East Asian governments surge in AI readiness – see global rankings in full

By on 26/01/2022 | Updated on 28/01/2022

Governments in countries across East Asia have leapt ahead in their readiness to adopt artificial intelligence in public services, according to an annual index report released by Oxford Insights.

The 2021 Government AI Readiness Index ranked 160 countries in total, scoring each out of 100 based on indicators of AI readiness, including government vision, technology sector size and available data and infrastructure.

Governments in East Asian countries for the first time made up around 25% of the top-scoring 20 countries overall, with Singapore the highest in second place. The US topped the index meanwhile, owing to the size and maturity of its technology sector.

The report pointed to East Asia’s “global success in AI research and its advanced computing power”. It added that, with the exception of Taiwan, all the highest-ranking countries in the region scored significantly above the global average on skills, education and infrastructure.

Of the highest-scoring governments in East Asia behind Singapore, South Korea came 10th, followed by Japan (12th), China (15th), and Taiwan (18th).

According to the report, pressure on East Asian countries to respond to the coronavirus pandemic prompted a surge in digital healthcare innovation, especially Indonesia, Malaysia and the Philippines.

“While it is hard to trace the effects of such developments on specific indicator scores in our index, it is feasible that they will trickle through to higher rankings in the private sector innovation capacity and government digital capacity dimensions in the future,” it said.

Regional disparities in AI developments across the world

Though East Asian governments collectively appeared to advance in preparedness for AI-based services, the degree of readiness between them varied significantly. Possible explanations were linked to factors such as the lack of available data in individual countries, the relative maturity of their technology sectors, as well as under-developed national AI strategies. 

“Despite the advances of many of the top scoring countries in the region, East Asia is one of the most unequal regions in terms of AI readiness. Several countries score below the global overall average of 47.42 out of 100,” it said.

Developed countries were more likely to have developed national AI strategies, but only around 40% of the countries ranked were found either to have released a strategy or be involved in creating one. Of the top five countries overall, three were based in Western Europe: the United Kingdom, Finland, and the Netherlands. Nordic countries were noted to have scored highly despite the relative size of their economies. An example given was Finland, which has the world’s 41st largest economy, yet managed to rank 4th in the index.

Read more: Nordics lead EU’s innovation league table

“These results demonstrate how a country’s size does not determine its ranking in the index,” the report said. Countries that had made the least progress were based in Sub-Saharan Africa and Central and South Asia. Together, these regions held a combined average score of 36.27.

“With governments in some developing nations having limited capacity to build and deliver AI in their public services, few are at the stage in AI readiness to publish their vision for AI,” it said.

Read more: AI regulation is coming. Here’s how governments can get it right

AI Readiness Index in full

Global
Position
CountryOverall
Score
GovernmentTechnology
Sector
Data and
Infrastructure
1United States of America88.1688.46 83.3192.71
2Singapore82.4694.8866.6985.80
3United Kingdom81.2585.6967.2690.81
4Finland79.2388.4563.8585.40
5Netherlands78.5180.4266.1788.92
6Sweden78.1680.7667.3786.36
7Canada77.7384.3663.7585.08
8Germany77.2678.0467.6886.07
9Denmark76.9683.5063.2484.14
10Republic of Korea76.5585.2758.4985.89
11France76.4182.1060.6186.53
12Japan76.1881.9059.3187.32
13Norway76.1484.2459.2584.91
14Australia75.4183.7957.0785.37
15China74.4283.7961.3378.15
16Luxembourg73.3782.6750.6686.80
17Ireland72.8074.7061.1182.59
18Taiwan71.9877.5959.4278.92
19United Arab Emirates71.6079.4153.3382.05
20Israel70.0164.6465.8779.52
21Estonia69.1877.6549.4680.45
22Switzerland68.5652.3067.6085.79
23New Zealand68.0866.0753.1685.02
24Austria68.0763.0958.5482.59
25Spain67.6871.8749.8481.32
26Qatar67.1879.5643.0278.96
27Italy67.0772.7548.1980.28
28Belgium66.1659.0758.5480.89
29Czech Republic65.9568.9850.5678.32
30Lithuania65.1972.7944.2778.50
31Slovenia65.0570.1045.4879.58
32Malta64.8583.6241.5269.41
33Portugal64.3174.6450.4267.87
34Saudi Arabia63.4267.2345.1477.89
35Poland62.5067.2742.8277.42
36Malaysia62.4668.3752.6766.34
37Latvia62.2768.7441.4576.60
38Russian Federation61.9367.4446.4671.90
39Slovakia61.6267.5941.3975.89
40Brazil60.6465.0442.7074.16
41Chile60.4269.9942.1469.13
42Bulgaria60.0767.0339.1973.97
43Hungary59.7268.0941.6569.40
44Cyprus59.7169.4637.9971.70
45Colombia58.9173.0334.6669.04
46Iceland58.5349.9750.7374.87
47Indonesia58.1473.0540.9660.40
48Uruguay57.9369.4831.6372.67
49Oman57.2663.0134.9973.77
50Greece56.2252.5443.3472.79
51India56.1173.2648.0447.02
52Serbia55.9868.1536.3563.42
53Turkey55.4971.4139.0555.99
54Argentina54.3664.8633.6264.59
55Bahrain53.5451.4631.5477.62
56Romania53.2252.0937.5070.09
57Brunei Darussalam52.9341.0543.5074.23
58Mauritius52.7168.5233.8255.80
59Thailand52.6345.4541.2271.21
60Mexico52.6254.7040.2262.94
61Croatia52.3048.7036.4871.71
62Viet Nam51.8270.8132.7851.87
63Kuwait50.9746.5334.3771.99
64Ukraine50.5852.3638.1961.19
65Egypt49.7562.7235.1751.37
66Kazakhstan48.4348.8032.3864.10
67Azerbaijan48.2650.6033.8660.34
68South Africa48.2440.9239.1464.66
69Fiji48.1744.5737.9961.94
70Seychelles47.4841.2833.8267.33
71Philippines47.2041.9737.2062.44
72Iran46.2336.4235.2067.06
73Belarus46.2039.8934.3064.40
74Costa Rica46.1940.6434.5763.36
75Montenegro46.1040.7134.6162.96
76Armenia45.9343.1031.1463.53
77Tunisia45.7150.2236.3150.61
78Kenya45.5457.1528.7550.72
79Georgia45.4144.2029.2262.83
80Jordan44.3838.2638.3156.56
81North Macedonia43.7340.7931.0859.31
82Panama42.9838.2529.9760.73
83Albania42.9041.4728.5458.69
84Morocco42.3842.1331.7453.27
85Barbados42.2035.3931.06 60.14
86Republic of Moldova41.7140.0329.8055.29
87Jamaica41.5036.7631.6856.06
88Sri Lanka41.1234.8432.5455.97
89Dominican Republic40.8941.2524.5756.84
90Trinidad and Tobago40.7834.6328.7858.92
91Peru40.5638.2429.5253.90
92Ghana40.1941.7824.7554.05
93Uzbekistan40.1337.9531.3251.13
94Lebanon39.6738.2333.4747.30
95Ecuador39.1935.4226.1256.03
96Bosnia and Herzegovina38.6731.0527.1057.87
97Suriname38.5824.9726.6264.16
98Tajikistan38.4935.8526.2953.31
99Algeria37.9232.9629.5751.24
100Kyrgyzstan37.6135.1623.6254.04
101Maldives37.4729.5916.0866.73
102Paraguay37.3535.3222.4554.27
103Mongolia37.2033.4527.2450.90
104Iraq36.9325.5131.2754.02
105Côte D’Ivoire36.7138.7322.2949.10
106Saint Lucia36.6128.2227.5354.08
107Gabon36.5132.4026.5050.62
108Senegal36.3439.4728.3141.23
109Botswana36.3336.2728.1844.53
110Bangladesh36.1040.2125.1042.99
111Cabo Verde36.0736.4623.3548.40
112Rwanda35.1644.4427.0334.01
113Nigeria35.1535.5326.3243.61
114Lao People’s Democratic Republic34.9329.2224.6950.88
115Honduras34.9130.6525.5848.51
116Papua New Guinea34.7535.5027.8340.93
117Pakistan34.0339.5835.0027.50
118Bhutan34.0236.5226.1539.40
119Cambodia33.3532.6622.5344.87
120Namibia33.1430.4922.9745.95
121United Republic of Tanzania32.6939.9022.9935.18
122Togo32.6832.6720.7144.65
123El Salvador32.4126.1623.6947.39
124Belize32.2827.9722.5746.29
125Guatemala32.2524.3023.2849.18
126Burkina Faso32.2435.7121.3139.71
127Nepal31.9233.2824.4738.03
128Cameroon31.8834.5823.7737.30
129Uganda31.7635.9722.2037.11
130Bolivia31.6223.5021.4949.87
131Nicaragua31.5730.4122.4541.86
132Myanmar31.5724.8027.5442.36
133Zambia31.2135.0821.9336.63
134Venezuela30.5418.7722.4450.40
135Gambia30.4528.7320.2942.34
136Congo30.4230.4920.9739.80
137Cuba30.3820.7529.6740.71
138Mali30.1431.8122.0036.61
139Lesotho29.8423.2620.7845.49
140Eswatini29.7628.5921.5339.16
141Mauritania29.6225.7421.5441.59
142Madagascar29.5331.1620.4436.99
143Sierra Leone29.3932.5919.6535.93
144Zimbabwe29.1526.4522.2838.71
145Benin28.7334.7621.6129.82
146Liberia28.1431.7219.7232.97
147Ethiopia27.9534.6520.5728.62
148Niger27.8133.3120.0930.03
149Guinea27.5527.7822.2232.65
150Mozambique26.2332.2817.6028.83
151Sudan25.9124.8321.6231.29
152Haiti25.1424.2414.6736.49
153Malawi24.8528.5718.4327.55
154Afghanistan24.3825.679.2338.23
155Chad24.0027.5814.4130.02
156Burundi23.7228.4019.0623.70
157Democratic Republic of the Congo23.3227.4616.7225.79
158Angola22.8727.3013.8627.44
159Central African Republic20.7315.0119.2127.98
160Yemen17.9315.8517.9120.03



The three pillars of the report’s methodology: Government, Technology Sector, Data and Infrastructure, consist of 42 indicators across 10 dimensions.

The dimensions for Government include: Vision (indicators of which are: a National AI strategy), Governance & Ethics (indicators: data protection and privacy legislation, cybersecurity, national ethics framework, legal framework’s adaptability to digital business models), Digital Capacity (indicators: Government promotion of investment in emerging technologies, ICT use and government efficiency, Online services, Trust in Government websites and apps), Adaptability (indicators: Effectiveness of government, Government’s responsiveness to change and E-procurement capacity.
Dimensions for Technology Sector include: Size (indicators: Number of AI unicorns CB Insights Number of non-AI technology unicorns, Market value of public technology companies, Value of trade in ICT services [per capita], Value of trade in ICT goods [per capita], Computer software spending), Innovation Capacity (indicators: Entrepreneurial culture, Business administrative requirements, R&D spending, Company investment in emerging technologies) and Human Capital (Graduates in STEM, Quality of engineering and technology higher education, Digital skills, Github commits, Knowledge-intensive employment, Research papers published in AI).

Dimensions for Data and Infrastructure include Infrastructure (indicators: Telecommunications infrastructure, 5G infrastructure, Number of supercomputers, Internet bandwidth, Adoption of emerging technologies) Data Availability (indicators: Open government data, Open data policies, Statistical capacity, Mobile-cellular telephone subscriptions, Households with Internet access at home) and Data Representativeness (indicators: Gender gap in Internet access, Gender gap in mobile access, Cost of internet-enabled device relative to GDP per capita, Socioeconomic gap in Internet usage).

About Jack Aldane

Jack is a British journalist, cartoonist and podcaster. He graduated from Heythrop College London in 2009 with a BA in philosophy, before living and working in China for three years as a freelance reporter. After training in financial journalism at City University from 2013 to 2014, Jack worked at Bloomberg and Thomson Reuters before moving into editing magazines on global trade and development finance. Shortly after editing opinion writing for UnHerd, he joined the independent think tank ResPublica, where he led a media campaign to change the health and safety requirements around asbestos in UK public buildings. As host and producer of The Booking Club podcast – a conversation series featuring prominent authors and commentators at their favourite restaurants – Jack continues to engage today’s most distinguished thinkers on the biggest problems pertaining to ideology and power in the 21st century. He joined Global Government Forum as its Senior Staff Writer and Community Co-ordinator in 2021.

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