Learn practical steps for preparing your business for AI, focusing on data, change management, and strategic value creation.

How to Prepare Your Business for AI: Practical Steps

Artificial Intelligence (AI) has emerged as both a powerful tool and a disruptive force, reshaping industries and challenging traditional workflows. Yet, as exciting as AI seems, it’s crucial to approach its adoption with a balanced and practical mindset. While the hype surrounding AI often promises revolutionary gains, businesses must ground their strategies in realism and focus on long-term value rather than short-term fascination.

In this article, we delve into the transformative potential of AI for enterprises and small businesses alike, emphasizing pragmatic ways to embrace this technology. Based on a thought-provoking discussion with industry experts, this guide provides actionable insights on integrating AI into your business, navigating challenges, and preparing for the future.

The Dual Nature of AI: Hype Meets Reality

The conversation around AI is polarized - on one hand, there’s unbridled optimism about AI’s potential to revolutionize productivity and open new markets; on the other, there are fears of job displacement and rapid societal shifts. Historically, similar waves of enthusiasm and trepidation accompanied the PC revolution of the 1980s and the dot-com boom of the 1990s. Both brought immense change, but their benefits unfolded gradually, often requiring patience and adaptation.

A key takeaway is this: AI is not magic - it’s math. It’s a tool, albeit a powerful one, that must be wielded by humans to create real value. Businesses that succeed with AI will focus not on the technology itself but on its application to specific business problems and opportunities.

How to Prepare for AI Integration: A Foundational Approach

1. Focus on Business Capabilities, Not the Hype

AI is most effective when applied to areas that align with your core business objectives. Instead of chasing trendy use cases, identify areas where AI can drive measurable impact. For example:

Insurance Industry Use Case: In insurance, tasks like document review and claims recovery often involve handling vast amounts of unstructured data. AI can process and analyze these documents more efficiently than humans ever could, making previously cost-prohibitive processes economically viable.
Legacy Migrations: Many businesses have struggled to modernize outdated systems due to high costs. AI can automate complex migration processes, potentially unlocking value that was previously inaccessible.

Start with the question: What was impossible or uneconomical before that AI can now make possible?

2. Lay a Solid Data Foundation

AI success begins with high-quality data. Without structured, reliable, and well-organized data, even the most sophisticated AI tools will produce subpar outcomes. Preparing your business for AI includes:

Ensuring your data is clean, accurate, and fit for purpose.
Establishing strong data governance practices.
Investing in metadata management and knowledge organization (think of this as creating a "Dewey Decimal System" for your data).

Remember, data quality is non-negotiable. If your data is flawed, your models - and the decisions they inform - will be flawed too.

3. Embrace Small Language Models (SLMs) for Specific Business Use Cases

While large language models (LLMs) like ChatGPT have captured public attention, smaller, domain-specific models may be better suited for most enterprises. These models offer several advantages:

Cost-Effectiveness: Smaller models are less expensive to train and run.
Predictability: Their outputs are more reliable because they’re trained on focused datasets.
Accuracy in High-Stakes Scenarios: Industries like healthcare, legal services, and finance require low tolerance for errors. Smaller models tailored to these contexts can reduce risks.

The future of AI in enterprises lies in leveraging smaller, more efficient models within agentic systems that combine multiple tools to achieve specific goals.

The Human Factor: Roles in the Age of AI

Evolving Roles in the Enterprise

AI doesn’t remove the need for human expertise - it shifts where that expertise is applied. Two emerging roles stand out as critical in the AI era:

Data Librarians: These professionals ensure that data is organized, accessible, and understandable. Think of them as the custodians of your organization’s information assets, responsible for cataloging datasets, maintaining metadata, and assessing data reliability.
AI Engineers: These specialists focus on fine-tuning, integrating, and maintaining AI models. Their role involves bridging the gap between business needs and technical execution.

These roles are inherently cross-functional, requiring professionals to combine technical knowledge with business acumen. As AI automates repetitive tasks, organizations will rely more on individuals who can synthesize information, understand context, and drive strategy.

Building a Culture Ready for Change

Change management is critical to AI adoption. Historical lessons show that technology is rarely the stumbling block - people and processes are. Businesses must invest in:

Training and Upskilling: Help employees understand and leverage AI tools effectively.
Clear Communication: Address concerns about AI replacing jobs by emphasizing its role as a productivity enhancer rather than a disruptor.
Cross-Functional Collaboration: Break down silos between data teams, business units, and IT to foster innovation.

Looking Ahead: The Future Beyond AI

As businesses adapt to AI, new frontiers are already emerging. Quantum computing, for instance, could redefine what’s possible in data processing and model training. Preparing for these shifts will require a forward-thinking mindset and a willingness to embrace continuous learning.

While the technology landscape will continue to evolve, the underlying principle remains the same: focus on creating value for people, supported by tools - not the other way around.

Key Takeaways

AI is a tool, not a silver bullet. Its success depends on human creativity and its alignment with business objectives.
Start with tangible use cases. Identify processes that were previously uneconomical but can now be automated effectively with AI.
Data quality is critical. Invest in robust data governance to ensure your AI models work on reliable foundations.
Focus on smaller, targeted models. Domain-specific AI offers better accuracy and cost-efficiency than generic large language models for most business contexts.
Roles are shifting. Emerging positions like data librarians and AI engineers highlight the need for cross-functional expertise.
Change management is essential. The cultural and human aspects of AI adoption are as important as the technical ones.
Think long-term. While AI is transformative, its real potential unfolds over time. Keep your focus on sustainable growth and continuous improvement.
Prepare for quantum computing. As the next wave of technology looms, businesses must stay adaptable and curious.

The age of AI is here, but its full impact will take time to unfold. By focusing on practical strategies and building a solid foundation, businesses can not only survive but thrive in this exciting new era. Embrace the challenge, stay curious, and get ready to dance with the robots - you might just surprise yourself.

Source: "AI isn’t magic, it’s math w/ Danielle Crop" - ServiceNow Community, YouTube, Dec 3, 2025 - https://www.youtube.com/watch?v=TddXMY4t5Nw