As limitless as the possibilities are, when it comes down to the brass tacks of AI adoption, a lot of organizations flounder.
Artificial intelligence (AI) has existed in some form for decades now. However, it’s only recently that researchers, organizations, and governments have truly started to comprehend the sheer scale and scope of the technology and its innumerable applications. For a long time, the scope of AI was restricted to Artificial Narrow Intelligence, wherein the knowledge of machines was limited compared to human knowledge.
Over the last several years, though, the world has started to transition into a new era in AI evolution – the advent of Artificial General Intelligence, wherein machines possess knowledge comparable to that of humans. And the launch of large language models such as OpenAI’s ChatGPT and Google’s Bard AI has only fueled greater excitement about the limitless possibilities of an AI-driven business landscape.
As limitless as the possibilities are though, when it comes down to the brass tacks of AI adoption, a lot of organizations flounder. Leaders and teams responsible for AI initiatives have the challenging task of managing the high expectations of multiple stakeholders while finding ways to incorporate AI into business functions in an impactful, cost-effective, and ultimately profitable manner.
Leveraging AI requires a substantial investment in terms of time, financial resources, business knowledge, and human effort. It is therefore understandable for organizations to commit to implementing AI solutions only if they have a strategy in place that drives business ROI. Here is a broad framework that organizations can follow to ensure that they derive the best possible value from the technology.
Identifying Goals and Challenges
Before the AI journey begins, it is important to first define what the ideal destination should be for the organization. AI is not a “one-size-fits-all” solution that a business can purchase off the shelf to automatically solve all its problems in one fell swoop! As with any other technological investment, it needs to be guided by a specific purpose.
What business challenge is AI expected to address for the organization? What is the goal that the AI initiative is working towards achieving? – These are the questions that leaders must ask themselves at the outset while determining the use-cases for the solution. Following this, they need to define concrete parameters – in the form of benchmarks and KPIs – to track progress towards achieving these goals.
Ensuring the Readiness of Data
Any AI solution an organization implements is only as effective as the ‘fuel’ it is fed, in the form of data. Insufficient access to data, or poor quality of data, are among the key barriers to successful AI adoption. This is why it is crucial for leaders to prioritize data while commencing with any AI initiative.
Organizations need to evolve a robust strategy for understanding, architecting, and activating data. They need to assess the quality of the data being captured (and the validity of the sources of this data) and ensure that the data is both sufficient as well as relevant to solving the business problem at hand.
Choosing the Right Solution
CEO,
Fulcrum Digital,
Investing in the right AI technology is critical for the success of an organization’s AI initiative. At the outset, leaders and teams should take the time to understand the various AI technologies available, the tasks they can perform, and the strengths and limitations of each option. Above all, the technology selected should be in alignment with the core objective of the AI implementation.
For instance, if the objective is simply to minimize human effort by automating routine and repetitive processes, then a rules-based system such as Robotic Process Automation (RPA) will likely be the ideal solution. For more complex use-cases that need to be handled with greater sophistication and human-like thinking, a solution that incorporates deep learning techniques, which will enable the AI to learn and improve over time, would be a better choice.
Implementing the Solution and Monitoring Performance
The gap between expectation and reality when it comes to emerging technology such as AI can ultimately only be addressed with a live implementation. Greenlighting an ‘AI Pilot’ project to assess the AI solution before rolling it out across the enterprise would be a wise decision.
An AI Pilot project would serve as testing the water in proving the value (POV), enabling the team to test the solution, determine if it fulfils the core objective, and identify and resolve bottlenecks and other issues before wider adoption of AI. Moreover, it will provide a better understanding of the true costs, efforts, and complexities involved in the solution and the potential for customization.
Once implemented, it’s essential for leaders to constantly monitor the solution and measure its ROI using the parameters defined during the initial planning process. This will enable them to determine if the solution is successful and ready for wider implementation and/or take an informed decision about the program’s future if expectations are not met.
Scaling the Solution
AI Evangelist,
Fulcrum Digital
If the AI Pilot is successful, and once the learnings from it have been incorporated into the program, the AI solution can be implemented at scale. This will require careful consultation with stakeholders across the organization. A collaborative approach will be essential to address some of the key challenges of enterprise-wide implementation of the AI solution, such as integration with existing systems and processes.
Ensuring the continued success of the implementation will also require teams to invest in ongoing maintenance and development efforts.
AI, if implemented properly, can be a powerful asset for driving business ROI. As the technology continues to evolve, as well as become even more ubiquitous, it is incumbent on organizations to develop a robust AI strategy with clear objectives and benchmarks for evaluation.
