AI, or Artificial Intelligence, is revolutionizing the way people interact with businesses. Customers are leveraging this technology to learn, shop, and interact with businesses in their own way. However, building it is not an easy task.
Creating AI is certainly no rocket science, but it does require careful consideration of a lot of factors. Additionally, it requires businesses to mitigate the challenges and risks the development poses.
Let us briefly look into some of the significant challenges, risks, and real lessons associated with AI development for consumer applications to help you make informed decisions when reaching out to AI development services in USA.
Building AI for consumer applications: Challenges
The road to building AI for consumer applications is full of hurdles and challenges. The most significant ones are:
1. Data challenges
Data is one of the biggest concerns when developing AI for consumer applications. Businesses often lack complete, high-quality, and consistent data required to customize the AI models for specific applications. As a result, the output delivered by AI models appears to be discriminatory and unreliable a lot of times.
Lastly, integrating the advanced or latest AI systems with legacy or conventional IT infrastructures can be costly, complex, and might require some technical expertise.
2. Organizational challenges
Several businesses or organizations struggle with finding and hiring the relevant AI expertise to build, plan, and execute their AI projects. Moreover, the AI introduction can leave employees in fear of job displacement. So, a solid plan is essential to adapt AI in the workforce.
3. Technical and operational challenges
Consumer-facing AI is capable of handling a variety of requests. However, things can go wrong when the AI systems start receiving inquiries that are nuanced or complex. This is where they begin delivering irrelevant or inaccurate responses.
Another challenge that many face is scaling the resource. It is because this requires a strong infrastructure and a huge investment. Moreover, AI models require regular upgrades, learning, and maintenance with changes in market conditions and consumer behavior. This is yet another resource-intensive activity.
Building AI for consumers: Risks
Even though AI is extremely beneficial, it does come with some risks. The major ones are as follows:
1. Bias
Humans tend to make biased decisions many times, and the AI they develop can reflect the biases from training data. This ultimately leads to discriminatory or unfair outputs.
2. Data security and privacy
AI models and systems like chatbots and virtual assistants require access to huge data stored across relevant databases. This is essential to deliver relevant output and answers.
However, web crawlers often source data that may include PII (Personally Identifiable Information) from different sites and databases without consent. This increases the risk of leakage and non-compliance with privacy regulations. As a result, security and privacy become a challenge.
3. Cybersecurity threats
Bad actors may manipulate the AI tools and models to generate fake identities, clone voices, and generate phishing emails, ultimately exploiting the technology to launch cyberattacks. This is intentionally done to hack, scam, or steal identities or attack someone’s security and privacy.
4. Lack of accountability
One of the biggest risks of developing AI for consumer experience is the system’s lack of accountability. The questions like – who is responsible when an AI system goes wrong? Who is responsible for the aftermath of the tool’s damaging decisions? appears to be worrisome.
5. Misinformation and manipulation
Bad actors, just like in the case of cyberattacks, exploit the AI models to spread disinformation and misinformation. Ultimately, manipulates users’ actions and decisions.
Building AI for consumer applications: Real lessons
Some real lessons that can help organizations build effective AI for consumer applications are:
- Begin with a narrow problem instead of applying AI to everything.
- Invest in collecting clean, representative, and diversified data.
- Build AI models ethically and responsibly to avoid biases throughout the development period.
- Plan for AI failures from the very beginning.
- Ensure to monitor and upgrade the technology as and when required.
Wrapping up
Building AI for consumer applications is not simply offering a technology for use. Instead, it is about delivering transparent and trustworthy experiences. To understand relevant AI models for your consumer applications and enhanced experiences, consult experts at our AI development company.
Frequently Asked Questions
One of the major challenges when developing AI for consumer applications is combining unbiased and quality training data with privacy compliance to deliver good user experiences.
Yes, they do. Regulations like EU AI Act and GDPR affect how training data is stored, collected, and used. Adhering to compliance ensures safety and enhances consumer trust.
