As organizations plan to implement AI, they will increasingly focus on the maturity of their data environments. Data is a critical component for achieving the operational efficiencies and growth promised by this new wave of digital transformation. Organizations looking to rapidly enhance their data infrastructure should begin with small projects that have well-defined goals and provide direct business benefits. The value generated from AI will drive further investments, enabling organizations to undergo a transformative experience.
We have all read stories about the power of AI and how it can improve operations with automation1 and personalize customer interactions that drive revenue growth. According to Exploding Topics, 82 percent of companies are either using or exploring the use of AI.2 This new wave of digital transformation cannot be ignored.3 Organizations must leverage the power of their data and AI now, or they will be left behind.
Address gaps in data infrastructure
To rapidly embrace AI, an organization must unlock the full potential of its data. By consolidating data and making it easily accessible through a data platform, data scientists and technologists can develop powerful AI models. These models will generate insights and drive a new generation of AI applications, propelling the organization into an exciting AI-driven future.
Many organizations that want to jump into this AI journey do not have the adequate infrastructure in place to support this, such as a data lake or consolidated data set. These organizations do not have a basic data strategy or adequate governance to manage risk and compliance. Their reports and dashboards are not reliable and produce conflicting results. Unfortunately for these organizations, the jump into AI will be very challenging. There are many gaps to fill and many resources required to support them all.
What can you do if you find yourself in this situation?
One option is to create a comprehensive presentation for your CEO to teach him or her about the power of AI, how you are going to transform the organization with new AI capabilities, and the $3 million-plus investment needed for the people, technologies and services required to get there. This presentation may be well written and completely accurate, but you may face challenges convincing your CEO to say yes. This approach requires a large amount of investment with a return that may be seen as ill-defined or unclear.
Start small for a more practical approach
A more practical approach is to start small. To start the journey is a win. Starting with a small project that has well defined goals and direct business benefits is easier to start quickly and is more likely to gain approval. The business benefit can be either a reduction in operational costs or growth in customer engagement and sales.
Within the project scope, include:
- The implementation of the basic components of a data platform
- Consolidation of a few critical data sets
- Development of an AI application minimum viable product (MVP) to provide immediate value
Through careful project planning and execution, you can begin to shape your data strategy and governance programs within the scope of the project. Release the MVP quickly, then follow up with incremental enhancements that build upon the value and investment return.
Show value to continue your AI journey
After release of an MVP, it is critical to measure value, like lift in sales or reduction in man-hours, to help secure approval of the next project, and possibly even fund it. With each project that is executed, you can develop more of your organization's data strategy, governance and your end-to-end data platform.
Your AI Implementation Checklist
Starting small and building on your successes gives you choices that can make it easier to scale your transformation.
- Identify the right project, use cases and scope. It is essential that you have a clear view into the feasibility and value of the project.
- Stay focused on your near-term use cases and requirements. Designing an architecture to support your long-term use cases will take too much time and introduce excessive costs. By the time you are ready to tackle your long-term use cases, new technologies will likely be available, and your architecture will need to change.
- Leverage a cloud platform that allows you to pay as you go so that your costs align with your usage and value generation.
- Build with standard interfaces between components so that you can swap out pieces that cannot scale or that you have outgrown.
- Add a strong solution architect to the team who has done this before. An expert who designs and manages technical solutions will help align business goals and value generation with a successful technical design. A solution architect can also provide technical leadership that will give you flexibility in sourcing your development team.
- Leverage the right partner that understands the business challenges you face and has the capabilities to solve them with you. The right partner will upskill your team and guide you not only on project delivery, but also in the broader organization model, skills and processes needed to transform your organization as a whole.
- Make sure your data and AI models are appropriate for your expected outcome.4 Consider data bias that may exist and any privacy and compliance constraints.
The AI transformation wave will not happen overnight. It will be a gradual change over years. Organizations with immature data environments that cannot support new AI-driven applications today can still catch the wave. By starting with small, tightly scoped, well-defined projects that can generate near-term value, organizations can start to build the data platform that is required. Showing success and proof of the value generated will give you traction and opportunities to continue down the path of your AI transformation.
Ready to unlock the full potential of AI for your business? Our A&MPLIFY experts can guide you through starting small, delivering immediate value and building a robust AI strategy tailored to your organization's needs. Contact us today to kickstart your AI transformation journey!
Footnotes
1. Dan Simion et al., "Ready for AI Automation? Use a Large Language Model Agentic Workflow To Power Your Business Processes," A&MPLIFY by Alvarez & Marsal, July 15, 2024, https://www.a-mplify.com/insights/ready-ai-automation-use-large-language-model-agentic-workflow-power-your-business.
2. Anthony Cardillo, "How Many Companies Use AI (New Data)," Exploding Topics, August 21, 2024, https://explodingtopics.com/blog/companies-using-ai.
3. Bob Ghafouri, "Unlock the Power of AI: Where is Your Organization on Its AI Journey?" A&MPLIFY by Alvarez & Marsal, July 15, 2024, https://www.a-mplify.com/insights/unlock-power-ai-where-your-organization-its-ai-journey.
4. Dan Simion, Jamie Wheeler and Justin Gould, "Artificial Intelligence and Machine Learning Model Forensics," Alvarez & Marsal, January 31, 2024, https://www.alvarezandmarsal.com/insights/artificial-intelligence-and-machine-learning-model-forensics.
The content of this article is intended to provide a general guide to the subject matter. Specialist advice should be sought about your specific circumstances.