4 Major Challenges Governments Face While Adopting AI Solutions
Govciooutlook

4 Major Challenges Governments Face While Adopting AI Solutions

By Gov CIO Outlook | Monday, August 24, 2020

Artificial Intelligence has almost entered every sector. How is it going to impact the government sector? What challenges will the government have to face?

FREMONT, CA: From transportation solutions to applications like video-streaming, Artificial Intelligence (AI) has impacted almost every aspect of our lives. It includes the government sector as well, which is in constant search of innovative technologies to make the lifestyle of the public more convenient. Taking the example of Emma chatbots, US Citizenship and Immigration Services receive loads of service requests on a daily basis. The chatbot named Emma is specifically deployed to address the immigration questions. The chatbots possess the ability to operate in both English and Spanish languages and handle more than a billion immigration queries every month.

The adoption of AI has been steadier in the government sector as compared to the private sector. Though the magnitude of AI's impact is significant on public entities, it is vital to understand the roadblocks that are in the way of the government to adopt systematic AI.

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

1. Data and AI skills

Data management skills and AI are in short supply. The learning curve for data management can be somehow managed. However, achieving the skills that are needed to develop AI solutions is very difficult. Companies place a premium on alluring high-caliber AI talent, increasing compensation and making it hard for companies with smaller hiring budgets, like the government, to attract the best candidates. Public agencies lack core AI skills, which hampers their capability to deploy and operate AI solutions.

Also, government employees in non-technical jobs, like department directors, procurement officials, and policymakers, usually don’t have enough knowledge about data and AI. This comprises technical knowledge and the most necessary knowledge of the legal and ethical implications of utilizing colossal amounts of data where privacy is the primary concern. This makes it harder for the government to invest in the technology or be aware of the present laws that have a direct impact on AI projects, like data and privacy legislation.

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

Government agencies that are devoid of AI in-house knowledge most likely face an added complexity, i.e., lack of communication. Silos between functions make it difficult for the resources of AI and their colleagues to have constant touchpoints and to take the full privilege of each other’s understanding and knowledge.

2. Legacy Culture

All enterprises face skyrocketing challenges before adopting innovative technologies. However, public entities seem to be less flexible than private-sector counterparts in opting for new technologies because of their established practices and processes. There is a strong culture of experimentation existing in parts of the private sector that encourages the employees to innovate. In return, the employees are rewarded for their positive performance. However, in the government sector, there is not much encouragement for the employees to innovate and take risks.

Instead of financial compensation, many people working in the government sector derive their work satisfaction by thinking of having an opportunity to impact society in a positive way. However, it can be very complex to adopt an innovative technology like AI if flexibility is not inherent to the culture of the organization.

3. Efficient use of data

We are currently living in a digital era. People use a lot of platforms on a daily basis, such as Facebook, Twitter, etc., due to which a huge amount of data is generated. In 2017, IBM estimated that 90 percent of the world’s data had been generated in the past two years. The main issue is that the organization, both private and public, failed to handle this huge volume and variety of data. Most companies have a very rudimentary knowledge of their data assets. Answering even basic questions such as the number of databases existing in the organizations, type of information stored in different databases, and the amount of data collected can be a huge challenge for them. Keeping in mind the fact that data is the fuel that governs modern AI solutions, this becomes a significant issue.Top Artificial Intelligence Solution Companies

A parallel roadblock is that most companies don’t possess data governance officials in place, like established data owners, an organization’s data champion, such as Chief Data Officer. In addition, organizations also lack instruments and tools for the personnel to securely and efficiently access and enjoy the benefit of enterprise data. Besides, they also don’t have the practices to handle and ensure data privacy and integrity. Companies that do not possess the abilities to comprehend and handle their data cannot enjoy the privileges offered by AI.

4. The ambiance of AI

The AI landscape is very intricate and is constantly evolving. Even in more established technology sectors, there are a handful of well-known players or buyers who know where to go. For instance, the cloud landscape is dominated by Amazon, Alibaba, Microsoft, and Google, which together account for around 84 percent of the global public cloud market. On the contrary, the AI market, which holds a massive presence from tech heavyweights, is further fragmented and has many small players appearing continuously. The changing speed of the AI market and the number of players are big enough to potentially hinder a buyer who has newly entered into the AI market. Many buyers are sometimes unaware of the complete landscape in the first place.

This heterogeneity of players in this system stems from various AI start-ups that have appeared across different geographies. This represents the fourth challenge for the government: there is a large amount of AI know-how inside newer and small companies that have little experience of working with the government and have difficulty in scaling up for large projects. The government should find new ways to include these new players, both to utilize their considerable expertise and to foster AI industry hubs’ growth that can contribute largely to the local economy.

The government faces huge 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, and this is the most straightforward part to address. Culture and processes in the organization also require adjustment before AI can be fully deployed.

Check out: Top Artificial Intelligence Companies

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