AI Solutions: Major Challenges for Governments
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AI Solutions: Major Challenges for Governments

Government CIO Outlook | Monday, July 25, 2022

Artificial Intelligence has almost enrolled every sector. How is it going to influence the government sector? What disputes will the government have to face?

FREMONT, CA: AI has impacted almost every aspect of our lives. It also includes the government sector, which is constantly searching for innovative technologies to make the lifestyle of the public more convenient.

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The adoption of AI has been stabler in the government than in the private sector. Though AI's influence is significant on public entities, it is essential to understand the roadblocks in the government adopting systematic AI.

There are five major key obstacles to AI adoption in government.

1. Data and AI skills

Data management skills and AI are in shortage. The education process for data management can be somehow managed. Although, achieving the skills required to develop AI solutions is very difficult. Companies place a premium on attractive, high-caliber AI talent, increasing compensation and making it tough for companies with lower hiring budgets, like the government, to draw the best candidates. In addition, public agencies' lack of core AI skills hampers their capacity to deploy and operate AI solutions.

Additionally, government employees in non-technical jobs, like department directors, procurement officials, and policymakers, usually don't know enough about data and AI. This comprises technical knowledge and the essential knowledge of the legal and ethical implications of utilizing the colossal quantity of data where privacy is the primary concern. This makes it tougher for the government to invest in the technology or be aware of the present laws that directly impact AI projects, like data and privacy legislation.

Investing in AI projects without complete knowledge of the applicable local laws threatens the constituents' rights, such as privacy and the long-term ability of the government to deploy AI with full public support. In addition, a lack of technical AI skills can lead to a massive failure of AI procurement.

Government agencies without AI in-house knowledge likely face an added complexity, i.e., lack of communication. Silos between functions make it hard for the resources of AI and their associates to have constant touchpoints and to take the complete privilege of each other's understanding & knowledge.

2. Legacy Culture

All enterprises confront skyrocketing challenges before adopting innovative technologies. Still, public entities seem less flexible than private-sector counterparts in opting for new technologies because of their established practices and processes. A strong culture of experimentation exists in parts of the private sector that encourages employees to innovate. In reply, the employees are rewarded for their positive performance. Yet, employees are not encouraged to innovate and take risks in the government sector.

Rather than financial compensation, many people working in the government sector derive their work gratification by thinking of having a chance to impact society positively. However, adopting innovative technology like AI can be very complex if flexibility is not inherent to the organization's culture.

3. Efficient use of data

Nowadays, We are living in a net era. People use many platforms daily, such as Facebook, Twitter, etc., due to which a vast amount of data is generated. In 2017, IBM estimated that 90% of the world's data had been developed in the past two years. The main issue is that the private and public organizations failed to manage this huge volume and variety of data.

Most companies have a very basic knowledge of their data assets. Answering basic questions such as the number of databases in the organizations, the type of information stored in distinct databases, and the amount of data collected can be a massive challenge for them. Keeping in mind that data is the fuel governing modern AI solutions, this becomes a significant issue.

A parallel barricade is that most companies don't possess data governance officials like established data owners, an organization's data champion, and Chief Data Officer. Also, organizations lack instruments and tools for the staff to securely and efficiently access and enjoy the benefit of enterprise data. Moreover, they also don't have the practices to handle and ensure data privacy and integrity. Companies that do not hold the ability to comprehend and manage their data cannot enjoy the privileges provided by AI.

4. The ambiance of AI

The AI landscape is very complex and is constantly evolving. Some well-known players or buyers know where to go, even in more established technology sectors. For example, the cloud landscape is dominated by Amazon, Alibaba, Microsoft, and Google, which account for around 84% of the global public cloud market. In contrast, the AI market, which contains a massive presence from tech heavyweights, is further fragmented and has several small players appearing continuously.

The varying speed of the AI market and the number of players are quite large to potentially impede a buyer who has newly entered the AI market. Moreover, many buyers are intermittently unaware of the total landscape in the first place.

This heterogeneity of players in this system stems from different AI start-ups that have emerged across different geographies. This portrays the fourth challenge for the government. There is a huge amount of AI know-how inside newer and small companies with little experience working with the government and have difficulty scaling up for large projects. Therefore, the government should find ways to include these new players to utilize their considerable expertise and foster AI industry hubs' growth that can contribute mainly to the local economy.

The government faces enormous challenges regarding the adoption of widespread AI. In contrast to the famous belief that technology is the major roadblock, technical challenges are just a part of the task, which is the most straightforward to address. Culture and processes in the organization also require adjustment before AI can be fully deployed.

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