Populations are becoming more educated. Websites are efficient at matching vacancies and qualified applicants. And ever more women work. In fact women account for almost all the growth in the rich-world employment rate since 2007. That has something to do with pro-family policies in Europe, but since 2015 the trend is found in America, too. Last, reforms to welfare programmes, both to make them less generous and to toughen eligibility tests, seem to have encouraged people to seek work.

Thanks to the jobs boom, unemployment, once the central issue of political economy, has all but disappeared from the political landscape in many countries. It has been replaced by a series of complaints about the quality and direction of work. These are less tangible and harder to judge than employment statistics. The most important are that automation is destroying opportunities and that work, though plentiful, is low-quality and precarious. “Our jobs market is being turned into a sea of insecurity,” says Jeremy Corbyn, leader of Britain’s Labour Party.

Again, reality begs to differ. In manufacturing, machines have replaced workers over a period of decades. This seems to have contributed to a pocket of persistent joblessness among American men. But across the oecd as a whole, a jobs apocalypse carried out by machines and algorithms, much feared in Silicon Valley, is nowhere to be seen. A greater share of people with only a secondary education or less is in work now than in 2000.

It is also true that middle-skilled jobs are becoming harder to find as the structure of the economy changes, and as the service sector—including the gig economy—expands. By 2026 America will have more at-home careers than secretaries, according to official projections. Yet as labour markets hollow out, more high-skilled jobs are being created than menial ones.

Meanwhile, low-end work is becoming better paid, in part because of higher minimum wages. Across the rich world, wages below two-thirds of the national median are becoming rarer, not more common.

As for precariousness, in America traditional full-time jobs made up the same proportion of employment in 2017 as they did in 2005. The gig economy accounts for only around 1% of jobs there. In France, despite recent reforms to make labour markets more flexible, the share of new hires given permanent contracts recently hit an all-time high. The truly precarious work is found in southern European countries like Italy, and neither exploitative employers nor modern technology is to blame. The culprit is old-fashioned law that stitches up labour markets, locking out young workers in order to keep insiders in cushy jobs.

Elsewhere, the knock-on benefits of abundant work are becoming clear. As firms compete for workers rather than workers for jobs, average wage growth is rising, pushing up workers’ share of the pie—albeit not as fast as the extent of the boom might have suggested. Tight labour markets lead firms to fish for employees in neglected pools, including among ex-convicts, and to boost training amid skills shortages. American wonks fretted for years about how to shrink disability-benefit rolls. Now the hot labour market is doing it for them. Indeed, one attraction of the jobs boom is its potential to help solve social ills without governments having to do or spend very much.

Nonetheless, policymakers do have lessons to learn. Economists have again been humbled. They have consistently underestimated potential employment, leading to hesitant fiscal and monetary policy. Just as their sanguine outlook on finance in the 2000s contributed to the bust, so their mistaken pessimism about the potential for jobs growth in the 2010s has needlessly slowed the recovery.

The left needs to accept that many of the criticisms it levels at capitalism do not fit the facts. Life at the bottom of the labour market is not joyous—far from it. However, the lot of workers is improving and entry-level jobs are a much better launch pad to something better than joblessness is. A failure to acknowledge this will lead to government intervention that is at best unnecessary and at worst jeopardises recent progress. The jobs boom seems to be partly down to welfare reforms that the likes of Mr Corbyn have vociferously opposed.

The right should acknowledge that jobs have boomed without the bonfire of regulations that typically forms its labour-market policy. In fact, labour-market rules are proliferating. And although the jury is out on whether rising minimum wages are harming some groups, such as the young, they are not doing damage that is large enough to show up in aggregate.

The jobs boom will not last for ever. Eventually, a recession will kill it off. 


Meanwhile, it deserves a little appreciation.
Correction (May 28th 2019): An earlier version of this article misstated the number of hours that Britons will work this year. Sorry.
This article appeared in the Leaders section of the print edition under the headline "The great jobs boom"

Minggu, 14 Juli 2019

PIDATO VISI INDONESIA JOKOWI

VISI INDONESIA
(Pidato Presiden Terpilih Joko Widodo)
14 Juli 2019

Assalamuallaikum wr. Wb
Salam sejahtera bagi kita semua
Om swastiastu
Namo buddhaya
Salam kebajikan

Bapak, Ibu, saudara-saudara sebangsa dan setanah air.
Seluruh rakyat Indonesia yang saya cintai. Hadirin yang berbahagia.

Kita harus menyadari, kita harus sadar semuanya bahwa sekarang kita hidup dalam sebuah lingkungan global yang sangat dinamis! Fenomena global yang ciri-cirinya kita ketahui, penuh perubahan, penuh kecepatan, penuh risiko, penuh kompleksitas, dan penuh kejutan, yang sering jauh dari kalkulasi kita, sering jauh dari hitungan kita.

Oleh sebab itu, kita harus mencari sebuah MODEL BARU, cara baru, nilai-nilai baru dalam mencari solusi dari setiap masalah dengan INOVASI-INOVASI. Dan kita semuanya harus mau dan akan kita paksa untuk mau. Kita harus meninggalkan cara-cara lama, pola-pola lama, baik dalam mengelola organisasi, baik dalam mengelola lembaga, maupun dalam mengelola pemerintahan. Yang sudah tidak efektif, kita buat menjadi efektif! Yang sudah tidak efisien, kita buat menjadi efisien!

Manajemen seperti inilah yang kita perlukan sekarang ini. Kita harus menuju pada sebuah negara yang lebih produktif, yang memiliki daya saing, yang memiliki fleksibilitas yang tinggi dalam menghadapi perubahan-perubahan itu. Oleh sebab itu, kita menyiapkan tahapan-tahapan besar.

PERTAMA, pembangunan INFRASTRUKTUR akan terus kita lanjutkan! Infrastruktur yang besar-besar sudah kita bangun. Ke depan, kita akan lanjutkan dengan lebih cepat dan menyambungkan infrastruktur besar tersebut, seperti jalan tol, kereta api, pelabuhan, dan bandara dengan kawasan-kawasan produksi rakyat. Kita sambungkan dengan kawasan industri kecil, sambungkan dengan Kawasan Ekonomi Khusus, sambungkan dengan kawasan pariwisata. Kita juga harus menyambungkan infrastruktur besar dengan kawasan persawahan, kawasan perkebunan, dan tambak-tambak perikanan. 

KEDUA, pembangunan SDM. Kita akan memberikan prioritas pembangunan kita pada pembangunan sumber daya manusia. Pembangunan SDM menjadi kunci Indonesia ke depan. Titik dimulainya pembangunan SDM adalah dengan menjamin kesehatan ibu hamil, kesehatan bayi, kesehatan balita, kesehatan anak usia sekolah. Ini merupakan umur emas untuk mencetak manusia Indonesia unggul ke depan. Itu harus dijaga betul. Jangan sampai ada stunting, kematian ibu, atau kematian bayi meningkat. Tugas besar kita di situ!

Kualitas pendidikannya juga akan terus kita tingkatkan. Bisa dipastikan pentingnya VOCATIONAL training, pentingnya VOCATIONAL school. Kita juga akan membangun lembaga Manajemen Talenta Indonesia.

Pemerintah akan mengidentifikasi, memfasilitasi, serta memberikan dukungan pendidikan dan pengembangan diri bagi talenta-talenta Indonesia.

Diaspora yang bertalenta tinggi harus kita berikan dukungan agar memberikan kontribusi besar bagi percepatan pembangunan Indonesia. Kita akan menyiapkan lembaga khusus yang mengurus manajemen talenta ini. Kita akan mengelola talenta-talenta hebat yang bisa membawa negara ini bersaing secara global.

KETIGA, kita harus mengundang INVESTASI yang seluas-luasnya dalam rangka membuka lapangan pekerjaan. Jangan ada yang alergi terhadap investasi.  Dengan cara inilah lapangan pekerjaan akan terbuka sebesar-besarnya. Oleh sebab itu, yang menghambat investasi, semuanya harus dipangkas, baik perizinan yang lambat, berbelit-belit, apalagi ada punglinya! Hati-hati, ke depan saya pastikan akan saya kejar, saya kontrol, saya cek, dan saya hajar kalau diperlukan. Tidak ada lagi hambatan-hambatan investasi karena ini adalah kunci pembuka lapangan pekerjaan.

KEEMPAT, sangat penting bagi kita untuk mereformasi birokrasi kita. Reformasi struktural! Agar lembaga semakin sederhana, semakin simpel, semakin lincah! Hati-hati! Kalau pola pikir, mindset birokrasi tidak berubah, saya pastikan akan saya pangkas!

Kecepatan melayani, kecepatan memberikan izin, menjadi kunci bagi reformasi birokrasi. Akan saya cek sendiri! Akan saya kontrol sendiri! Begitu saya lihat tidak efisien atau tidak efektif, saya pastikan akan saya pangkas, copot pejabatnya. Kalau ada lembaga yang tidak bermanfaat dan bermasalah, akan saya bubarkan!

Tidak ada lagi pola pikir lama! Tidak ada lagi kerja linier, tidak ada lagi kerja rutinitas, tidak ada lagi kerja monoton, tidak ada lagi kerja di zona nyaman. HARUS BERUBAH! Sekali lagi, kita harus berubah. Kita harus membangun nilai-nilai baru dalam bekerja, menuntut kita harus cepat beradaptasi dengan perkembangan zaman. Maka kita harus terus membangun Indonesia yang ADAPTIF, Indonesia yang PRODUKTIF, dan Indonesia yang INOVATIF, Indonesia yang KOMPETITIF.

KELIMA, kita harus menjamin penggunaan APBN yang fokus dan tepat sasaran. Setiap rupiah yang keluar dari APBN, semuanya harus kita pastikan memiliki manfaat ekonomi, memberikan manfaat untuk rakyat, meningkatkan kesejahteraan untuk masyarakat.

BAPAK IBU DAN HADIRIN YANG BERBAHAGIA, namun perlu saya ingatkan bahwa mimpi-mimpi besar hanya bisa terwujud jika kita bersatu! Jika kita optimis! Jika kita percaya diri! Kita harus ingat bahwa negara kita adalah negara besar! Negara dengan 17 ribu pulau. Dengan letak geo-politik yang strategis. Kita adalah negara yang ber-Bhinneka Tunggal Ika! Memiliki kekayaan budaya yang luar biasa. Demografi kita juga sangat kuat! Jumlah penduduk 267 juta jiwa, yang mayoritas di usia produktif.

Kita harus optimis menatap masa depan! Kita harus percaya diri dan berani menghadapi tantangan kompetisi global. Kita harus yakin bahwa kita bisa menjadi salah satu negara terkuat di dunia.

Persatuan dan kesatuan bangsa adalah pengikat utama dalam meraih kemajuan. Persatuan dan persaudaraan kita harus terus kita perkuat! Hanya dengan bersatu, kita akan menjadi negara yang kuat dan disegani di dunia! Ideologi Pancasila adalah satu-satunya ideologi bangsa yang setiap Warga Negara harus menjadi bagian darinya!

Dalam demokrasi, mendukung mati-matian seorang kandidat itu boleh. Mendukung dengan militansi yang tinggi itu juga boleh. Menjadi oposisi itu juga sangat mulia. Silakan. Asal jangan oposisi menimbulkan dendam. Asal jangan oposisi menimbulkan kebencian. Apalagi disertai dengan hinaan, cacian, dan makian.

Kita memiliki norma-norma agama, etika, tata krama, dan budaya yang luhur.

Pancasila adalah rumah kita bersama, rumah bersama kita sebagai saudara sebangsa! Tidak ada toleransi sedikit pun bagi yang mengganggu Pancasila! Yang mempermasalahkan Pancasila! Tidak ada lagi orang Indonesia yang tidak mau ber-Bhinneka Tunggal Ika! Tidak ada lagi orang Indonesia yang tidak toleran terhadap perbedaan! Tidak ada lagi orang Indonesia yang tidak menghargai penganut agama lain, warga suku lain, dan etnis lain.

Sekali lagi, ideologi kita adalah Pancasila. Kita ingin bersama dalam Bhinneka Tunggal Ika, dalam keberagaman. Rukun itu indah. Bersaudara itu indah. Bersatu itu indah.

Saya yakin, semua kita berkomitmen meletakkan demokrasi yang berkeadaban, yang menunjujung tinggi kepribadian Indonesia, yang menunjung tinggi martabat Indonesia, yang akan membawa Indonesia menjadi Indonesia Maju, Adil dan Makmur.

Indonesia Maju adalah Indonesia yang tidak ada satu pun rakyatnya tertinggal untuk meraih cita-citanya. Indonesia yang demokratis, yang hasilnya dinikmati oleh seluruh rakyat. Indonesia yang setiap warga negaranya memiliki hak yang sama di depan hukum. Indonesia yang menguasai ilmu pengetahuan dan teknologi kelas dunia. Indonesia yang mampu menjaga dan mengamankan bangsa dan negara dalam dunia yang semakin kompetitif.

Ini bukanlah tentang aku, atau kamu. Juga bukan tentang kami, atau mereka. Bukan soal Barat atau Timur. Juga bukan Selatan atau Utara. Sekarang bukan saatnya memikirkan itu semua. Tapi ini saatnya memikirkan tentang bangsa kita bersama. Jangan pernah ragu untuk maju karena kita mampu jika kita bersatu!

Terima kasih,
Wassalamu’alaikum warahmatullahi wabarakatuh.

Bogor, 14 Juli 2019
CALON PRESIDEN TERPILIH,
JOKO WIDODO

Kamis, 11 Juli 2019

LION AIR - MANADO - TIONGKOK

*Kegilaan Rusdi Kirana*
*Terobos Vacuum* 💫

PABRIK, BUAH TROPIS DAN TOURISM

Oleh : _DAHLAN  ISKAN_

TIONGKOK itu ibarat vacuum cleaner.
Kita bisa kesedot.
Tiongkok tidak bermaksud menyedot pun, negara sekitarnya bisa kesedot sendiri.
Ekonominya.

PABRIK: KAPASITAS PRODUKSI BANGSA
Sekarang Tiongkok lagi kelebihan KAPASITAS.
Barang-barang mereka menjadi murah.
PABRIK-PABRIKnya sudah terlalu banyak.
Dan terlalu besar.
PABRIK apa pun.
Bidang apa pun.
Benar-benar kelebihan pabrik.
Kebesaran PABRIK.
Tanpa bermaksud membanjiri negara lain pun, banjir produk Tiongkok terjadi dengan sendirinya. 

Apa yang harus kita perbuat?
Marah? Menghancurkan  mesin vacuum cleaner itu? Menyumbat slangnya?
Agar kita tidak kesedot? 
Rasanya akan sia-sia.
Bahkan destruktif.
Ibarat membuat asap dengan cara membakar diri sendiri. 

Kita harus menyesal.
Dulu.
Dulu sekali.
Sekitar tahun 2005.
Kita tidak mau memanfaatkan kebijakan "rukun tetangga" dari Tiongkok. Malaysia-lah, dan terutama Thailand, yang panen raya.
Pejabat kita waktu itu tidak tahu bahwa ada kebijakan ini: Tiongkok membuka diri secara khusus untuk menerima produk pertanian
dari negara tetangga. Tarif pajaknya khusus. Seperti sayur dan buah tertentu. 

Kebijakan itu disebut early harvest policy. 
Sayang, kita melewatkannya begitu saja.
Justru kita kebanjiran buah dari Tiongkok. 

Kita terlambat.

Tapi, kesempatan masih luas.
Penduduk Tiongkok terlalu besar: 1,3 miliar.

Nafsu makannya terlalu baik: Perlu makanan apa saja.
Mereka pun mampu membelinya. 

Semua orang kuat punya titik lemahnya sendiri.
Tiongkok sekalipun.
Ia tidak bisa menghasilkan "buah tropik" atau "sayur tropik".
Titik itulah yang harus kita totok dengan kekuatan totok yang telak.

PERKEBUNAN BUAH TROPIK HARUS JADI KEKUATAN INDONESIA

Harus jadi senjata totok yang kuat. 

Entah siapa yang akan bisa jadi panglima di sektor itu.
BUMN?

Yang dulu sudah mulai tanam PISANG, manggis, JAMBU, dan DURIAN secara besar-besaran? dan KELAPA
Apa kabar perkembangannya? 

Titik lemah lainnya adalah ini: Orang Tiongkok senang bepergian. TOURISM
Seperti kita juga.

TOURISM ADALAH SENJATA INDONESIA JUGA
Bisa jadi senjata totok berikutnya.
Kita tidak perlu bercocok tanam.
Cukup tersenyum-senyum sepanjang tahun.
Apa susahnya tersenyum? 

Panglima di sektor itu sudah ketahuan: swasta.
Lion Air.

Sudah teruji melakukan terobosan selama hampir satu tahun terakhir. 

Terobosan Lion ini tidak main-main.
Sulit dilakukan siapa pun: membuka penerbangan langsung ke jantung-jantung Tiongkok.
Dari dan ke MANADO

CHONGQING - MANADO

Adakah orang gila yang mau membuka penerbangan dari kota seperti Chongqing ke Manado?
Selain Rusdi Kirana?
Si pemilik Lion?
Di mana itu Chongqing?
Jangankan letaknya. Bagaimana mengucapkan nama kota itu saja tidak mudah. 

Kota tersebut adalah kota pedalaman yang paling pedalaman.
Dulu kota itu bagian dari Provinsi Sichuan.
Lebih miskin daripada NTT.
Kini Chongqing berdiri sendiri.
Sebagai kota besar langsung di bawah pemerintah pusat. Penduduknya lebih dari 52 juta.
Hampir dua kali penduduk Jatim.
Gedung bertingkatnya melebihi gedung bertingkat di Surabaya, Bandung, Semarang, dan Medan yang dijadikan satu.
Kotanya bergunung-gunung.
Indah.
Dibelah Bengawan Chang Jiang, sungai terpanjang ketiga di dunia. 

Mungkin awalnya penduduk Chongqing juga tidak tahu di mana itu Manado. 

Lion juga buka penerbangan langsung Manado-Wuhan.
Kota pedalaman Tiongkok yang lain lagi.
Ibu kota Hubei.
Provinsi yang berpenduduk sekitar 100 juta. 

Gila. Empat kali seminggu Lion Air menerbangi Manado-Tiongkok.
Itu hanya bisa dilakukan oleh orang yang kelebihan kapasitas.
Singapura yang dulu dikenal pandai menyedot penumpang dari negara lain.
Kini Lion dengan kelebihan kapasitasnya mencoba menyedot penumpang negara lain. 

Tahun lalu saja, selama setengah tahun Lion bisa menyedot 40 ribu penumpang Tiongkok.
Dibawa ke Manado.

Kota kecil itu sampai terkaget-kaget.
Tahun ini, menurut perkiraan Dino Gobel dari North Sulawesi Tourims Board, bisa dua kali lipatnya.

Ternyata mereka menyukai Pulau Lihaga yang masih perawan.
Bukan Bunaken. 

Saya kagum pada langkah Lion tersebut.
Itulah usaha nyata untuk "melawan" Tiongkok secara benar.
Bukan berteriak anti asing.

Minggu, 07 Juli 2019

LEADERS JUST DO NOTHING

https://www.forbes.com/sites/janbruce/2014/04/08/leadership-101-how-doing-nothing-makes-you-a-better-leader/#642f04036c96

LEADERSHIP LESSON - DOING NOTHING*
.



10,953 views|Apr 8, 2014,1:54 pm

Leadership 101: How Doing Nothing Makes You A Better Leader

Jan Bruce

I write about stress and being agile and adaptable in the workplace.

Here’s the truth: No one ever reached unimaginable heights or made millions by hitting “reply” all day. It’s easy to do—and we all do it. Email is nothing more than our effort to get others to help us with our own to-do lists. And that’s fine—but if it consumes the lion’s share of your waking hours, there’s a problem.
Busyness, manifest perhaps most tangibly in our bulging inboxes, is a fact of business life, and a constant wear on your time and energy. But keeping all the day-to-day balls in the air is not your only job. You also have the responsibility and privilege of creative work, the work of envisioning what you could do, not just check the boxes of what you are doing. In fact, when you let busyness, and the stress that comes with it, consume you, you are maxing out your mental resources long before you’ve lived up to your potential as a leader. And at this point you also deprive your business of the true leadership it needs.

The problem, as an article in the Harvard Business Review (“The Case for Slacking Off”) explains, is that “doing nothing” is socially unacceptable. “As an adult, have you ever found anybody at work telling you to do nothing — to just take your time and reflect?” writes Manfred F. R. Kets de Vries, professor of leadership development and organizational change.

“For most of us, doing nothing is associated with being irresponsible, with being on the wrong track, or even worse, with wasting our lives.”
Quite the opposite is true. It takes a kind of discipline and courage to step away from the constant clamor and give your mind time to reflect, recharge, and be decidedly un-busy.
And there’s fascinating research into the power of letting your mind wander that suggests that daydreaming can help “consolidate memories and synthesize disparate ideas and plans, yielding a greater sense of identity and personal meaning,” says Scott Barry Kaufman, Ph.D. in his recent feature (“Days of Glory”) in Psychology Today.

Daydreaming helps us see ourselves more clearly, since much of our daydreaming is focused on our future selves. Kaufman cites the work of E. Paul Torrance in that story, whose groundbreaking 30-year study of creative achievement, explored a variety of indicators of future creative and scholastic promise. Kaufman says that Torrance “found that the best predictor of lifelong personal and publicly recognized creative achievement—even better than academic indicators such as school grades and IQ scores—was the extent to which children had a clear future-focused image of themselves.”

The Forbes E-book: Find And Keep Your Dream Job 
The Definitive Careers Guide From Forbesencompasses every aspect of the job hunt from interview to promotion. Written by some of Forbes’ best careers and leadership writers, it is available now for download.

Bottom line: Daydreaming, musing, creative visioning…call what you want— by any name it is hardly a waste of time. In fact, it’s the source of your future genius and the promise of your future self. But you can’t do it checking email.

Stop Being Busy
Try to race to keep up with busy, and you lose. The only other option is to control the black hole of busyness  and keep it from swallowing your life. Here are a few ways to do it  and be a more effective leader in the not-doing:

1. Clear the decks.
Push everything you have on your desk to the side so that you’re faced with a clear surface and whatever note taking method you like to use (paper and pen, Evernote, etc). Now, without looking at your email (this is key), ask yourself, what would you most like to have done this week? Break ground on a new project, have that critical conversation with your team, put action steps around a pending deadline. What’s your highest priority (versus someone else’s)? Write down three to five things that you want to have done. Remember – less is more; too many and you’ll drown in busyness again.

2. Question your calendar.
What’s on there for today, tomorrow, and the next day—and how beholden are you to it?
Which of those meetings or obligations can be put off til next week or even next month? When everything’s a priority, nothing is, so it’s worth getting control of your time and recognizing what best serves you right now.

3. Daydream.
With email off, and a block of time (even just 45 minutes will do), and no one howling at your door, turn to a blank page and sit there. No typing, not toggling back and forth between browser windows, no dashing off a note to this person or that person. Sit and think, draw, visualize. Bring your full, open, creative mind to the table and let it breathe for a change. Don’t force a resolution or an idea. Let your brain do what it does best when put in a constrained yet unstructured space, and it may surprise you. As thoughts come to mind, jot them down.

4. Do nothing somewhere else.
Or better yet, leave the building altogether for a 20 minute walk without your phone. Let body and mind roam for a bit off the grid. Remember, try not to think or solve something, just roam.

5. Try not solving just reviewing.
This is the counter to the only touch it once, rapid fire approach to decision-making. But for significant issues, biggest dilemma or disappointment. Rather than try to fix it, get daydreamy about it for a few minutes:  think of yourself as walking around it, viewing it from all sides, vision how where it might lead you and your enterprise.
You will be surprised what happens when you can let yourself just be for a few periods each week. Because in fact you’re not “doing nothing”; quite the opposite. You’re letting busy go so you can do the bigger, non-linear work that supports the future of your business and your career.


Jan BruceContributor



”Beware the barrenness of a busy life.”_ - Socrates
.

Leaders!!! Do Nothing!!! Hah??? Pemimpin… Tidak melakukan apa-apa itu OK? Di tahun 2014 ada sebuah artikel yang dipublikasi oleh Majalah terkenal Forbes.com yang judulnya seperti ini: Leadership 101: How Doing Nothing Makes You A Better Leader by Jan Bruce. Artikel ini menjadi bahan diskusi menarik dikalangan banyak CEO yang di seluruh dunia.

Link: https://www.forbes.com/sites/janbruce/2014/04/08/leadership-101-how-doing-nothing-makes-you-a-better-leader/#163e718c6c96

Mari kita liat cerita metafora berikut yang barangkali bisa memberikan inspirasi pada kita apa itu Pemimpin yang tidak melakukan apa-apa.

Disebuah sungai yang tenang di sebuah hutan yang ada di Amerika Utara seekor buaya tua tampak mengapung disebuah pinggiran, terlihat bermalas-malasan. Seekor buaya muda menghampirinya dan berkata: “Saya mendengar dari banyak buaya bahwa kamu adalah pemburu paling ganas di sepanjang sungai ini. Ajarkan donk kepada saya bagaimana caranya menjadi seperti diri kamu.”

Buaya Tua tersebut membuka matanya dan menatap buaya muda tersebut, kemudian dia menutup matanya dan tertidur kembali di atas air.

Merasa dicuekin, buaya muda itu pun bertekad memberi “contoh” bahwa ia juga bisa berburu dengan cepat. Ia pun menyelam dan segera saja mengejar ikan lele yang sedang berenang di sungai tersebut.

Kurang lebih beberapa jam kemudian, buaya muda itu kembali ke buaya tua itu. Buaya tua itu masih tidur disana. Ia pun mulai menyombongkan dirinya tentang kesuksesannya barusan dan keberhasilannya mendapatkan 3 ekor ikan lele yang lumayan besar.  Ia berkata: “Barangkali mereka semua salah… kamu bukanlah pemburu yang ganas seperti yang mereka katakan… Ia pun tertawa…”

Sang Buaya tua kembali membuka matanya, tidak berkata apa-apa dan kembali tertidur lagi disana. Kali ini malahan ada beberapa ekor burung yang hinggap di atas badan buaya tua itu. Tetapi buaya itu diam saja tidak bergerak.

Tidak beberapa lama kemudian serombongan bison datang ke sungai tersebut untuk minum air hanya beberapa centi-meter dari kepala buaya tersebut. Dalam sebuah gerakan yang sangat cepat, buaya tua itu menancapkan gigi taringnya ke leher bison tersebut lalu menyeretnya ke dalam sungai.

Sang buaya muda begitu shok melihat kejadian yang cepat seperti itu dan terus bengong menyaksikan buaya tua itu memakan daging bison yang beratnya hampir 0.5 Ton tersebut. “Ba.. ba… bagaimana kamu bisa melakukannya seperti itu?”

Dengan mulutnya yang penuh dengan daging, buaya tua itu akhirnya merespon, “Saya tidak melakukan apa-apa, diam dan mengamati. Itulah hal terpenting yang saya lakukan.”

Banyak pemimpin yang sibuk menjalankan bisnisnya.
Mereka melakukan Business katanya… padahal tanpa mereka sadari mereka sesungguhnya melakukan Busyness. Kesibukan-kesibukan yang menghabiskan waktunya untuk mengamati dan mengambil peluang besar bagi kemajuan usaha dan bisnisnya.

Apalagi di era globalisasi seperti sekarang ini. Dengan arus informasi yang luar biasa dan organisasi yang multi ruwet, maka bisa jadi seorang pemimpin bisnis menghabiskan waktunya lebih dari 8 jam untuk dealing dengan proses-proses yang harus dilaluinya. Rapat, tanda tangan yang segunung, laporan-laporan yang harus dibaca dll. Sehingga ya… pemimpin tersebut sangat-sangat sibuk. Dengan volume kesibukan seperti itu Pemimpin menjadi sulit untuk berpikir jernih untuk membuat loncatan-loncatan baru bagi usaha yang ditekuninya.

*Quality VS Quantities*
Jika kita melihat pekerjaan seorang pemimpin, jika items (Quantities) kerja pemimpin tersebut terlalu banyak, bagaimana mereka bisa mempunyai Quality yang baik? Untuk meningkatkan kualitas kerja mereka maka sesungguhnya pemimpin harus pintar-pintar mendelegasi pekerjaannya supaya …. Apa? Supaya dia tidak ada kerjaan apa-apa… Dengan begitu ia bisa melihat setiap items pekerjaannya yang sekarang dikerjakan anak buahnya lalu meningkatkan kualitas bahkan output dari pekerjaan tersebut.

Jika Anda terlalu sibuk dan tidak mampu menghilangkan 50% pekerjaan anda, belajarlah dari Tim Ferriss, ia punya buku yang menarik yang bahkan sudah diterjemahkan dalam bahasa Indonesia. The 4-hour workweek.

Sekedar informasi aja, beberapa founder-founder perusahaan terkenal seperti Steve Jobs, Mark Zuckerberg, Tim Ferriss biasanya mempunyai apa yang disebut sebagai “Think Weeks”, mereka memasukkannya di dalam agenda tahunannya mereka “Think Weeks” tersebut. Mereka menghabiskan minggu itu untuk melakukan refleksi, membaca buku, berpikir dan “menyucikan” dirinya dari dunia bisnis yang digelutinya. Bahkan istilah “Think Weeks” ini menjadi terkenal karena Bill Gates yang sering mempromosikannya.

Jadi jika Anda adalah pemimpin perusahaan / organisasi, jangan lupa, set up Think weeks dan buatlah tangkapan besar seperti bison di atas….


Author of Hacking Your Mind Book

150 Orang Indonesia Terkaya 2018

https://m.liputan6.com/bisnis/read/3600690/daftar-terbaru-150-orang-terkaya-di-indonesia

Berikut daftar lengkap orang-orang terkaya di Indonesia pada tahun 2018:

150. Nadiem Makarim, 33
Go-Jek (StartUp Digital)
USD 100 juta

149. Achmad Zaky, 31
Bukalapak (StartUp Ecommerce)
USD 105 juta

148. William Tanuwijaya, 36
Tokopedia (StartUp ECommerce)
USD 130 juta

147. Tandean Rustandy, 62
Arwana Citramulia
USD 135 juta

146. Ferry Unardi, 30
Traveloka (StartUp Travel Ticket Commerce)
USD 145 juta

145. Soedjono, 70
Wira Sakti Adimulya
USD 145 juta
144. GS Margono, 79
Gapura Prima
USD 145 juta
143. Ludijanto Setijo, 49 dan keluarga
Pan Brothers
USD 150 juta
142. Jacobus Busono, 79
Pura Group
USD 152 juta
141. Mintarjo Halim, 64
Sandratex
USD 150 juta
140. Batihalim Stefanus, 54
Nojorono Tobacco
USD 155 jut


139. A Tong, 75
Roda Vivatex
USD 160 juta
138. Honggo Wendratno, 53
Arsari Pratama
USD 175 juta
137. Rudy Unjoto, 71
Daliatex Kusuma
USD 180 juta
136. Anna Bambang Surjo Sunindar, 66
Kirana Tanker
USD 180 juta
135. Shindo Sumidomo, 63
Siantar Top
USD 185 juta
134. Iskandar Widyadi, 82
Bank Jasa Jakarta
USD 185 juta

133. Ricardo Gelael, 59
Fast Food Indonesia (KFC Restaurant Chain)
USD 185 juta

132. G Lukman Pudjiadi, 63
Jayakarta Group
USD 200 juta

131. Patrick Walujo,
Northstar Capital (Investment House of GOJEK)
USD 200 juta

130. Siti Hardijanti Rukmana, 71
Citra Lamtoro Gung Persada
USD 205 juta

129. Mardjoeki Atmadiredja, 75
Surya Toto Indonesia
USD 220 juta
128. Samin Tan, 57
Borneo Lumbung Energy and Metal
USD 230 juta

127. Stanley S Atmadja, 63
Asco Automotive
USD 235 juta

126. Harry Sanusi, 52
Kino Group
USD 235 juta
125. Sri Sultan Hamengkubuwono X, 74
Sultan Yogyakarta
USD 250 juta
124. Bambang Trihatmodjo, 64
Asriland
USD 250 juta

123. Ilham Habibie, 54 dan Thareq Habibie, 52
Ilthabi Rekatama
USD 250 juta

122. Budi Purnomo Hadisurjo, 82
Optik Melawai
USD 250 juta
121. Sendi Bingei, 90
Sumatra Tobacco Trading
USD 252 juta
120. Anton Setiawan, 73
Tunas Group
USD 255 juta
 
119. Rachmat Gobel, 57
Gobel International
USD 260 juta

118. Johanes B. Kotjo, 73
Apac Group
USD 260 juta

117. Pontjo Sutowo, 68
Nugra Sentana Group
USD 265 juta

116. Kaharudin Ongko, 82
Ongko Group
USD 270 juta

115. Elizabeth Sindoro, 61
Dan Liris, Paramount Group
USD 270 juta
114. Tan Tjai Kie, 65
Gunung Garuda Steel
USD 300 juta

113. Siswono Yodohusodo, 77
Bangun Cipta Sarana
USD 300 juta

112. Karmaka Surjaudaja, 85
OCBC NISP
USD 300 juta

111. Soetjipto Nagaria, 78
Summarecon (PROPERTY, MALL, REAL ESTATE, APARTMENTS)
USD 305 juta
 

110. Harry Susilo, 77
Sekar Group
USD 305 juta
109. Henry Onggo, 85
Ratu Sayang Group
USD 310 juta

108. Chandra Lie, 55 dan Hendry Lie, 53
Sriwijaya Air
USD 320 juta

107. Boyke Gozali, 70
Mitra Adi Perkasa (LUXURY RETAILS)
USD 350 juta

106. Didi Dawis, 73
Ling Brothers
USD 350 juta

105. Djoenaedi Joesoef, 86
Konimex (PHARMACEUTICALS)
USD 355 juta

104. Oesman Sapta Odang, 69
OSO Group
USD 355 juta

103. Johnny Widjaja, 86
Sintesa Group
USD 360 juta
102. Iwan Lukminto, 43
Sritex Group
USD 365 juta
101. Muljadi Budiman, 64
Honda Prospect Motor (CAR SALES DISTRIBUTOR)
USD 370 juta
 

100. Tatang Hermawan, 68
Fuju Palapa Textiles, Bank Parahyangan
USD 375 juta

99. Sabana Prawidjaja, 77
Ultrajaya Group (FMCG Milk)
USD 390 juta

98. Jahja Santoso, 74
Sanbe Farma (PHARMACEUTICAL)
USD 395 juta

97.Rudolph Merukh, 53 dan Lucky Merukh, 54
Merukh Enterprises
USD 400 juta
96. Yos Sutomo, 88
Sumber Mas
USD 405 juta

95. Dahlan Iskan, 69
Jawa Pos Group (PRINTED MEDIA NEWSPAPER)
USD 410 juta

94. Iwan Budi Brasali, 72 dan Aldo Brasali, 52
Brasali Group
USD 420 juta

93. Widarto, 74
Sungai Budi Group
USD 415 juta
92. Heru Hidayat, 46
Inti Agri Resources
USD 440 juta
91. Kris Taenar Wiluan, 70
Citra Mas Group
USD 445 juta

90. Sukamdani Sahid Gitosardjono dan keluarga
Sahid Group (PROPERTY, HOTEL)
USD 450 juta

89. Hendro Gondokusumo, 70
Intiland (PROPERTY, MALL, OFFICE BUILDING)
USD 456 juta

88. Rosan Roeslani, 50
Recapital (INVESTMENT HOUSE)
USD 460 juta

87. Stefanus Lo, 50 dan keluarga
Frank & Co
USD 465 juta

86. Jimmy Masrin, 57
Lautan Luas Group
USD 490 juta

85. Sandiaga Uno, 50
Saratoga, Recapital (INVESTMENT HOUSE)
USD 300 juta

84. Rudy Suliawan, 67
Karang Mas Sejahtera
USD 500 juta
83. Henry Pribadi, 71
Napan Group
USD 500 juta
82. Ginawan Tjondro, 64
CNI Group
USD 510 juta
81. Amirsjah Risjad, 51
Risjadson Group
USD 515 juta
80. Purnomo Prawiro, 80
Blue Bird Group (TAXI TRANSPORT SERVICE)
USD 520 juta

79. George Tahija, 60 dan Sjakon Tahija, 65
Austindo Nusantara Jaya
USD 550 juta

78. Alim Markus, 67
Maspion Group (ELECTRONIC HH FACTORY)
USD 560 juta

77. Surya Dharma Paloh, 68
Media Indonesia (TV, Media)
USD 575 juta

76. Tan Kian, 61
Dua Mutiara
USD 600 juta
75. Sudhamek, 62
Garuda Food (FMCG Food Retail)
USD 600 juta

74. Trihatma K Haliman, 66
Agung Podomoro Group (PROPERTY)
USD 605 juta

73. Sofjan Wanandi, 74
Gemala Santini
USD 610 juta

72. Paulus Tumewu, 66
Ramayana Group (Fashion Supermarket)
USD 610 juta

71. Soegiharto Sosrodjoyo, 84
Rekso Group (FMCG, McDonalds)
USD 610 juta

70. Benny Suherman, 71
Studio 21 Group (Movie Theatre XXI)
USD 610 juta

69. Sutanto Djuhar, 89
First Pacific
USD 630 juta

68. Harjo Sutanto, 78
Wings Group (FMCG)
USD 630 juta

67. Bachtiar Karim, 61
Musim Mas
USD 630 juta

66. Subianto, Tjandra, 74
Ateja Group
USD 630 juta

65. Mucki Tan, 59
Rodamas Group
USD 654 juta

64. Mohammad Reza Chalid, 58
Global Energy Resources (Mafia Migas)
USD 650 juta

63. AHK Hamami, 84
ABM Investment (TRAKINDO GROUP)
USD 650 juta

62. Eka Tjandranegara, 71
Mulia Group
USD 650 juta

61. John Chuan, 68
Ceres Indonesia, Petra Food (FMCG)
USD 655 juta

60. Hutomo Mandala Putra, 56
Humpuss
USD 670 juta

59. Jan Darmadi, 77
Jan Darmadi Group
USD 740 juta

58. Osbert Lyman, 68
Lyman Group
USD 760 juta

57. Boenjamin Setiawan 85, dan keluarga
Kalbe Farma (PHARMACEUTICAL)
USD 840 juta

56. Alexander Tedja, 74 dan Melinda Tedja, 74
Pakuwon Group (MALLS, APARTMENTS, REAL ESTATE)
USD 870 juta

55. Kuncoro Wibowo, 63
Ace Hardware (RETAIL CHAIN HARDWARE SUPERMARKET)
USD 900 juta

54. Jusuf Kalla, 76 dan keluarga
Kalla Group
USD 900 juta

53. Desi Sulistio Hidayat, 87 dan keluarga
Sido Muncul (PHARMACEUTICAL)
USD 905 juta

52. Luntungan Honoris, 69
Modern Group
USD 910 juta

51. Hashim Djojohadikusumo, 64
Arsari Group
USD 915 juta
 

50. Tomy Winata, 60
Artha Graha Network
USD 930 juta

49. Johan Lensa, 68
J Resources
USD 955 juta

48. Gunawan Jusuf, 64
Sugar Group Companies (GULA FMCG)
USD 965 juta

47. Sugianto Kusuma, 67
Agung Sedayu, Bank Artha Graha
USD 970 juta

46. Ninin Subianto, 52
Persada Capital Group (ADARO, TRIPUTRA)
USD 980 juta

45. Winarko Sulistyo, 72
Fajar Surya Wisesa
USD 1 miliar

44. Martias Fangiono, 80 dan Tjiliandra Fangiono, 42
First Resources
USD 1,15 miliar

43. Husein Djojonegoro, 68
ABC, Orang Tua Group (FMCG Snacks)
USD 1,2 miliar

42. Rusdi Kirana, 55
Lion Air Group (AIRLINE)
USD 1,2 miliar

41. Jogi Hendra Atmadja, 72
Mayora Group (FMCG Snacks, Beverages, Food)
USD 1,25 miliar

40. Mu'min Ali Gunawan, 79
Panin Group
USD 1,3 miliar

39. Arifin Panigoro, 72 dan Hilmi Panigoro, 62
Medco International (Oil Mining)
USD 1,3 miliar

38. Surya Darmada, 68
Duta Palma Nusantara Group
USD 1,3 miliar

37. Hartadi Angkosubroto, 65 dan Husodo Angkosubroto, 63
Gunung Sewu Group (PROPERTY)
USD 1,35 miliar

36. Handojo Muljadi, 52
Tempo Scan Group (Pharmaceutical)
USD 1, 35 miliar

35. Prajogo Pangestu, 68
Barito Pacific Group
USD 1,38 miliar

34. Handojo Santoso, 55
Japfa Comfeed Group (Livestock , FMCG)
USD 1,4 miliar

33. Djoko Susanto, 69
Sumber Alfaria Trijaya (RETAIL SUPERMARKET)
USD 1,4 miliar

32. Murdaya Poo, 77 dan Siti Hartati Murdaya, 72
Central Cipta Murdaya (PROPERTY)
USD 1,4 miliar

31. Ciputra, 86
Ciputra Group (PROPERTY, REAL ESTATE)
USD 1,4 miliar

30. Dato Low Tuck Kwong, 69
Bayan Resources
USD 1,45 miliar

29. Garibaldi "Boy" Thohir, 53
TNT Group, Adaro Group
USD 1,45 miliar

28. The Nin King, 87
Argo Manunggal Group
USD 1,5 miliar

27. Kiki Barki, 78
Harum Energy Group
USD 1,5 miliar

26. Haryanto Adikoesoemo, 72
AKR Corporindo
USD 1,5 miliar

25. Wiwoho Basuki Tjokronegoro, 79
Indika Energy
USD 1,5 miliar

24. Lim Hariyanto Wijaya Sarwono, 89
Harita Group
USD 1,5 miliar

23. Benjamin Jiaravanon, 45 dan Jialipto Jiaravanon, 43
Charoen Pokphand Indonesia
USD 1,6 milair

22. Agus Lasmono Sudwikatmono, 46
Indika Energy
1,6 miliar

21. Jakob Oetama, 87 dan Lilik Oetama
Kompas Gramedia Group
USD 1,65 miliar

20. Martua Sitorus, 59
GANDA Group (Palm Oil)
USD 1,7 miliar

19. Hary Tanoesoedibjo, 53
MNC Group
USD 1,8 miliar

18. Aksa Mahmud, 72
Bosowa Corporation
USD 1,8 miliar

17. Eddy Sariaatmadja, 67 dan Fofo Sariaatmadja, 55
Elang Mahkota Technology Group (TV, MEDIA)
USD 1,8 miliar

16. Peter Sondakh, 67
Rajawali Group
USD 1,8 miliar

15. Mochtar Riady, 89
Lippo Group (PROPERTY, MALL, REAL ESTATE, RETAIL, TECH PLATFORM, HOSPITAL, SCHOOL, F&B OUTLETS, BANKS)
USD 2 miliar

14. Sjamsul Nursalim, 75
Gajah Tunggal Group
USD 2 miliar

13. Edwin Soeryadjaya, 68
Saratoga (INVESTMENT HOUSE)
USD 2 miliar

12. Aburizal Bakrie, 70
Bakrie Group
USD 2,05 miliar

11. Eddy William Katuari, 66
Wings Group (FMCG)
USD 2,1 miliar

10. Dato Sri Tahir, 66

Mayapada Banking Group

USD 2,15 miliar

9. Sukanto Tanoto, 68

Royal Golden Eagle

USD 2,7 miliar

8. Putera Sampoerna, 68

Sampoerna Strategic

USD 4,3 miliar

7. Theodore P. Rachmat, 69

Triputra Group, Adaro

USD 4,5 miliar

6. Sri Prakash Lohia, 69

Indorama

USD 4,5 miliar

5. Chairul Tanjung, 56

CT Corp

USD 4,6 miliar

4. Susilo Wonowidjojo, 61

Gudang Garam

USD 11 miliar

3. Anthoni Salim, 69

First Pacific

USD 11,5 miliar

2. Eka Tjipta Widjaja, 95

Sinar Mas Group (PROPERTY, REAL ESTATE, PULP PAPER, PALM OIL, BANK)

USD 13,9 miliar

1. Robert Hartono, 77 dan Michael Hartono, 78

Djarum, BCA

USD 21 miliar

Building the AI Powered Organization

https://hbr.org/2019/07/building-the-ai-powered-organization


Building the AI-Powered Organization


FROM THE JULY–AUGUST 2019 ISSUE


Artificial intelligence is reshaping business—though not at the blistering pace many assume. True, AI is now guiding decisions on everything from crop harvests to bank loans, and once pie-in-the-sky prospects such as totally automated customer service are on the horizon. Th
e technologies that enable AI, like development platforms and vast processing power and data storage, are advancing rapidly and becoming increasingly affordable.

The time seems ripe for companies to capitalize on AI. Indeed, we estimate that AI will add $13 trillion to the global economy over the next decade.

Yet, despite the promise of AI, many organizations’ efforts with it are falling short. We’ve surveyed thousands of executives about how their companies use and organize for AI and advanced analytics, and our data shows that only 8% of firms engage in core practices that support widespread adoption. Most firms have run only ad hoc pilots or are applying AI in just a single business process.
Why the slow prog­ress?

At the highest level, it’s a reflection of a failure to rewire the organization. In our surveys and our work with hundreds of clients, we’ve seen that AI initiatives face formidable cultural and organizational barriers. But we’ve also seen that leaders who at the outset take steps to break down those barriers can effectively capture AI’s opportunities.

Making the Shift

One of the biggest mistakes leaders make is to view AI as a plug-and-play technology with immediate returns.

Deciding to get a few projects up and running, they begin investing millions in data infrastructure, AI software tools, data expertise, and model development. Some of the pilots manage to eke out small gains in pockets of organizations. But then months or years pass without bringing the big wins executives expected. Firms struggle to move from the pilots to companywide programs—and from a focus on discrete business problems, such as improved customer segmentation, to big business challenges, like optimizing the entire customer journey.

Leaders also often think too narrowly about AI requirements. While cutting-edge technology and talent are certainly needed, it’s equally important to align a company’s culture, structure, and ways of working to support broad AI adoption. But at most businesses that aren’t born digital, traditional mindsets and ways of working run counter to those needed for AI.
To scale up AI, companies must make three shifts:

From siloed work to interdisciplinary collaboration.

AI has the biggest impact when it’s developed by cross-functional teams with a mix of skills and perspectives. Having business and operational people work side by side with analytics experts will ensure that initiatives address broad organizational priorities, not just isolated business issues.

Diverse teams can also think through the operational changes new applications may require—they’re likelier to recognize, say, that the introduction of an algorithm that predicts maintenance needs should be accompanied by an overhaul of maintenance workflows. And when development teams involve end users in the design of applications, the chances of adoption increase dramatically.

From experience-based, leader-driven decision making to data-driven decision making at the front line.

When AI is adopted broadly, employees up and down the hierarchy will augment their own judgment and intuition with algorithms’ recommendations to arrive at better answers than either humans or machines could reach on their own. But for this approach to work, people at all levels have to trust the algorithms’ suggestions and feel empowered to make decisions—and that means abandoning the traditional top-down approach. If employees have to consult a higher-up before taking action, that will inhibit the use of AI.


LEONARDO ULIAN

Decision processes shifted dramatically at one organization when it replaced a complex manual method for scheduling events with a new AI system. Historically, the firm’s event planners had used colored tags, pins, and stickers to track conflicts, participants’ preferences, and other considerations. They’d often relied on gut instinct and on input from senior managers, who also were operating on their instincts, to make decisions.

The new system rapidly analyzed the vast range of scheduling permutations, using first one algorithm to distill hundreds of millions of options into millions of scenarios, and then another algorithm to boil down those millions into just hundreds, ranking the optimal schedules for each participant.

Experienced human planners then applied their expertise to make final decisions supported by the data, without the need to get input from their leaders. The planners adopted the tool readily, trusting its output because they’d helped set its parameters and constraints and knew that they themselves would make the final call.

From rigid and risk-averse to agile, experimental, and adaptable.

Organizations must shed the mindset that an idea needs to be fully baked or a business tool must have every bell and whistle before it’s deployed. On the first iteration, AI applications rarely have all their desired functionality. A test-and-learn mentality will reframe mistakes as a source of discoveries, reducing the fear of failure. Getting early user feedback and incorporating it into the next version will allow firms to correct minor issues before they become costly problems. Development will speed up, enabling small AI teams to create minimum viable products in a matter of weeks rather than months.

Such fundamental shifts don’t come easily. They require leaders to prepare, motivate, and equip the workforce to make a change. But leaders must first be prepared themselves. We’ve seen failure after failure caused by the lack of a foundational understanding of AI among senior executives.(Further on, we’ll discuss how analytics academies can help leaders acquire that understanding.)

Setting Up for Success

To get employees on board and smooth the way for successful AI launches, leaders should devote early attention to several tasks:

Explaining why.

A compelling story helps organizations understand the urgency of change initiatives and how all will benefit from them. This is particularly critical with AI projects, because fear that AI will take away jobs increases employees’ resistance to it.

Leaders have to provide a vision that rallies everyone around a common goal. Workers must understand why AI is important to the business and how they’ll fit into a new, AI-oriented culture. In particular, they need reassurance that AI will enhance rather than diminish or even eliminate their roles. (Our research shows that the majority of workers will need to adapt to using AI rather than be replaced by AI.)

At most firms that aren’t born digital, mindsets run counter to those needed for AI.

When a large retail conglomerate wanted to get its employees behind its AI strategy, management presented it as an existential imperative. Leaders described the threat that digital retailers posed and how AI could help fend it off by improving the firm’s operational efficiency and responsiveness. By issuing a call to arms in a fight for survival, management underscored the critical role that employees had to play.
In sharing their vision, the company’s leaders put a spotlight on workers who had piloted a new AI tool that helped them optimize stores’ product assortments and increase revenue. That inspired other workers to imagine how AI could augment and elevate their performance.

Anticipating unique barriers to change.

Some obstacles, such as workers’ fear of becoming obsolete, are common across organizations. But a company’s culture may also have distinctive characteristics that contribute to resistance. For example, if a company has relationship managers who pride themselves on being attuned to customer needs, they may reject the notion that a machine could have better ideas about what customers want and ignore an AI tool’s tailored product recommendations. And managers in large organizations who believe their status is based on the number of people they oversee might object to the decentralized decision making or reduction in reports that AI could allow.
In other cases, siloed processes can inhibit the broad adoption of AI. Organizations that assign budgets by function or business unit may struggle to assemble interdisciplinary agile teams, for example.

Some solutions can be found by reviewing how past change initiatives overcame barriers. Others may involve aligning AI initiatives with the very cultural values that seem like obstacles. At one financial institution with a strong emphasis on relationship banking, for example, leaders highlighted AI’s ability to enhance ties with customers. The bank created a booklet for relationship managers that showed how combining their expertise and skills with AI’s tailored product recommendations could improve customers’ experiences and increase revenue and profit. The AI adoption program also included a contest for sales conversions driven by using the new tool; the winners’ achievements were showcased in the CEO’s monthly newsletter to employees.


LEONARDO ULIAN

A relatively new class of expert, analytics translators, can play a role in identifying roadblocks. These people bridge the data engineers and scientists from the technical realm with the people from the business realm—marketing, supply chain, manufacturing, risk personnel, and so on. Translators help ensure that the AI applications developed address business needs and that adoption goes smoothly. Early in the implementation process, they may survey end users, observe their habits, and study workflows to diagnose and fix problems.
Understanding the barriers to change can not only inform leaders about how to communicate with the workforce but also help them determine where to invest, what AI initiatives are most feasible, what training should be offered, what incentives may be necessary, and more.

Budgeting as much for integration and adoption as for technology (if not more).

In one of our surveys nearly 90% of the companies that had engaged in successful scaling practices had spent more than half of their analytics budgets on activities that drove adoption, such as workflow redesign, communication, and training. Only 23% of the remaining companies had committed similar resources.

Relationship managers may reject the notion that a machine knows what customers want.

Consider one telecom provider that was launching a new AI-driven customer-retention program in its call center. The company invested simultaneously in AI model development and in helping the center’s employees transition to the new approach. Instead of just reacting to calls canceling service, they would proactively reach out to customers at risk of defection, giving them AI-generated recommendations on new offers they’d be likely to accept. The employees got training and on-the-job coaching in the sales skills needed to close the business. Coaches and managers listened in on their calls, gave them individualized feedback, and continually updated the training materials and call scripts. Thanks to those coordinated efforts, the new program reduced customer attrition by 10%.

Balancing feasibility, time investment, and value.

Pursuing initiatives that are unduly difficult to implement or require more than a year to launch can sabotage both current and future AI projects.

Organizations needn’t focus solely on quick wins; they should develop a portfolio of initiatives with different time horizons. Automated processes that don’t need human intervention, such as AI-assisted fraud detection, can deliver a return in months, while projects that require human involvement, such as AI-supported customer service, are likely to pay off over a longer period. Prioritization should be based on a long-term (typically three-year) view and take into consideration how several initiatives with different time lines could be combined to maximize value. For example, to achieve a view of customers detailed enough to allow AI to do microsegmentation, a company might need to set up a number of sales and marketing initiatives. Some, such as targeted offers, might deliver value in a few months, while it might take 12 to 18 months for the entire suite of capabilities to achieve full impact.
An Asian Pacific retailer determined that an AI initiative to optimize floor space and inventory placement wouldn’t yield its complete value unless the company refurbished all its stores, reallocating the space for each category of goods. After much debate, the firm’s executives decided the proj­ect was important enough to future profitability to proceed—but not without splitting it in two. Part one produced an AI tool that gave store managers recommendations for a few incremental items that would sell well in their outlets. The tool provided only a small fraction of the total return anticipated, but the managers could get the new items into stores immediately, demonstrating the project’s benefits and building enthusiasm for the multiyear journey ahead.

Organizing for Scale

There’s a lot of debate about where AI and analytics capabilities should reside within organizations. Often leaders simply ask, “What organizational model works best?” and then, after hearing what succeeded at other companies, do one of three things: consolidate the majority of AI and analytics capabilities within a central “hub”; decentralize them and embed them mostly in the business units (“the spokes”); or distribute them across both, using a hybrid (“hub-and-spoke”) model. We’ve found that none of these models is always better than the others at getting AI up to scale; the right choice depends on a firm’s individual situation.

Companies with good scaling practices spent half their analytics budgets on adoption.

Consider two large financial institutions we’ve worked with. One consolidated its AI and analytics teams in a central hub, with all analytics staff reporting to the chief data and analytics officer and being deployed to business units as needed. The second decentralized nearly all its analytics talent, having teams reside in and report to the business units. Both firms developed AI on a scale at the top of their industry; the second organization grew from 30 to 200 profitable AI initiatives in just two years. And both selected their model after taking into account their organizations’ structure, capabilities, strategy, and unique characteristics.

The hub.

A small handful of responsibilities are always best handled by a hub and led by the chief analytics or chief data officer. These include data governance, AI recruiting and training strategy, and work with third-party providers of data and AI services and software. Hubs should nurture AI talent, create communities where AI experts can share best practices, and lay out processes for AI development across the organization. Our research shows that companies that have implemented AI on a large scale are three times as likely as their peers to have a hub and 2.5 times as likely to have a clear methodology for creating models, interpreting insights, and deploying new AI capabilities.
Hubs should also be responsible for systems and standards related to AI. These should be driven by the needs of a firm’s initiatives, which means they should be developed gradually, rather than set up in one fell swoop, before business cases have been determined. We’ve seen many organizations squander significant time and money—spending hundreds of millions of dollars—up front on companywide data-cleaning and data-integration projects, only to abort those efforts midway, realizing little or no benefits.
In contrast, when a European bank found that conflicting data-management strategies were hindering its development of new AI tools, it took a slower approach, making a plan to unify its data architecture and management over the next four years as it built various business cases for its AI transformation. This multiphase program, which also includes an organizational redesign and a revised talent strategy, is expected to have an annual impact of more than $900 million.

The spokes.

Another handful of responsibilities should almost always be owned by the spokes, because they’re closest to those who will be using the AI systems. Among them are tasks related to adoption, including end-user training, workflow redesign, incentive programs, performance management, and impact tracking.
To encourage customers to embrace the AI-enabled services offered with its smart, connected equipment, one manufacturer’s sales and service organization created a “SWAT team” that supported customers using the product and developed a pricing plan to boost adoption. Such work is clearly the bailiwick of a spoke and can’t be delegated to an analytics hub.

Organizing AI for Scale

AI-enabled companies divide key roles between a hub and spokes. A few tasks are always owned by the hub, and the spokes always own execution. The rest of the work falls into a gray area, and a firm’s individual characteristics determine where it should be done.

The gray area.

Much of the work in successful AI transformations falls into a gray area in terms of responsibility. Key tasks—setting the direction for AI projects, analyzing the problems they’ll solve, building the algorithms, designing the tools, testing them with end users, managing the change, and creating the supporting IT infrastructure—can be owned by either the hub or the spoke, shared by both, or shared with IT. Deciding where responsibility should lie within an organization is not an exact science, but it should be influenced by three factors:
The maturity of AI capabilities. When a company is early in its AI journey, it often makes sense for analytics executives, data scientists, data engineers, user interface designers, visualization specialists who graphically interpret analytics findings, and the like to sit within a hub and be deployed as needed to the spokes. Working together, these players can establish the company’s core AI assets and capabilities, such as common analytics tools, data processes, and delivery methodologies. But as time passes and processes become standardized, these experts can reside within the spokes just as (or more) effectively.
Business model complexity. The greater the number of business functions, lines of business, or geographies AI tools will support, the greater the need to build guilds of AI experts (of, say, data scientists or designers). Companies with complex businesses often consolidate these guilds in the hub and then assign them out as needed to business units, functions, or geographies.
The pace and level of technical innovation required. When they need to innovate rapidly, some companies put more gray-area strategy and capability building in the hub, so they can monitor industry and technology changes better and quickly deploy AI resources to head off competitive challenges.

Let’s return to the two financial institutions we discussed earlier. Both faced competitive pressures that required rapid innovation. However, their analytics maturity and business complexity differed.

The institution that placed its analytics teams within its hub had a much more complex business model and relatively low AI maturity. Its existing AI expertise was primarily in risk management. By concentrating its data scientists, engineers, and many other gray-area experts within the hub, the company ensured that all business units and functions could rapidly access essential know-how when needed.
The second financial institution had a much simpler business model that involved specializing in fewer financial services. This bank also had substantial AI experience and expertise. So it was able to decentralize its AI talent, embedding many of its gray-area analytics, strategy, and technology experts within the business-unit spokes.
As these examples suggest, some art is involved in deciding where responsibilities should live. Every organization has distinctive capabilities and competitive pressures, and the three key factors must be considered in totality, rather than individually. For example, an organization might have high business complexity and need very rapid innovation (suggesting it should shift more responsibilities to the hub) but also have very mature AI capabilities (suggesting it should move them to the spokes). Its leaders would have to weigh the relative importance of all three factors to determine where, on balance, talent would most effectively be deployed. Talent levels (an element of AI maturity) often have an outsize influence on the decision. Does the organization have enough data experts that, if it moved them permanently to the spokes, it could still fill the needs of all business units, functions, and geographies? If not, it would probably be better to house them in the hub and share them throughout the organization.

Oversight and execution.

While the distribution of AI and analytics responsibilities varies from one organization to the next, those that scale up AI have two things in common:
A governing coalition of business, IT, and analytics leaders. Fully integrating AI is a long journey. Creating a joint task force to oversee it will ensure that the three functions collaborate and share accountability, regardless of how roles and responsibilities are divided. This group, which is often convened by the chief analytics officer, can also be instrumental in building momentum for AI initiatives, especially early on.
Assignment-based execution teams. Organizations that scale up AI are twice as likely to set up interdisciplinary teams within the spokes. Such teams bring a diversity of perspectives together and solicit input from frontline staff as they build, deploy, and monitor new AI capabilities. The teams are usually assembled at the outset of each initiative and draw skills from both the hub and the spokes. Each generally includes the manager in charge of the new AI tool’s success (the “product owner”), translators, data architects, engineers and scientists, designers, visualization specialists, and business analysts. These teams address implementation issues early and extract value faster.

Some art is involved in deciding where AI responsibilities and roles should live.

For example, at the Asian Pacific retailer that was using AI to optimize store space and inventory placement, an interdisciplinary execution team helped break down walls between merchandisers (who determined how items would be displayed in stores) and buyers (who chose the range of products). Previously, each group had worked independently, with the buyers altering the AI recommendations as they saw fit. That led to a mismatch between inventory purchased and space available. By inviting both groups to collaborate on the further development of the AI tool, the team created a more effective model that provided a range of weighted options to the buyers, who could then choose the best ones with input from the merchandisers. At the end of the process, gross margins on each product category that had applied the tool increased by 4% to 7%.

Educating Everyone

To ensure the adoption of AI, companies need to educate everyone, from the top leaders down. To this end some are launching internal AI academies, which typically incorporate classroom work (online or in person), workshops, on-the-job training, and even site visits to experienced industry peers. Most academies initially hire external faculty to write the curricula and deliver training, but they also usually put in place processes to build in-house capabilities.
Every academy is different, but most offer four broad types of instruction:

Leadership.

Most academies strive to give senior executives and business-unit leaders a high-level understanding of how AI works and ways to identify and prioritize AI opportunities. They also provide discussions of the impact on workers’ roles, barriers to adoption, and talent development, and offer guidance on instilling the underlying cultural changes required.

Analytics.

Here the focus is on constantly sharpening the hard and soft skills of data scientists, engineers, architects, and other employees who are responsible for data analytics, data governance, and building the AI solutions.

Translator.

Analytics translators often come from the business staff and need fundamental technical training—for instance, in how to apply analytical approaches to business problems and develop AI use cases. Their instruction may include online tutorials, hands-on experience shadowing veteran translators, and a final “exam” in which they must successfully implement an AI initiative.

10 Ways to Derail an AI Program

Read More


End user.

Frontline workers may need only a general introduction to new AI tools, followed by on-the-job training and coaching in how to use them. Strategic decision makers, such as marketers and finance staff, may require higher-level training sessions that incorporate real business scenarios in which new tools improve decisions about, say, product launches.

Reinforcing the Change

Most AI transformations take 18 to 36 months to complete, with some taking as long as five years. To prevent them from losing momentum, leaders need to do four things:

Walk the talk.

Role modeling is essential. For starters, leaders can demonstrate their commitment to AI by attending academy training.
But they also must actively encourage new ways of working. AI requires experimentation, and often early iterations don’t work out as planned. When that happens, leaders should highlight what was learned from the pilots. That will help encourage appropriate risk taking.
The most effective role models we’ve seen are humble. They ask questions and reinforce the value of diverse perspectives. They regularly meet with staff to discuss the data, asking questions such as “How often are we right?” and “What data do we have to support today’s decision?”
The CEO of one specialty retailer we know is a good example. At every meeting she goes to, she invites attendees to share their experience and opinions—and offers hers last. She also makes time to meet with business and analytics employees every few weeks to see what they’ve done—whether it’s launching a new pilot or scaling up an existing one.

Make businesses accountable.

It’s not uncommon to see analytics staff made the owners of AI products. However, because analytics are simply a means of solving business problems, it’s the business units that must lead projects and be responsible for their success. Ownership ought to be assigned to someone from the relevant business, who should map out roles and guide a proj­ect from start to finish. Sometimes organizations assign different owners at different points in the development life cycle (for instance, for proof of value, deployment, and scaling). That’s a mistake too, because it can result in loose ends or missed opportunities.
A scorecard that captures proj­ect performance metrics for all stakeholders is an excellent way to align the goals of analytics and business teams. One airline company, for instance, used a shared scorecard to measure rate of adoption, speed to full capability, and business outcomes for an AI solution that optimized pricing and booking.

Track and facilitate adoption.

Comparing the results of decisions made with and without AI can encourage employees to use it. For example, at one commodity company, traders learned that their non-AI-supported forecasts were typically right only half the time—no better than guessing. That discovery made them more open to AI tools for improved forecasting.

The business units must lead AI projects and be responsible for their success.

Teams that monitor implementation can correct course as needed. At one North American retailer, an AI proj­ect owner saw store managers struggling to incorporate a pilot’s output into their tracking of store performance results. The AI’s user interface was difficult to navigate, and the AI insights generated weren’t integrated into the dashboards the managers relied on every day to make decisions. To fix the issue, the AI team simplified the interface and reconfigured the output so that the new data stream appeared in the dashboard.

Provide incentives for change.

Acknowledgment inspires employees for the long haul. The CEO of the specialty retailer starts meetings by shining a spotlight on an employee (such as a product manager, a data scientist, or a frontline worker) who has helped make the company’s AI program a success. At the large retail conglomerate, the CEO created new roles for top performers who participated in the AI transformation. For instance, he promoted the category manager who helped test the optimization solution during its pilot to lead its rollout across stores—visibly demonstrating the career impact that embracing AI could have.
Finally, firms have to check that employees’ incentives are truly aligned with AI use. This was not the case at a brick-and-mortar retailer that had developed an AI model to optimize discount pricing so that it could clear out old stock. The model revealed that sometimes it was more profitable to dispose of old stock than to sell it at a discount, but the store personnel had incentives to sell everything, even at steep discounts. Because the AI recommendations contradicted their standard, rewarded practice, employees became suspicious of the tool and ignored it. Since their sales incentives were also closely tied to contracts and couldn’t easily be changed, the organization ultimately updated the AI model to recognize the trade-off between profits and the incentives, which helped drive user adoption and lifted the bottom line.

CONCLUSION

The actions that promote scale in AI create a virtuous circle. The move from functional to interdisciplinary teams initially brings together the diverse skills and perspectives and the user input needed to build effective tools. In time, workers across the organization absorb new collaborative practices. As they work more closely with colleagues in other functions and geographies, employees begin to think bigger—they move from trying to solve discrete problems to completely reimagining business and operating models. The speed of innovation picks up as the rest of the organization begins to adopt the test-and-learn approaches that successfully propelled the pilots.
As AI tools spread throughout the organization, those closest to the action become increasingly able to make decisions once made by those above them, flattening organizational hierarchies. That encourages further collaboration and even bigger thinking.
The ways AI can be used to augment decision making keep expanding. New applications will create fundamental and sometimes difficult changes in workflows, roles, and culture, which leaders will need to shepherd their organizations through carefully. Companies that excel at implementing AI throughout the organization will find themselves at a great advantage in a world where humans and machines working together outperform either humans or machines working on their own.

A version of this article appeared in the July–August 2019 issue (pp.62–73) of Harvard Business Review.

Tim Fountaine is a partner in McKinsey’s Sydney office and leads QuantumBlack, an advanced analytics firm owned by McKinsey, in Australia.


Brian McCarthy is a partner in McKinsey’s Atlanta office and coleads the knowledge development agenda for McKinsey Analytics.

Tamim Saleh is a senior partner in McKinsey’s London office and heads McKinsey Analytics in Europe.

This article is about TECHNOLOGY

 Follow This Topic

Related Articles

UP NEXT IN ANALYTICS

You Don’t Have to Be a Data Scientist to Fill This Must-Have Analytics Role

Jordan Levine; Nicolaus Henke; Paul McInerney


UP NEXT IN LEADERSHIP

Nimble Leadership

Deborah Ancona; Elaine Backman; Kate Isaacs


UP NEXT IN STRESS

When Passion Leads to Burnout

Jennifer Moss


Related Products

LEADERSHIP

Harvard Business Review Magazine Subscription

HBR All-Access Subscription

View Details