Unleashing the Power of Groq: A Revolution in AI Performance
Unsupervised learning, a cornerstone of artificial intelligence, has emerged as a powerful technique that enables machines to uncover patterns and insights from unlabelled data.
According to two recent Gartner reports, 85% of AI and machine learning projects fail to deliver, and only 53% of projects make it from prototypes to production. Yet there is no indication of investments slowing down. Because now, more than ever, executives are eager to leverage on the benefits AI and ML promises to deliver. Each is finding ways and means to start a successful project.
Let’s take some time to look at some of the main reasons why AI projects succeed and some of the reasons they fail.
In fact, all of these pitfalls are avoidable. Learn from these before you embark on your own AI & ML project.
As studies have outlined, the success or failure of AI initiatives has more to do with people than with technology. As Executives and Managers, it is vital to not only pave the way to get AI/ML projects approved but also to enable the key elements to the project’s success. The first one is to develop a strategy.
Best Practices in Starting an AI/ML Projects
Develop an AI Strategy
It’s important to have a well-defined AI strategy in place that clearly outlines the drivers you want to achieve, the outcomes you want to accomplish for the project, and to identify the target users. This is crucial in setting AI roadmap and priorities as well as funding especially for multi-stage implementation.
Determine your AI Readiness
Examine existing business processes, systems, and data structures to determine the gaps between existing operation and the target setup during and after AI implementation. Look into organisational structure and possible changes when adopting the change and how much disruption this will be to both the operation and the people. How about the skills necessary to implement and deliver the project? Having a gauge of these factors will enable a better understanding of additional steps necessary for a successful implementation of AI/ML in the enterprise.
Planning and Exploratory Data Analysis (EDA)
Establish immaculate strategy, solid planning including Project Management and selecting the right team. Clearly define the problem with close alignment to business outcomes and SMEs, and test the hypothesis before investing massive resources.
A reputable AI/ML consulting team usually a senior data scientist will be able to initially look at your data, make sensible adjustments for data quality, test your hypothesis and give you an indication of how likely your project is to succeed.
Data, Data, Data
Data ingestion, data validation, sample testing, statistics, data quality remediation and data augmentation when necessary. Project teams often overlook how significant and time consuming this stage can be, as this forms the foundation of successful ML/AI projects.
Iterating between Feature Engineering, Modelling (ML/AI) and Tuning
Until an acceptable level of accuracy and predictive capability is produced. There is a need to re-iterate about black box tools and off-the-shelf tools and the need for customisation to extract the best results.
Operationalisation
The real test of any AI/ML project is operationalisation. When we feed real-time or new data automatically through the model to make dynamic predictions about future operations. If managed properly and efficiently is a key success element. Having not just the right hardware and software but also the right team: developers and engineers with the skills and knowledge to integrate AI into existing company processes and systems. Ensure the project is developed as a software engineering and practice that aims to integrate software development and software operations, as it is the key to converting the work of AI engines into real business offerings and achieving AI at a large, reliable scale.
Reporting, Evaluation and Continuous Improvement
Set up reporting and evaluation success and alert criteria to know how your model is performing and when to recommence Feature Engineering, Modelling and Tuning. Continuous improvement is an important element in AI and ML, ensuring the model is optimized to adopt to changes and additional information that will help shape better business decisions.
Now let’s look at some reasons behind most common pitfalls in implementing AI/ML. This way you won’t be going in blindsided as you start planning for your project.
Common AI/ML Implementation Pitfalls
Not looking at AI as a way to improve existing business process
Embarking on an AI project without knowing what it is you are trying to address or answer is the first sign it will fail. The latest hype and trends will not mean anything to your business if this does not address your most pressing need – make better business decisions. So keep your focus and your sight on your core business value. Make your AI/ML project as a way to improve business process.
Expecting immediate transformational wins
Successful AI and Machine Learning initiatives require experience in its people, process, technology, and infrastructure. Any organization implementing a project should not expect immediate transformational wins as experience is not achieved quickly. It takes time, years even. Many projects fail as they are beyond the capability and capacity of the company.
There are numerous business decisions made in large enterprises on a daily basis that could be automated by AI and data. Tap AI to improve small decisions to get better returns on the investment. Companies are better off starting with less risky investments to improve their existing processes.
Neglecting Organisational Change
The failure to or the difficulty of implementing change management is a large contributor to the overall success or failure of AI projects. Employee mindset, buy-in, and preparedness for a data-first environment is critical in the project’s success, even more important than the AI itself. When everyone understands how important their role is in the transformation of an organization, it is much easier to implement the change. An important first step to a successful AI initiative is building trust that data-driven decisions are superior to gut feel or tradition.
Not considering operationalisation
AI & ML initiatives must be integrated into existing systems and operations. If the project means major disruption to operation or unable to link with existing data systems then there is high chance of getting the project halted. Throughout the course of planning, integrating this to the existing operation and system is critical and involves expertise of engineers and developers.
Other Considerations
Technology Consideration – work with an AI/ML consulting team that leverage an open source Data Lakehouse and remain technology agnostic with use of popular platforms. Don’t use black boxes, use only customisable open source products that don’t get tied into proprietary products that promise everything out of the box, as these usually result in abandoned projects and PoCs since off-the-shelf black box tools simply can’t deal with the essential customisation and automation required for successful operationalisation and continuous improvement of AI/ML.
Don’t wait until you have the data just right to get started. start now, and improve your data over time. Also, from a financial perspective, the short-term and long-term ROI is inevitably higher if you start today.
Before making any commitment, make sure you’re ready to overcome the challenges of implementing an AI/ML project, breaking down the barriers. So, you can start leveraging the value of AI. If you’re not confident how to start, AI Consulting Group’s Data & AI Think Tank offers a complimentary 2-hour consultation with a Management Consultant and senior AI/ML specialist to roadmap identified opportunities, implementation outlines, and potential next steps.
