By Mukundha Madhavan, APAC Tech Lead, DataStax
Since ChatGPT debuted to the public, generative AI has reshaped many of the processes we once believed to be set in stone. Look no further than the way in which this breakthrough has shaken up content generation and automation, which has been nothing short of astounding. A report by IBM finds that India has the highest adoption rate of countries surveyed, with 59% of enterprises here deploying these tools to optimise costs and accelerate automation. The path to success with GenAI, however, brings a set of challenges that can be avoided by following some best practices.
Charting the AI course
For organisations looking to integrate AI into their operations, there are four steps that need to be taken so they can maximise returns and unlock new avenues to drive success. Chief among them is identifying unique opportunities or issues that businesses have that can be solved by AI. This is because simply jumping on the AI bandwagon is not good for realising AI’s full potential. On the contrary, it can be financially wasteful. This kind of largesse just won’t do for businesses looking to thrive in today’s hyper-competitive landscape.
Secondly, organisations should think carefully about their implementation timelines. This is especially the case as generative AI continues to receive new developments, and a large number of resources to harness the technology. Rushing into the adoption process without careful consideration of resource allocation can lead to organisations failing to meet their AI goals. However, taking it slow is also ill-advised as organisations may miss out on new features. Therefore, businesses will have to carefully chart a timeline that works for them in the long run.
Third, a roadmap must include the necessary measures to implement AI, how the technology should be supported, and the KPIs needed to measure their effectiveness.
Finally, organisations need to verify the accuracy of their data as well as evaluate that the AI models and infrastructures are built correctly. This way, employees can minimize inaccurate and irrelevant responses (known as “hallucinations”) and gain accurate insights.
Beware of the pitfalls
A common mistake that organisations make is not having clear objectives for their AI initiatives. While some pride themselves on being early adopters, they often fail to define the outcomes these efforts are meant to achieve.
Fundamentally, developing an AI strategy requires clarity. When creating their AI strategies, organisations may seek to incorporate solutions quickly without having clearly defined goals that justify the need for the technology. Another frequent error is a lack of change management, which adds friction to the adoption process and is a one-way road to significantly diminished returns on investments (ROI).
Leaders should also be clued up on data literacy levels across the organisation. Ensuring that the people working with these tools understand data is crucial, as AI is nothing without it. Be wary, too, of underestimating budgets and resources as it can lead to businesses incorporating AI solutions that fail to meet their expectations. To that end, it is important that businesses realise that AI implementation is an ongoing effort and not a one-time activity.
Measuring success
But what do clear objectives look like? As with the adoption of any new technology, it’s critical that these metrics are realistic. That will inevitably look different from organisation to organisation, but generally it boils down to user satisfaction and impact.
Upon incorporating AI solutions into their tech stacks, organisations need to assess their impacts on user experience and satisfaction. To achieve this, organisations need to collect feedback by asking questions that allow users to highlight what worked or didn’t. This information allows organisations to chart future direction – shedding light, for example, on whether models should be re-trained to ensure accuracy.
Besides that, organisations should also evaluate their systems to see if they are performing flawlessly. By analysing historical and simulated data, organisations can test the models for any bottlenecks and resolve them quickly.
AI works best when organisations have a clear direction on what goals they want to achieve and how to meet them. Otherwise, businesses will fail to achieve hoped-for outcomes. Harnessing AI, therefore, will require the right strategies and a deep understanding of limitations so that businesses can make the right calls.