Inc42 and Google Cloud recently held a roundtable titled How Startups Can Leverage AI & ML To Work Faster, Better And Smarter, exploring many critical areas such as:
- Tracking key innovations and challenges startups face while executing artificial intelligence (AI) and machine learning (ML) projects
- Embracing an AI-ML-first mindset to accelerate time-to-market
- Driving product research, innovation and cost optimisation/profitability using AI-ML
Moderated by Kshitij Shah, strategy consultant and former principal of 3one4 Capital, the discussion included Gaurav Bagga, SVP of product and engineering at Pristyn Care; Rahul Prasad, cofounder and CTO of Bobble AI; Kirthi Ganapathy, customer engineering leader at Google Cloud; Snehil Khanor, cofounder and CEO of TrulyMadly, and Arun Chandru, cofounder and CTO at Pandorum Technologies.
How Startups Are Adopting An AI-ML-First Approach To Drive Growth
Long considered a popular sci-fi theme, AI-ML is now extensively used to transform everyday life. But the masses may not have noticed it upfront.
Take, for instance, the personalised recommendations on Netflix, YouTube or Amazon. It is the handiwork of AI-ML. This advanced tech can auto-correct any written document’s grammar, spelling and typos, control self-driving vehicles, generate content at scale (novel-writing software is very much in use), create fine art and do a lot more things. Simply put, AI-ML-driven solutions are here to stay and grow.
According to a Zion Market report, the global AI market was valued at nearly $59.7 Bn in 2021 and is estimated to reach $422.4 Bn by 2028, growing at a CAGR of 39.4%. This indicates the rising adoption of AI-ML by startups and legacy industries alike.
Implementing AI-ML is often considered for its cost benefits (read fewer resources), quick time to market and the preference for hassle-free managed services. Interestingly, this takes us back to the fundamental question: Should startups build or buy the tech stack in question?
Speed is critical in the new-age, tech-driven market, as Prasad of the conversation media platform, Bobble AI, puts it. In the early days, the startup preferred managed services to quickly carry out product experiments and see if users liked a particular feature.
For the dating app TrulyMadly, AI-ML is used to automate tasks and reduce time-consuming activities such as scanning millions of user profiles and curating communities. On the other hand, Pandorum uses it for automating the tedious cell culture work and analysis as it is working on developing functional human tissues like bioengineered cornea and liver.
But startups chasing quick growth must not forget the costs incurred by AI-ML tools and their long-term impact on the business. “Weekly review meetings and cost optimisation tools help keep a tab on rising spends and close that tap,” said Gaurav Bagga, SVP of product and engineering at Pristyn Care which focusses on simplifying the entire surgery journey of both patient and surgeon.
AI-ML can undoubtedly be a boon. But if not regulated constantly, one can use the technology for various things which are ethically wrong.
“Just as we use AI-ML to understand how various parts of our body communicate to develop better medicines, the same AI can be used to develop toxins and dangerous agents,” added Chandru of Pandorum.
However, most startups think that Indian regulators are pretty mature and open to feedback from the industry as both parties constantly engage to tackle new developments in the AI-ML space.
According to Ganapathy of Google Cloud, the tech giant is already working with Indian regulators such as SEBI, IRDAI and the RBI to help draft security and compliance measures for AI-ML usage.
Given the growing usage, startups and businesses that have not yet jumped on the AI-ML bandwagon must do so soon or risk being left behind. It is high time that startups push the envelope and adopt cutting-edge AI-ML tech to leverage innovation and cost advantages and impact our everyday life positively.
Source: inc42.com
