What are the major problems of an artificial intelligence-based startup? Read on with me to know more.
Artificial intelligence (AI) has completely revolutionised the startup scene. The recent OECD research states that from $3 billion in 2012 to around $75 billion in 2020, the yearly value of VC investments in AI businesses would have increased globally.
Chopwork is a digital market platform driven by AI that was just launched in Nigeria for buyers and sellers of digital goods and services. The company is committed to giving businesses a way to expand globally and offer customers high-quality services.
Even while having an AI solution does not guarantee that you will become a unicorn startup, recent investors and public interest have encouraged many more business owners to consider launching AI firms. But as a company in AI, What are the day-to-day problems an AI startup was founded on? And what are their problems and how to overcome them? What precisely do you need to know?
What Are The Major Problems of an AI-based Startup?
The major problems of an artificial intelligence-based startup include:
- Fundraising
- Marketing
- Lack of Data
- Lack of business ailment
- Lack of patience and trust
- Not that Advanced in Computing
- Lack of funds and Subpar IT infrastructure.
- Other challenges include a lack of qualified specialists, the high cost of development and implementing the technology, and privacy and ethical issues.
1. Fundraising
Startups still need to understand how to manoeuvre in the fundraising market, despite greater backing from VCs.
Many investors desire to ride the artificial intelligence wave but may not fully comprehend the AI industry.
In the realm of AI venture capital, AI entrepreneurs must learn to sort the good investments from the bad.
Read- How Startups Should Measure Labour And Growth
2. Marketing
It can be quite difficult to market an AI startup, especially if you want to go beyond buzzwords.
Startups in the AI industry must develop the ability to explain complex technological problems.
They must demonstrate to non-technical folks how their AI solution differs from others available.
Read- What Startups Should Know About Mergers and Acquisitions.
3. Lack of Data
Massive amounts of data may be processed fast by AI solutions, which can also find various patterns.
However, companies need to own or have access to a lot of data to be able to achieve that. If access to the data itself is not the problem, data security should be a top priority for AI firm founders.
Cyberattacks frequently target data. This is crucial for sensitive data, such as names, addresses, bank account numbers, etc.
Due to GDPR violations, failing to secure cyber security for AI startups may result in both reputational and financial losses.
AI start-ups should therefore be able to respond to the problems of whether they will have sufficient data to develop the idea and whether they can adequately preserve this data.
Lack of Business Ailments
Although the majority of businesses today use machine learning, they are not AI businesses. Businesses must have a self-learning algorithmic system and the capacity to make decisions on their own to qualify as true artificial intelligence (AI) companies.
Another problem with AI, like any other new technology, is the related hyper optimism that encourages businesses to operate without a defined ROI framework or to pursue impractical objectives.
The people who suffer from hyper optimism are typically new managers and startup executives who read inflated reports that are supported by vendor firms and begin to feel like they are lagging.
Startups need experts with in-depth knowledge of the field’s most recent developments to deploy AI technologies effectively.
5. Lack of Patience and Trust
AI is a relatively new and complicated technology. An AI system typically requires a long time to create, and it is typical to need at least two years before it starts to bring in money.
Since there is such a large disconnect between theory and practical application, it presents a significant hurdle for businesses that want to start making money right away.
For startup founders, waiting an eternity for any return on their investment can be very frustrating, unlike for larger corporations.
One of the biggest hurdles for an AI firm can be finding the proper data and employing the appropriate technologies. Therefore, it’s crucial to have faith in the technology and find the proper balance while creating AI products.
6. Not That Advanced in Computing
Heavy machinery and cutting-edge computers are needed to apply artificial intelligence, machine learning, and deep learning techniques to solve issues at supersonic speeds.
Due to their limited financial resources, startups have a very difficult time affording the modern processors that businesses need to produce at such a high pace of processing.
Cloud computing and massively parallel processing systems, however, have catered to the needs of startups to have a short-term answer.
However, the true issue starts when the amount of data keeps increasing and deep learning introduces increasingly sophisticated algorithms.
Startups must implement next-generation computing infrastructure to address this problem, such as quantum computing, which utilises the superposition principle to process data significantly more swiftly than current computers.
The startup’s long-term viability may be questioned further by its lack of a scalable product and its experienced AI founders.
7. Lack of Funds & Subpar IT Infrastructure
AI technology requires powerful hardware because it processes enormous volumes of data. Startups need a strong IT infrastructure and cutting-edge computer systems to power an AI-based marketing strategy, which may be quite expensive to set up and maintain.
To ensure seamless functioning, these systems probably need regular upkeep and update. And for startups and smaller businesses with smaller IT expenditures, this can be a major roadblock.
It becomes challenging for startups and smaller businesses to integrate AI solutions into their business operations, although giant corporations like Facebook, Apple, Microsoft, Google, and Amazon have specific budget allocations for the application of AI.
The industry did, however, come up with a different approach to avoid this issue. While larger companies might decide to create and manage their own AI marketing tools, startups with less powerful resources can always choose affordable cloud-based options.
Cloud software providers are incredibly helpful to startups since they offer all the IT infrastructure needed to deploy AI applications.
These cloud services are a clear alternative for companies lacking the IT infrastructure necessary to develop internal systems.
Conclusion
The software has indeed taken over the world, but artificial intelligence (AI) is supplanting software.
However, there is encouraging news for startups who are considering building AI-based mobile apps.
Many organisations have been able to attract more clients and generate more income thanks to the growing acceptance of AI in mobile app development.
Then, you may envision how these new firms, such as Microsoft, Google, and Apple, have altered user communication, expanded their audience, and succeeded in keeping mobile app users engaged.
Unstoppable artificial intelligence technology is opening up new opportunities. Hope you enjoyed the read on What are the major problems of an artificial intelligence-based startup? You can make your contributions via the comment section.
