Before getting started with an AI initiative, those involved need to take a hard look at the organization’s existing resources and capabilities.
It’s fair to say that Artificial Intelligence (AI) is no longer a ‘futuristic’ technology, but something that is actively in play across sectors. AI is amidst a period of transition, evolving from ANI or Artificial Narrow Intelligence (wherein the knowledge of machines is limited compared to human knowledge) to AGI or Artificial General Intelligence (wherein machines possess knowledge comparable to that of humans). The recent innovations in Large Language Models, such as OpenAI’s ChatGPT or Google’s Bard AI, have enabled its adoption to take a great leap forward – with organizations of all sizes eager to explore how they can incorporate such solutions into their core business functions.
Given the immense disruptive potential of the technology, the impulse to ride the AI wave is understandable. But this enthusiasm needs to be channelized through robust implementation strategies to truly drive business value. Moreover, there is a need for creative ‘out-of-the-box’ thinking when it comes to identifying ways to leverage this technology to save on time, labour, and ultimately costs.
Let us examine a few factors organizations need to keep in mind while planning AI initiatives for their businesses.
Understanding How AI Can Add Value to Business
As a first step, it’s crucial for leaders and team members responsible for AI initiatives to thoroughly understand the fundamentals of the technology and the benefits and capabilities it can potentially offer the organization.
Following this, they need to define the specific business needs and objectives that the solution is intended to fulfil or the specific issues that it is supposed to address.
What are the outcomes we need to drive? What are the gaps in our current systems/processes that need to be filled? How will AI enable us to address these challenges? And how will success be determined and measured? These are some of the key questions that need to be at the top of your mind. This clarity, coupled with a precise understanding of its capabilities, will lay a strong foundation for a powerful implementation strategy.
Ensure Effective Data Management & Storage
Data is the lifeblood of AI, which is why it is critical for any organization to implement a solution based on this technology, and to have a robust data management strategy in place. This begins with defining what data is required to address the goals of the implementation, and whether there are existing systems in place to capture this data. It’s also essential for the data to be ‘clean’ (i.e. accurate, free from incoherent information, and with all the attributes needed for an algorithm to perform its task), and to be managed securely and in line with the relevant data privacy regulations.
Storage needs to be a key facet of this data strategy. Systems require vast amounts of data to develop precise models. Given this rapid data growth, organizations need to ensure that an optimal storage solution is in place while planning the specifics of the implementation.
Assess and Build Internal Capabilities
Before getting started with an AI initiative, those involved need to take a hard look at the organization’s existing resources and capabilities. This is vital both in terms of technology as well as for people. Depending on the scope and complexity of the implementation, projects will require a skilled and diverse team that includes data scientists and engineers, machine learning specialists, and other domain experts.
Building a winning AI team will require leaders to look at both internal and external talent sources – from upskilling/reskilling existing employees to hiring fresh talent. Collaborating with an external partner specializing in these solutions, or outsourcing the development of the project entirely, are also options that can be considered.
Start with Low Turn-Key Solutions
CEO,
Fulcrum Digital
It would be wise to take a patient approach to AI implementation and begin with a simple, low-turn-key solution. By starting with a small-scale, low-cost pilot project, involving a small dataset, the team can safely test the effectiveness of their strategy. This serves as a ‘proof of concept’, demonstrating the success of the implementation to leaders and other internal stakeholders. It also provides the opportunity for the team to identify gaps with the solution and correct course as necessary.
To avoid having to make an expensive and time-consuming investment in building a solution completely from scratch at an early stage, it would be advisable to start with using a pre-built industry-made Machine Learning platform to build a quick pipeline for training and inference. Based on the results from the pilot project, the pipeline can be tweaked, and appropriate changes made with successive runs of the models.
Monitor & Evaluate Performance
Chief AI Scientist,
Fulcrum Digital
Once the solution is fully implemented, the team needs to regularly monitor its performance. The effectiveness of the solution needs to be measured against the KPIs (key performance indicators) defined during the initial planning process. Regular analysis of results will enable the organization to identify challenges and areas of improvement and make the necessary changes over time.
Following the aforementioned steps will enable organizations to begin their AI initiatives with a focused and practical approach. Needless to say, the work doesn’t end with a successful implementation. Continuous refinement and improvement of the solution – in line with evolving business needs as well as advancements in technology – are key to maintaining the competitive advantage that AI provides to a business.
