In this guest post, Dmitrii Evstiukhin, director of managed services at Provectus, discusses five major AI-related challenges faced by growing ML/AI startups and offers possible solutions to overcome them and unlock the true potential of AI.

The startup business environment has been evolving over the last couple decades. With every new opportunity, new challenges appear that force companies to either adapt, or lose their niche to the competition. 

The AI industry, while presenting a goldmine of opportunities, is no different from other startups. In fact, AI startups have come and gone faster, on average, than other businesses. When it comes to ML&AI, many opportunities still await, but you have to act quickly — not hastily — to cement your competitive spot in the niche.

Avoiding rash and reckless decisions is key. Inadequate preparation for dealing with the current ML/AI landscape, from ideating on use cases to handling ML models in production, can lead to unwanted outcomes. 

A comprehensive AI business solution requires a set of must-have components, just to get off the ground, let alone generating stable value and lucrative revenue streams.

• A robust infrastructural foundation for Data storage and Machine Learning computations

• Data collection, processing, and protection components 

• End-to-end observability of Data/ML work

• Talent: Data Science, Data Engineering, ML Engineering, Platform Engineering — the bare minimum for success

Having these components in place can lead a startup to success, but they can be fraught with challenges and pitfalls. In the existing realm of solutions, the technical side of the formula is immensely scattered and diverse. Every solution’s pros and cons are dramatically different from those of competitors who are trying to solve the same problem.

So how can small startups address and resolve these challenges, and where do they begin? 

Thought leaders from Provectus share five major AI-related challenges, and offer ways for organizations to effectively deal with them while unlocking the true potential of AI.

1. The balance between investments and ROI

This challenge is not unique to startups, but it has profound effects on a smaller scale. Making the right choice between buy, rent, or build may define the overall financial success of a company from its first days in business. It concerns more than hardware and software solutions, but hiring an entire team of professionals while renting a data center may undermine the whole business. At the same time, if the technical quality required to tip the scales in a competitive landscape is missing, it can kill sales and, consequently, the business. For that reason, “renting” expertise rather than hiring a whole team of in-house professionals is a reasonable thing to do. Relying on MSPs and Managed AI providers may help to keep things in balance. 

2. Attention dispersion

Another common startup challenge is having too many balls in the air. Performance, security, cost efficiency, scaling — all these things need to be on your radar 24/7. Even if you have a true Jack-of-all-trades on your team, they can only devote so many hours a day. With Managed AI, startups can offload a significant portion of tasks to an MSP. While this may not solve the challenges entirely, it can at least make them manageable, freeing up time of key team members to focus on strategic, business-critical development.

3. Not enough expertise in AI

When you’re starting up, everyone does everything, and they are forced to operate in a know-it-all mode for lack of resources. As a rule, startup teams are extremely motivated and have broad expertise in different areas. Unfortunately, they are rarely AI experts. The provision of end-to-end Managed AI services by an MSP can help close this gap, in effect future-proofing decisions that lay the foundation of any new product.

4. Reinvention of the wheel

Startups often build their toolset along the way, instead of researching the market and testing already existing solutions. Taking time for research and testing may waste precious time and delay your domination of the market, but building your own tools is not time-efficient in the long run. It can double your efforts and create tech debt in the code that is at your company's core. An experienced team of Managed AI specialists who have worked with dozens of startups and have deep insight into the AI field can save many human hours, and redirect them to the primary value-generating activities. 

5. Privacy and security 

Startups and small companies typically lack adequate security. This is understandable, since you need to run very fast, just to hold your place, and even faster to get ahead of the competition. There is no spare time for security – We will do it later, right? The Internet is awash with cases of startups that were intentionally hacked. In some cases, hackers were astonished by the number of vulnerabilities they found. Not to mention the threats of broadcast hacking and all kinds of script kiddies. Being hacked is bad for your reputation, and even worse for a recently launched business. In the ML/AI field, the severity of this danger is even higher, because if you get hacked, you could lose your data — the lifeblood of any AI startup. It would be wise to bring professionals on board who can provide security-first Managed AI services, delivered by a team of cloud professionals who follow best practices.

Our list of challenges faced by growing ML/AI startups is far from comprehensive, but it highlights the key aspects, and offers possible solutions to overcome them. An experienced team can provide support on all levels — from setting up simple infrastructure to developing highly complex production machine learning systems. 

Proofing ideas early will save you time and resources down the line. Starting with Managed AI is a wise strategy for building a strong foundation for future success!

*This post was written by Dmitrii Evstiukhin, director of managed services at Provectus. We thank Provectus for their ongoing support of TheSequence.