5 Pillars of AI Deployment in Startups
Although AI is becoming a critical factor in the long-term success of startups, a majority of them fail to deploy it. Most of them feel that employing the best in tech would address their problems, when in reality, the lack of cultural agility and leadership stands in the way of AI adoption.
Startups should consider these five pillars of successful AI implementation before mapping out the adoption plan.
1. Strategy
Business leaders regard strategy as an essential component of deploying AI solutions. It must include elements of Customer Service Improvement, Cost of Deployment, Culture Management, and ROI to figure out the potential success of AI deployment. A clear understanding of various factors can save startups from losing millions in a failed AI deployment.
2. Data
Having access to the correct data and a lot of it is a significant challenge for startups. Some startups claim to have created the most incredible AI model, but they fail miserably when tested with clients’ data. Data must be sufficiently large to factor in all kinds of biases in decision-making. If sufficient data is not available for a problem statement, startups must avoid jumping on to create a sub-par AI model.
3. Technology
Business leaders must define the problem statement and the requirements before figuring out which technology to use. Choice of Technology & Cloud is crucial for any enterprise implementing an AI Solution. When considering a particular technology, a startup must examine a variety of parameters, including the speed of development, the cost of scaling, and the availability of skills in the market.
4. People & Talent
Today, almost every startup is struggling to hire & retain skilled employees due to the gap in demand & supply in the talent market. However, the supply problem will ease with time as more IT companies deploy internal resources to train talent. For the time being, companies may rely on empowering good employees as decision-making stakeholders for a retention strategy.
5. Governance
When building an AI model, always consider its governance. Startups failing to do so can find themselves struggling with lawsuits as a poor outcome from AI solutions that can cause potential losses to clients & partners. For example, a recent report from a leading media house highlighted how developers from a known startup had direct access to customer data from social media channels. The article highlighted that companies like Google & Facebook take strict actions in such cases.
Startups pivoting to adopt AI must ensure that all the necessary resources are in place for these pillars.
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Potential entrepreneurs can access Affine’s Centre of Excellence (CoEs) across AI, Engineering & Cloud to build cutting-edge solutions for swift market access with DeepCamp.
