AI can be adopted in a multitude of ways, depending on organisational needs and business intelligence insights derived from data gathered.
AI seems to be the way ahead as every organisation is increasingly looking at AI. According to IDC data, the region’s cognitive and AI spending will reach $1.0 billion by the end of this year. While organisations recognise the important role AI plays in supplementing businesses today, many still struggle to implement sound AI strategies due to a lack of knowledge.
A common misconception is that AI presents a one-size-fits-all solution to otherwise complex and layered problems. The reality is that AI can be adopted in a multitude of ways, depending on organisational needs and business intelligence (BI) insights derived from data gathered. From mining social data to driving engagement in customer relationship management (CRM), the flexibility of AI gives organisations the freedom of choice to best address their most pressing needs.
Bringing AI into an organisation can often be a daunting task. However, understanding the technology specific building blocks needed can help organisations kick-start their AI adoption journey.
Organisations need to decide on where and how AI capabilities can help enhance existing products and services. They must understand the inherent value that exists in identifying necessary goals and targets in specific instances where AI could solve a business problem or generate adequate value. This allows for a better formulation of appropriate and efficient strategies to attain these goals.
Case in point, AI solutions cannot be generalised and require specific data resources and training, which means that organisations should place greater emphasis on activities that have the greatest potential business impact. In essence, by developing a keen understanding of the different aspects of AI, be it machine learning, deep learning, or natural language processing, organisations can utilise various facets of AI—cohesively or on their own—to better plug the necessary gaps within the system.
Organisations must consider these three factors to help streamline decision-making:
• One-time costs. Analyse the initial capital set-up involved in implementing a new AI solution, versus pay-as-you-go “AI as a service” platforms.
• Costs involved in switching solutions. Evaluate the costs associated with replacing existing solutions with the desired AI solution. This means more than just monetary costs in replacing legacy systems (which might also mean wholesale changes to other parts of the IT ecosystem) but includes cultural and political elements as well.
• Ecosystem requirements. Determine if an integrated solution will require any complementary technologies. For example, an AI solution that must be integrated with innovative IoT sensors and emerging robotics technology will be more complex to adopt.
Organisations must also plot their AI goals based on existing capabilities as there is often a gap between organisational goals, and what can actually be executed and accomplished within a specific period of time. It is important to first assess benefits — such as improved marketing or brand identity despite the fact that it may or may not be less quantifiable. This helps organisations avoid falling into the trap of seeking immediate monetary gains and sets them up for tangible success across the long term as well.
Alternatively, organisations can also look to pursue small-scale plans that deliver small-scale payoffs, similar to a pilot programme before aiming for larger implementations. While this process could potentially be longer, it helps ensure well-established processes are set up. Using these small-scale projects can help address any vital missing links and helps identify processes and solutions that need to be acquired or improved before actually beginning to implement AI systems. This also helps address the point earlier on there being no one-size-fits-all method when it comes to AI implementation. It all comes down to individual needs and requirements.
With the Indian landscape evolving at such a rapid pace, we’ve learnt that our partnerships with leading businesses are essential in helping our customers succeed. A classic example of this would be our recent work with the University of Adelaide. By developing a greater understanding of their needs, we were able to work with them to build, test, and deliver a system for research use in just six weeks.
It is absolutely crucial to note that after ensuring the organisation is primed to implement AI (both technologically and organically), it is important to have trusted partners who are experts in the field that can help provide the much-needed perspective around AI implementation. A partner can help:
• Set realistic, achievable goals
• Keep a tight timeframe and lean team to ensure goals are streamlined and focused
• Get a clearer picture of what can or needs to be done moving forward
(Disclaimer: The views and opinions expressed in this article are those of the author and do not necessarily reflect the views of YourStory.)