Only around 25% of companies get through the AI planning stage in the first year, experts say. Firms accept that preparing and strategizing for implementing AI projects can be a multi-year journey, and needs to be faster.
This delay is due to several factors including the relative maturity of the technology, limited enterprise experience, lack of internal skill sets, risk and compliance concerns, low confidence with AI bias, and more. AI projects that take very long implementation time potentially impact the many more initiatives as well. With CIOs shifting from “projects to products” in their approach focusing on product management, these lengthy AI projects significantly delay innovative product releases.
CIOs need to fast track their AI implementations with smarter investment strategies incorporated in the broader digital transformation and innovation initiatives
Decide on Build or buy
The initial decision is to choose between building and buying, based on whether AI can become a core competency for the organization. AI vendors who offer cloud-based AI services could be quickly trained and deployed for particular use case instead of buying. The decision to build or buy is based upon the criticality of AI in the organization as a future core competency. Not all businesses need to develop their algorithms in-house. Smaller enterprises can effectively focus on the business benefits of incorporating third-party AI technologies into their core workflows.
Quality data over quantity
Successful implementation of AI is often just 10% of AI with 90% of the role played by data. Any datasets used to train AI/ML algorithms must mirror human decision-making without prejudices. The data quantity and type need to match the algorithm type. The statistically-based algorithms deal better with smaller data sets.
Smartly invest in change management and training
It is easy to rely on an AI API to pass in a new dataset and receive a score, but it is challenging to train analysts to interpret these scores best. Business analysts spend several weeks or even a month, observing the results from the ML algorithms. However, an AI vendor guides on interpreting the results and training employees to derive optimum productivity from the new system.
Hypothesize and adopt a test approach
Since every AI implementation is unique, it is crucial to be incorporated into every project with a “hypothesize and test” mindset. This approach is always preferred to viewing projects as either outright successes or failures. Such testing methodologies quickly refine the AI deployment, enabling it to become a workable solution. While the test approach might lengthen project deployment times, the benefit is that firms can continually fine-tune the solution to incorporate real-life lessons that align the customer requirements.
Integrate and incorporate automation into the future vision
As enterprises embark on initial AI pilots, proofs-of-concept or MVPs should be aligned to the future vision concerning enterprise-wide AI. Enterprises should adopt fusion automation – from entirely manual to robotic process automation (RPA). Simply inserting RPA or AI into legacy, business processes, will lack balance. Another critical factor is the handoffs that occur between each tool. This could be machine-to-machine or human-to-machine. By optimizing, the handoffs will make the process quick, seamless, and reliable to further enhance future business processes to be cost-effective and competitive.
AI implementations can be fast-tracked, but enterprises are not necessarily getting smarter about it. Firms may be able to leverage some of the recommendations here to help fast track the race to adopt AI.
Of course, firms need to be prompt with the adoption process as just like digital transformation; this race is never over.