The Best AI Strategy: Combine Speed, Scale, and Control

7 minute read | 18 Dec 2023

By Thomas Lai

Enterprises today have bold goals. They want to continue the momentum of their digital transformation. That means applying data and automation in innovative ways to make back-office operations more efficient. It also means applying data and automation to create new customer experiences that wow customers and streamline their engagements with the organization.

Enterprises also want to take advantage of AI. They know it’s the next rocket fuel for growth, provided they handle it carefully. Harness AI correctly, and manual processes become automated and efficient. Questions are answered in mere seconds, avoiding days or weeks of back and forth with various team members. Use AI correctly for analysis, and business users and others gain an unprecedented understanding of operations, risks, and opportunities — all delivered fast enough to be acted upon.

Every Good AI Outcomes Depends on Good Data

Data is the foundation for all this opportunity and growth. Building transformative solutions based on AI actually requires two steps: finding, connecting, and managing the data to feed AI solutions, and crafting and managing the AI solutions themselves. A wide variety of tools are available for both these steps. If either is given short shrift, the benefits of the AI solution for digital transformation will suffer.

For example, let’s say that a company builds an AI solution monitoring its sales operations, but working only from the finance system that records closed deals. That analysis will obviously miss all the data from deals in progress that might be recorded in Salesforce or some other CRM. Work only with CRM data, though, and the solution still might be missing valuable data about prospect and customer interactions on the website.

The most meaningful AI results come from having all the right data at the right time at the right quality level so that the large language model (LLM) being used learns from accurate data. A successful AI strategy begins with a successful integration and data management strategy.

But even collecting all the right data doesn’t guarantee success with AI. Businesses need AI solutions that analyze that data accurately and productively, delivering meaningful results free from bias and compliant with all relevant company and industry policies for data privacy and data security. (This is where Responsible AI comes in.)

What’s the Best Strategy for Enterprise-wide AI?

How should they manage their data and their investments in AI? AI hype is at megaphone levels these days. Businesses have a variety of choices. They can:

  • Put AI projects solely under the guidance of an IT center of excellence, tightly controlling which data is accessed, which models are applied, and which use cases are addressed.
  • They can take advantage of easy-to-use “Citizen Developer” tools and encourage tech-savvy users across the organization to build the connections and automations they need to leverage AI for their areas of expertise.

There are risks with both these approaches. Tightly controlling access to tools will likely mean missing out on line-of-business innovations that could improve productivity and potentially improve customer experiences. On the other hand, giving departments free access to integration, data management, automation, and AI tools might lead to chaos, dead-end projects, and even security and compliance risks.

Guidelines for Balancing Democratization and Centralized Control of AI

Here are some guidelines for navigating these choices. These guidelines rest upon a few stark truths and assumptions.

In the stark truth category: in most organizations, about half of all data is “dark,” undiscovered, unmanaged, and hence useless for digital transformation. (You can’t optimize a customer experience using data you don’t have access to.)

Another stark truth: data privacy and data security regulations are increasing, not going away. Whatever strategy you adopt for leveraging AI, you need to keep in mind that connecting to new data sources, data platforms, and public tools such as ChatGPT only increases your exposure to data breaches and compliance violations.

There are stark truths on the plus side, too. AI has already proven its mettle at making predictions, generating useful text and images, and accelerating workflows. Ignoring AI or hobbling its use is the equivalent of insisting on horse travel in the age of jet planes.


  1. Find the data you need, and find a quick scalable way of connecting and managing it.
    Data is still the new oil. AI solutions depend on it, and so does BI and a long list of other applications. It only makes sense to invest in ways to find and manage data effectively,  leveraging best-in-class integration and automation. Scale matters, because every part of your organization needs good data now. For your shopping list: a platform for integrating and managing data everywhere at scale in a quick, cost-effective way.
  2. Encourage employee innovation, but make sure that democratized innovation is secure, compliant, and replicable.
    Give departments tools for innovating. Make this the era of the Citizen Developer. But when a department has a genius idea, make sure it’s replicable and scalable. In other words, give citizen developers and other non-IT users tools that deliver solutions that can prove themselves as solid IT projects that can be shared, replicated, and scaled. Conversely, don’t give departments tools that start quickly but hit walls at scalability or prove risky for security and compliance. Why invest in building rickety step ladders when your company really needs secure, reliable elevators? Choose technologies that can realize the full potential of that great idea from any Citizen Developer or tech-savvy user.
  3. Embrace responsible AI from beginning to end.
    AI holds tremendous potential. For example, by anticipating customer demands, it can power customer experiences that are uncanny in their accuracy and usefulness. It can also help support teams respond more quickly and effectively than ever before and help line-of-business leaders understand risks and opportunities better than ever before. It can also lead to accusations of bias, data misuse, and regulatory violations. Make sure that whatever technology you’re using to explore AI and build new AI-powered business solutions gives you the insight and control to make sure your innovations never compromise your organization’s mission and ethical standing.

AI is the biggest, most far-reaching innovation to hit business technology in many years. It’s natural for IT and business leaders to feel both excitement and trepidation at all AI offers for their organizations. By adopting these guidelines for connecting and managing data, encouraging compliant democratized innovation, and embracing responsibility and control at every step of your AI journey, you and your organization can minimize the risks of AI while reaping its greatest possible benefits.

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