{"id":27772,"date":"2019-09-12T10:43:00","date_gmt":"2019-09-12T09:43:00","guid":{"rendered":"https:\/\/digileaders.com\/?p=27772"},"modified":"2019-12-23T16:46:27","modified_gmt":"2019-12-23T16:46:27","slug":"public-sector-implementing-ai-properly","status":"publish","type":"post","link":"https:\/\/digileaders.com\/public-sector-implementing-ai-properly\/","title":{"rendered":"Is the public sector implementing Artificial Intelligence properly?"},"content":{"rendered":"

Artificial Intelligence provides huge opportunities for government to deliver more for less through increased productivity and to improve the lives of citizens.\u00a0<\/span><\/p>\n

The government understands its importance \u2013 AI and Data was named as one of the four \u2018Grand Challenges\u2019 in the <\/span>Industrial Strategy White Paper.<\/span><\/a><\/p>\n

PwC estimates that AI could contribute $15.7tr to the global economy by 2030. The UK is in the top three countries globally in the development of AI technologies. And AI could increase the UK\u2019s productivity by 14.3% and grow GDP up to 10.3% by 2030.<\/span><\/p>\n

So we know AI is the next big thing, but what\u2019s the most effective way of utilising it?<\/span><\/p>\n

Commoditising AI<\/span><\/h4>\n

Cloud technology has made accessing AI easier. In the past, you would need expensive infrastructure and specialist technology knowledge to use AI. Now, with the likes of Amazon Web Services and\u00a0Microsoft Azure, you have the capability to spin up services instantly and assemble sophisticated human-like functionality. The barrier to entry is lower; people can get started without the need to understand the complex\u00a0fundamentals.\u00a0<\/span><\/p>\n

This is already allowing a much larger set of hobbyists and organisations to experiment with AI than has previously been possible<\/span>,<\/span> but it<\/span>\u2019<\/span>s not without challenges. Bolting on AI to existing situations without thinking about the quality of your data or\u00a0overarching process can only provide limited benefits at best and unintended consequences at worst \u2014 and it certainly isn\u2019t transformational.\u00a0<\/span><\/p>\n

One place we\u2019ve encountered AI is within Robotic Process Automation (RPA) tools. So far RPA has had a mixed reputation with <\/span>30-50<\/span>% of projects deemed to fail<\/span><\/a> according to some analysis. A big part of that is due to its brittleness: for example a software update rolls out an<\/span><\/a>d a new dialogue pops up stopping the bot dead in its tracks until an engineer can modify it. This is why the likes of Intelligent Automation are keen to infuse RPA with AI. Most RPA vendors are not currently using advanced AI but some are including things like image recognition and text analysis in their products. Perhaps this is just the beginning but RPA has a long way to grow if it is to become more than a tactical, sticking plaster solution. Currently, it doesn\u2019t lead to true digital transformation. <\/span>It doesn’t streamline or cut the fat, and it can actually perpetuate legacy systems instead of digitising them<\/span><\/a>.\u00a0<\/span><\/p>\n

To utilise AI to its fullest and be transformational, you need good quality data from your processes, which requires the whole end-to-end process to be digitised to capture that data in the first place. The rich data you collect from this digitisation <\/span>will enable your AI to be more effective. If some of your processes are still manual, your data collection will be poor or incomplete and the AI will not get the whole picture. Poor quality and incomplete data will <\/span>hinder your<\/span> AI <\/span>from <\/span>mak<\/span>ing<\/span> better decision<\/span>s<\/span>.<\/span><\/p>\n

AI in government<\/span><\/h4>\n

A <\/span>recent analysis by Deloitte<\/span><\/a> identifies four distinct areas that AI can be applied within government.\u00a0<\/span><\/p>\n