In the high-speed train of technological innovations, AI holds a first-class ticket. The AI hype is certainly real.
And its impact echoes across diverse sectors, from the global tech landscape to the business world at large.
Recognised AI applications such as OpenAI’s ChatGPT and DALL·E have indeed sparked interest in their vast potential. According to AppRadar, AI apps have recorded a staggering 23.6 million downloads by Android users since November.
Still, the task of deciding which AI-based solutions to adopt poses a challenge for decision-makers and investors.
Especially with the widening range of applications available.
In a recent article by Juan de Castro of Cytora, there’s emphasis placed on the need for businesses to approach AI adoption with a focus on ROI and minimum risk, while addressing tangible pain points with tried-and-tested solutions.
Leveraging Business Value and ROI
Even though ChatGPT and generative AI have ignited mainstream AI interest, their adoption is only the tip of the iceberg.
AI’s transformative power is most evident in sectors like insurtech and manufacturing. The availability of dynamic data-driven solutions have improved operational processes, digital risk assessments, and improved customer experiences.
What’s intriguing is that sectors typically not associated with advanced tech, like insurance, are leading the AI adoption race due to the immediate business value and ROI these solutions offer.
The decisive factor for businesses considering this technology should be the technology’s potential to deliver substantial ROI with minimum risk.
Tried-and-Tested vs. Bleeding Edge
AI’s versatility presents a bewildering array of options for decision-makers.
While being adventurous with the latest innovations can seem tempting, it’s crucial to bear in mind the maturity and development levels of each solution.
For instance, fintech startups have a proven history of leveraging data science for solutions that lighten the load on finance departments while providing actionable insights.
“Fintech startups have a long track record of using data science to create sophisticated solutions that reduce the burden on finance departments and equip business leaders with real-time insights. Some of the latest advancements have concentrated on AI-enabled cash flow analysis and forecasting,” says de Castro.
Many of these services, with a focus on AI-enabled cash flow analysis and forecasting, are already tried-and-tested. This helps reduce the risk of misapplied AI.
Finding the Right AI Solution: Addressing Business Pain Points
The key to making the most out of AI is to align its adoption with actual business needs.
It’s essential to view AI technologies as tools to build enterprise-ready solutions that address tangible pain points. Efficiency, customer experiences, and pain points’ reduction are the cornerstones of an AI strategy designed for high ROI.
The process entails analysing internal data and gathering team and customer feedback to identify the most pressing business issues.
“To do that, you need to look at your internal data as well as team and customer feedback. From there, you will be able to narrow your search for AI solutions.”
Infrastructure: The Backbone of AI Integration
Adopting AI without preparing the business infrastructure for its integration can lead to complications.
AI systems perform optimally only when powered by unobstructed, complete, and accurate data. This necessitates a data management infrastructure that prevents information silos, encourages data sharing and analysis, and ensures data collection and management consistency.
Here are a number of statistics by Deloitte on the importance of data management in AI adoption:
- According to a survey by IDC, 28% of AI/ML initiatives failed due to lack of expertise, unavailability of production-ready data, and absence of an integrated development environment.
- A survey by Trifacta revealed that 46% of data professionals spent more than 10 hours every week to properly prepare data for analytics and AI/ML initiatives.
- Deloitte’s latest ‘State of AI in the Enterprise’ survey showed that at least 40% of adopter organisations reported a “low” or “medium” level of sophistication across a range of data practices.
Starting with small-scale AI implementations can help businesses test their infrastructure and policies’ readiness for broader AI adoption.
Human Oversight: Enforcing Guardrails
Even as AI automates processes, human oversight remains crucial.
With the data skills shortage in the market, educating company staff about data usage is vital. Especially to identify suitable solutions, monitor their outputs, and deploy them effectively.
This approach requires a top-down effort, with the expertise spread across every department.
It aligns with the “human on the loop” model, where human oversight ensures the AI system’s output is accurate and reliable.
De Castro mentions, “this model is what is often referred to as the “human on the loop” model, where systems do not rely on human input to perform their activity (as traditional “human in the loop” systems did) but instead push human control farther from the centre of the automated decision-making, playing a review role in ensuring the output is accurate and reliable”.
Navigating through the AI hype
The current buzz around generative AI revolves around marketing, especially with copy and imagery generation. While these applications might be a starting point for many enterprises, the key is to focus on how AI can expedite solutions to existing problems.
Often, these don’t necessitate the generative component but hinge on understanding unstructured data.
Navigating through the AI hype and identifying the right AI solution is just the starting point.
Success in AI adoption relies on having the right infrastructure, internal expertise, and checks and balances in place to ensure maximum value from AI implementation.
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