Effective AI Integration & Strategy

While these concerns are normal, artificial intelligence in enterprise is here to stay and growing rapidly. According to the 2025 AI Index Report from Stanford,

“78% of organizations reported using AI in 2024, up from 55% the year before. Meanwhile, a growing body of research confirms that AI boosts productivity and, in most cases, helps narrow skill gaps across the workforce.”

However, a new report released by MIT’s NANDA project found “that 95% of enterprise AI pilot programs fail to generate measurable financial returns, an indictment of how companies are deploying AI technologies.”

Everyone wants to integrate AI successfully, but few are certain how to accomplish it. We are convinced that success starts with a smart, robust AI integration strategy. This article zeros in on three key challenges of AI integration and how to leverage a strategy to surmount them:

1. High Costs and Absent ROI
2. Rationalizing Data
3. Getting Control of Workflows

#1 High Costs and Absent ROI

Software Customization is Expensive

Any time you’re customizing software at the enterprise level, it’s going to be expensive. Typically, in addition to software licensing fees which are often very high, there are additional expenditures for training and consulting, ongoing costs for software maintenance and support, and the outlay for the customizations themselves. In addition to the “hard” costs, there are “soft” costs that occur when managers need to act as stakeholders for the business during the requirements and testing phases.

And we’re still only talking about traditional software development, for which we have history, best practices, and expertise. Adding AI technology to the mix is unproven; which means that while all this money is being spent, businesses don’t yet have evidence that there will be a return on investment.

ROI Is Absent

A July 2025 report from the MIT Media Lab’s Project NANDA found that 95% of AI business initiatives fail to generate a positive return on investment. This is disappointing news for companies in the midst of launching AI initiatives; but the silver lining, if there is one, is that there are specific, actionable reasons why.

1. Companies are spending most of their money on sales and marketing when higher returns are realized from back office automation.
2. Generative AI has limitations. According to the report, “Most GenAI systems do not retain feedback, adapt to context, or improve over time.”
3. Companies prefer to build in house, but AI solutions from specialized external vendors succeed 67% of the time, compared to a 33% success rate with internally built tools.

To get in front of cost and ROI issues, your AI strategy needs to build a business case, supported by data from assessments done during the discovery phase, to justify the financial and operational investment in AI. It involves making detailed projections of cost savings and efficiency improvements and calculates expected ROI using data from:

• IT audits and an infrastructure review, focused on readiness for AI adoption
• Projected productivity gains analyzed against the cost of new software implementations
• The risks of missing out on business opportunities

While there is no evidence yet for positive ROI, the evidence for productivity gains associated with AI integration is clear. The Stanford Index found productivity gains of 10% to 45% when it analyzed AI impact from five major studies that included a total of 200,000 employees across multiple industries.

In your own company, employees who get 25% more work done in the same amount of time will at the very least reduce the cost of payroll with lower headcounts and employee turnover. When you take into account the extra bandwidth for creative problem solving and the higher quality of work, it’s only a matter of time before the return on investment is positive — if you’ve done the strategic planning work first.

It’s important to note that GenAI, in its current form, has intrinsic limits on its ability to increase productivity. From the Nanda Project: “It’s excellent for brainstorming and first drafts, but it doesn’t retain knowledge of client preferences or learn from previous edits. It repeats the same mistakes and requires extensive context input for each session. For high-stakes work, I need a system that accumulates knowledge and improves over time.”

#2 Rationalizing Data

Take a minute and ask yourself what AI integration needs to do for your business. We know we’ve given you a prompt with the subheading of this section, but even without that, solving an intractable, ongoing problem with accessing and using your company’s data would almost certainly be on the list. It’s a universal data management problem.

There are two things your organization’s data must do for your company to achieve its business goals:

1. Satisfy the demand for actionable information in real time
2. Enable business agility and velocity

For that to happen, you have to find every place you keep your data and rationalize (standardize) it so that you can get true control and insight. Since it’s such an enormous, and often expensive, undertaking, let’s take a few minutes to examine how you benefit from it.

Real-Time Information

More data is usually conflated with having more information, but that’s not often the case. In fact, as a company grows, and data begins to live in more and more places (silos), it’s rare for it to provide real information without a lot of extra work retrieving and interpreting it.

We have observed that success is not about collecting and providing access to as much data as possible. It’s about ensuring that the right information is available to the right people when they need it. Think just-in-time warehouse management, but for data.

AI Turns Data into Information

Information is data that can be used for a specific purpose and relevant to a specific user or audience. Problems arise in businesses when information can’t be acted on because it’s received too late to be useful, doesn’t give the whole picture, or is not accurate. Conversely, when leaders can consistently and reliably put their hands on the real-time information they need for better decision making, measurable value is created.

For instance, if you lease heavy machinery and a quick turnaround is necessary to maintain or increase profitability, you’ll need up-to-the-minute information on when a rental agreement ends, when the equipment needs to be delivered to the next location, and which staff members are available for transport duties. You’ll also need to manage paperwork details and business transactions in real time.

Your AI strategy must address your data-driven activities with an eye toward unlocking the business value that comes from being able to access accurate information in time to make profitable decisions. This will rarely happen with duplicated, siloed, unstandardized data.

Agility and Velocity

Business agility and velocity occur when companies can be first to capitalize on new opportunities and are the most able to adapt quickly to changing circumstances.

Business leaders are constantly seeking avenues for growth and expansion because the health of their company depends on it. It’s unfortunate when a new revenue stream is right under their nose and they aren’t aware of it. Sadly, it happens more often than not when you can’t get up-to-date actionable intelligence about your business operations.

When AI integration is well executed, from a strategy tailored expressly to your company’s growth goals, you’ll uncover and be able to exploit more and better opportunities to expand your presence in your target markets. It’s the payoff for the tedious work of data rationalization that must come before AI integration can achieve its potential.

#3 Getting Control of Workflows

AI integration requires carefully capturing, documenting, and integrating all business workflows. At Atigro, analyzing where AI powered software can streamline your workflows and increase operational efficiency comes during the AI SWOT analysis phase of strategy development. Workflows that are inefficient and lack clear communication channels have a laundry list of poor outcomes that Operations managers can rattle off from memory. To successfully change course, there are two non-negotiables:

1. You need a champion in the organization, with authority, who is willing to go through the pain of getting it done.
2. You need to include the people who are actually doing the jobs. They won’t be forthcoming until they have a problem, but they will respond to real life scenarios.

Where using AI is a game changer:

• Automating processes and workflows, which has the largest potential to impact the company’s financial health.
• Unearthing and resolving corner cases your workflows don’t address. Corner cases are the number and variety of processes that do not fit within your standard systems and managing them outside those systems impedes access to real-time data.
• Informing staff of any and all  movement and change in inventory and giving them trustworthy options for the next action step. AI enables these frictionless decisions through data on demand: “providing the right information to the right people at the right time, making further exploration easy, facilitating quicker, smarter decisions, and sparking innovation.”
• Onboarding and offboarding assets. In addition to monitoring the movement of inventory, AI systems can ensure that your company has an accurate, up-to-the-minute record of all of your assets, giving you full visibility across their lifecycle.

Ultimately, detailed use cases are necessary, but that comes in another phase of the project. Strategy is the first step to be taken.

Conclusion

Many of the organizational discussions about AI technologies center around automation, operational efficiency, and better decision making — as they should. At some point though, the focus needs to shift to what and why. That’s when it’s time to talk about strategy.

In this article, we’ve highlighted three big issues companies must face head-on if they are to be successful in adopting and integrating AI:

1. Avoiding cost overruns and absent ROI
2. Unlocking and harnessing valuable knowledge hidden in data silos
3. Having the capability to execute complex, multistep workflows

It’s a good place to start the conversation with your internal teams.

About Atigro
Atigro is a proven ERP transformation firm that pairs its modular augmentation capabilities with AI-native frameworks. Atigro’s experience and capabilities generate the rapid development and provisioning of new ERP functionality that meets dynamically changing business processes.

---

¹ “While early adoption showed promise, quantifying AI’s impact remained challenging until 2023, when the first wave of rigorous studies emerged. In 2024, a substantial body of empirical research established clear patterns of AI’s workplace effects across multiple domains and contexts.”