Everyone is taking part in the AI scramble, some more successfully than others, as we all know. But many enterprise companies are still just getting their feet wet. Here at Qlik we saw an opportunity to try to capture what it is that these successful companies did to get value from AI for their organizations. So, we partnered with IDC to get those answers and discovered that preparation is a major difference maker for success with AI. Somewhat unsurprisingly, just like many other things you need to set yourself up for success. Chefs need their “mise en place”, AI practitioners need AI ready data and analytics.
Analysts Megha Kumar, Research Vice President, Analytics and AI, IDC, and Stewart Bond, Vice President, Data Intelligence and Integration Software, IDC published a truly insightful look into the findings of their research. Uncovering that AI is set to contribute $20 trillion to the global economy through 2030. You can find the full breadth of their findings in this Qlik Sponsored InfoBrief: AI-Ready Data and Analytics: Priorities and Challenges of Data and Analytics in the Midst of AI Momentum.
I sat down with Megha, and Anna Lee Munns, one of Qlik’s long standing customers at JBS who contributed to the study to talk through all the findings. So, if you have a free 30 minutes, or prefer to listen to instead of read your research findings, I highly recommend you tune into this exciting webinar. But for those who want answers fast, I wanted to give a little insight beyond what could be found in the InfoBrief. What all this really means, and how you can use it to your advantage!
98% of organizations view AI as a priority for their organization, but only 26% had a GenAI solution in production, and only 12% are confident that their current infrastructure could support agentic AI. Clearly there is a considerable gap with the goals of the enterprise and reality. So, what can you do? The InfoBrief has some great stats for sure, but what I really wanted to focus on using the findings of this study to help any organization set themselves up for successfully AI implementation.
Now we get into the meat of things! Here are the 6 Essential Steps to AI Readiness my team developed based on IDC’s research. It’s a quick guide of what you need to know to get your data and analytics ready for successfully AI implementation across your use case – your mise en place.
6 Essential Steps to AI Readiness
1. Develop An AI Project Framework
To institutionalize AI for organization-wide use, an organized project structure is necessary to overcome technical data challenges and manage the relevancy of the outcomes. The biggest goal is to avoid having to reorient your approach mid-way through your AI project – 20% of organizations completely changed their approach to data since the emergence of GenAI and 69% updated their strategy.
The best first step is to outline what you want your model to solve for, what data do you need, how you need to prepare your data for training, who will develop your models and track performance, and how will you deploy your product and integrate it with other systems. Along the way make sure to avoid common technical challenges – develop a process of training and tuning your models, overseeing appropriate data quality, ensuring outcomes are relevant and accurate across both your initial use case, but future use cases as well!
2. Treat Data Transformation as Pivotal to Success
Ensure data is structured for your desired outcome. Emphasize a full understanding of your data products’ capabilities with your AI solution architects, and full gaps in data quality before launching an AI project.
Real-time data access is critical for most workflows, but accuracy, governance, and security problems need to be resolved before real-time can be realized. Organizations with a high-level of proficiency in their data products are 7X more likely to have GenAI solutions in production.
61% of organizations indicated that tailored and integrated platforms for specific data and analytics systems is more meaningful than a larger model trying to meet the needs of every use case. The TLDR – Prepare your data accordingly.
3. Consolidate Data Management & AI Planning to One Owner
Collaboration constraints, managing expectations of AI outcomes, and developing skills for implementation are the biggest challenges for AI implementation. Centralizing an AI-data owner can break down silos. 3 in 5 organizations consolidate data management and data analytics responsibilities to one person – 39% of organizations assign the responsibility to the CIO, 29% to CAO/CDO .
Help facilitate how to prioritize the needs of all teams who will depend on the validity of the AI outcomes – IT will be primarily concerned with technical debts, where line of business may be concerned with skills and training. So having one person to be the decision maker on when to loop in each team will keep your project on track.
4. Integrate BI/Analytics and Data Platforms Before Kickoff
Ensuring data quality, governance, and security across the new data sources that may be needed for AI implementation will be critical and demands integration for success of AI initiatives. 49% of organizations cite AI-based automation in their analytics platform as their immediate investment priority – Integrating AI into analytics via automation is the only way to improve productivity of analytics users, and having data integrations within the same platform will streamline usability and workflows.
Focus on AI, quality, and improving access to unstructured content. For any AI product to function, the ability to export data into AI preparation tools is the first step you need to consider. So, a consolidated data structure allows for smooth integration, and seamless troubleshooting.
5. Plan to Embed AI and Analytics into Priority Applications
To increase utilization of AI and analytics for pervasive insights, better data utilization, and enhanced user experiences ensure your product is embedded where your users can access it in their most used applications. 74% of organizations are seeking AI systems that require deeper integration with their existing data infrastructure and workflows. 73% specified the use of GenAI within their analytics solution.
94% of organizations with successful AI implementation have embedded or are in the process of embedding analytics into their enterprise applications with the goal of achieving better utilization by business users (52%), enhanced user experience (50%), and to allow analysts to focus more on analytics projects (49%). If AI isn’t readily accessible, don’t expect people to go out of their way to use it.
6. Set Realistic Expectations of Your AI Solution
Involving key stakeholders in defining AI value and develop a phased approach to data prep and implementation will set you up for success. If you set clear expectations for each development stage including output, you won’t end up having to field usability questions for the first month after launch.
Define not only what users can expect of your AI product but be prepared to capture feedback on how to evolve your tool based on what you will inevitably learn after users get their hands on the tool. For example, 80% of organizations are investing in Agentic AI – fully outlining the interactions and autonomous decision making you require from your solution will determine what you need from your data infrastructure. But only 12% of orgs are extremely confident that their current infrastructure can support Agentic AI. Understanding users wants and needs from an AI tool will set you up for further success.
Be realistic and share that realism broad and wide! Over 55% of organizations expect GenAI to generate data insights and trends, create data visualization, and predict forecasting and scenario analysis. Similarly, 40% expect GenAI to combine structured and unstructured datasets. Ensure projected results are clearly defined. If you can only feasibly launch with a portion of those capabilities, make sure everyone is aware! But consider setting up a roadmap to meet all expectations of your user-base later down the line.
Set the table for AI success
Success with AI doesn’t start with algorithms—it starts with readiness. The companies leading the way aren’t just experimenting, they’re executing with purpose. By following these six essential steps, you’ll be laying the groundwork for AI that delivers real, scalable value. Just like a chef wouldn’t start cooking without prepping their station, your organization shouldn’t dive into AI without its mise en place. Ready your data, align your teams, and embed AI where it matters most—and you’ll be ready not just to compete, but to lead.
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