Imagine a Singaporean biotechnology startup leveraging artificial intelligence (AI) in diagnostic solutions to determine the most effective cancer treatments for more rapid recovery. Meanwhile, a small agritech company might deploy AI-powered drones to enhance irrigation, pest management and crop health monitoring in rural India.
AI innovations have rapidly become a non-negotiable driver of success in technology startups, particularly across Asia. Yet, despite these innovations’ ability to streamline functions, boost invention and personalise customer experiences, technology startups face several challenges that can hinder achievement.
Challenges faced by tech startups in the AI age
Despite AI solutions having the power to transform technology startups, integrating them isn’t always straightforward. These are some of the greatest integration difficulties in startup culture.
Talent acquisition and up-skilling
The skills required for AI-influenced jobs change 25 per cent faster than jobs less impacted by AI, meaning workers must continuously up-skill to stay relevant. Compensation for positions relying on AI expertise also tends to be 25 per cent higher, incentivising professional development and highlighting the importance of AI to companies.
As it stands, talent availability is lacking. Saikat Banerjee — a leader at Bain & Company’s AI, Solutions, and Insights firm — says there will be 1.5 to 2 times more AI-related job openings than there are professionals to fill them by 2027.
According to an MIT Sloan study, 85 per cent of entrepreneurs agree they critically need an AI strategy, whether to seek new opportunities, encourage groundbreaking product development or gain deeper insight into the customer journey.
Data collection and governance
Because startup companies are in their infancy, they do not always have relevant data points to train AI models. Data quality and diversity are also crucial. Otherwise, inputs may result in inaccuracies, biases and inadequate predictions, with serious consequences in health care or financial settings.
Data privacy regulations are on the rise throughout Asia. For instance, Korea’s Personal Information Privacy Commission (PIPC) has issued rules allowing consumers to ask about AI decision-making, such as how it makes certain hiring decisions. Hong Kong also encourages responsible AI use in businesses by promoting fairness and transparency.
Also Read: How Hasan Venture Capital uses AI to build an ethically grounded investment future
Infrastructure and computing power
Technology startups must contend with the high costs of cloud computing and specialised equipment for training AI models. As these solutions grow more sophisticated, the need for expansion and additional resources may further strain a startup’s budget.
Areas with inconsistent internet connectivity could also affect AI performance. According to one report, internet use is 22.5 per cent lower in rural Southeast Asia than in urban areas, except Singapore and Brunei. Climate change impacts in Indonesia, the Philippines and Vietnam, especially, may also hinder broadband infrastructural investments.
Biases and fairness
Startups must address biases within AI systems. This includes unfair decision-making based on gender, age or race. Failing to mitigate biases could hurt a startup’s reputation and lead to noncompliance.
Biases may occur during data collection due to insufficient information capture. It might also happen when data gets fed to the models during training. Some regions have introduced new rules requiring companies to recheck information for fairness before continuing conditioning models.
Funding and investment
Because AI is still developing, technology startups must secure funding to demonstrate the tools’ potential to stakeholders. The most effective approach is establishing clear AI initiatives with each project’s likely return on investment. Asian markets can seek government grants and venture capital for AI specialisations.
China is a prime example of this, having previously invested 23 per cent of US$912 billion in government venture capital funds to 1.4 million early-stage AI startups. The Chinese government issues much of this venture capital to firms with lower software development costs and those with signs of higher growth from the investment.
Integration and implementation
AI implementation may be difficult in existing systems and workflows, especially if teams are resistant or lack proper training. These factors can also put a startup at risk of scams.
For example, AI models need access to sensitive data. If personal information gets into the wrong hands, businesses and their customers may be susceptible to scammers. Bad players may use AI tools to create convincing deepfakes of people or communications to collect money. Others may use fraudulent chatbots impersonating customer service representatives to steal credit card information.
Also Read: Navigating the trust labyrinth: My perspective on ethical AI marketing
According to a Deloitte report, only 33 per cent of employees have received generative AI training, and 35 per cent say they weren’t satisfied with their learning. A company must ensure a clear strategy for AI integrations and prepare its employees for the change.
Tips for startups to overcome these challenges
Technology startups must keep up with evolving AI advancements even as they find their footing. Companies should concentrate their investments in talent acquisition, data management and computing infrastructure for maximum returns.
Integrating AI into a company’s business plan should focus on concrete outcomes and revenue. Seeking investors with AI knowledge and pursuing federal grants and funding programs — including crowdfunding — is another way to garner capital, test the market and reduce risk.
A successful technology startup is only as good as those working there. Therefore, finding the best talent with AI expertise and providing comprehensive training and professional development is essential.
Additional suggestions for overcoming the challenges of AI in a technology startup include:
- Explore public data platforms and exchanges.
- Enhance training data by modifying existing points and creating new, quality data from scratch.
- Implement stringent data management and security measures.
- Utilise cloud computing for adaptability and scalability.
- Improve AI model efficiency for the most productive resource utilisation.
- Integrate AI with smaller, more concentrated projects, such as resolving specific business-related issues.
- Make improvements to AI tools according to feedback and results.
- Encourage employee and stakeholder engagement during AI implementation.
- Support employees with AI training.
It is equally important to address potential biases in AI technology. Startup owners might consider launching an ethics committee or advisory board to establish responsible AI development and utilisation. The committee will review AI projects, detect possible biases, and prioritise transparency to build trust and manage risks.
Embracing AI in the startup landscape
As AI advances, startups should find ways to adopt it in practice. Although the challenges are valid, AI can transform businesses for the better. Considering startups must build themselves from the ground up, embracing AI responsibly and gradually is a sure path to success.
—
Editor’s note: e27 aims to foster thought leadership by publishing views from the community. Share your opinion by submitting an article, video, podcast, or infographic.
Join us on Instagram, Facebook, X, LinkedIn, and our WA community to stay connected.
Image credit: Canva Pro
The post Is AI making it harder for tech startups to survive? appeared first on e27.