What Businesses Need to Know About AI Readiness

The emergence of artificial intelligence (AI) has dramatically reshaped various industries, with the potential to revolutionize automation, enhance customer experiences, and drive innovation. However, a troubling trend reveals that a significant percentage of AI projects do not succeed.

According to a report by Gartner, 80% of AI initiatives in 2023 failed to reach their full potential or meet their business objectives. This statistic underscores that while organizations have made strides in identifying viable use cases, effectively operationalizing AI at scale remains a daunting and often underestimated challenge.

Research from BCG indicates that 70% of AI projects in 2023 encountered failure due to critical issues such as poor data quality, lack of collaboration among cross-functional teams, and a disconnect between AI outputs and actionable insights. This emphasizes the importance for companies to focus not only on technical implementation but also on aligning AI initiatives with overall business strategies.

Furthermore, a 2024 AI adoption study by O'Reilly reveals that only 26% of AI projects progressed beyond the pilot phase. Meanwhile, the majority of AI projects faced significant obstacles, including failure to meet business goals, underperforming models, and inadequate infrastructure.

To ensure the successful implementation of AI, organizations must prioritize strategic alignment, invest in high-quality data, and foster collaboration across teams. Addressing these challenges can ultimately lead to more effective AI solutions and improved business outcomes.

Bridging Technology and Business

The failure of AI initiatives often stems from selecting technologies or models that do not align with business needs, as well as limitations in scaling efforts. One of the biggest mistakes organizations make is allowing a disconnect to happen between technology and business operations. 

AI should function as a virtual employee, enhancing productivity and supporting overall business strategy. It is crucial not to confine AI initiatives solely to your IT team. Instead, involve business stakeholders in the process to explore use cases without limitations. This collaborative approach will provide a comprehensive view of the infrastructure required to effectively scale your AI practice.

Setting realistic expectations is essential when onboarding AI into your organization. Begin with a clear and straightforward business use case to guide your team through the AI journey. This will help them understand the associated costs, potential benefits, and limitations of AI, ensuring a smoother integration process.

Assessing Your AI Readiness through a Data Journey

Your organization’s readiness for AI can be effectively measured by understanding your current position in the data journey. A PWC report highlights that “the data management market driven by AI needs is expected to grow to $513 billion by 2030”,  encompassing essential areas such as data ingestion, storage, transformation, analytics, governance, security, and orchestration.

Data preparation is crucial for successful AI implementation. While the phrase "No Data, No AI" is commonly heard, the more accurate assertion is "No Data Quality, No AI". Like any system, if your input is low-quality data, the output will be equally flawed.

To determine if your organization is data-ready for AI, evaluate your data quality in conjunction with your Business Intelligence (BI) capabilities. Are you able to generate accurate, qualitative business insights within the relevant operational context? If your reports and Key Performance Indicators (KPIs) rely on siloed data sources, this could hinder your readiness for AI adoption.

By prioritizing data quality and ensuring cohesive data integration, you can pave the way for a successful AI strategy that drives meaningful business outcomes.

Building your AI Project Step by Step

Your AI project can kick off once you have selected your business use case, and have fully clarified and identified KPIs to measure success. To ensure the AI solution will fully support your business strategy, you should never ignore the understanding and the design phase. It is an investment worth making for your business to be more competitive or productive. 

The Understanding Phase:

In this phase, collect and understand all the business cases that AI will have to support. This forms the foundations required to build a road map for your solution to meet the business needs from simple cases to more complex cases. It is important to start small to understand how AI development works, including issues of cost, task, compliance and infrastructure. 

With a use case formulated, you can identify the data and information the AI will leverage to meet your business requirements. Additionally, you will need data understanding and assessment to move ahead to design architecture and infrastructure.

This phase translates business cases into technology. The Data AI team will have to work together with business stakeholders to ensure future success.

The Preparation Phase: 

The dataset you use will need to be prepared with cleaning, tagging, cataloging, and tokenization operations  before loading those data into the vector database where your LLM will be trained, and eventually compute answers. This phase is crucial in limiting the risks of hallucination and fine-tuning.

Model Development and Validation:

Once your model is developed, it will be trained from the vector database with test data. You can evaluate your AI against your success criteria KPIs to validate whether your solution is meeting the business needs and requirements you have set for it. Some iterations of fine tuning will be necessary to reach your scoring success criteria. 

Operationalization:

Integrate and activate your model into your business operations with analytics to monitor and measure the accuracy of your AI tool. This bolsters the strength your stakeholders have in their new business companion. Have a pilot phase before fully launch your model where you can explain to your organization how the model works and demonstrate how the solution aligns with your business strategy.

Data as Key Success Factors:

AI will be fed with all kinds of data, such as structured, semi-structured and unstructured data. Here are some success factors all businesses should consider when determining your AI readiness:

1. Poor Quality Data

The quality of data obviously correlates with the performance of your AI model. Ensuring higher quality data limits the cost of fixing hallucinations that could otherwise drive your business into the wall, while  mitigating the costs of endless fine tuning, which would involve significant computation resources to retrain your AI again and again.

2. Biased Data 

To avoid having your AI model generate biased results, it is important to run your model with a large amount of data. The more data you use, the more accurate your model’s results will be. Your data pool can be enriched by external data acquisition, or generating more first party data. 

3. Data Control

 Strictly monitor the data flow to your AI solution to ensure compliance with legal and regulatory requirements. Ensure your data access management is clear and tested. AI faces the risks of exposing confidential data, and a data breach can create irreversible damage to your organization.

4. AI Output Performance

AI will drive many automated decisions or recommendations. Those should be continuously monitored with your business data to highlight any points for fine tuning.

Assess Your Company’s AI Readiness

AI can be very costly if business goals are badly expressed. Business and technical  teams should work together. AI can be a big investment for your organization, which is why it is important to be transparent between time and ensure that the solution and model is fully explained.  Involving  business teams will ensure that AI will help them to work more efficiently to focus on valuable tasks, and reassure them that it is not here to replace them.  

As the available AI market solutions grow wider and wider with time, onboarding a vendor expert as a technical consultant is a good first step in limiting risk, and can be a good bridge between your team, while fill the knowledge gap in your organization. If you're looking for more advice, contact us today.

Damien Velly,  VP of Data and Analytics at Seven Peaks Damien currently leads the Data Analytics department at Seven Peaks, focusing on BI solutions, management data platforms and end-to-end data solutions for clients. Damien has worked with clients in several industries including finance and banking, automotive, defense, retail and marketing.