AI Growth Depends on Workflow Proof, Not More Demos

By Daniel Okafor

Gartner’s July 2026 forecast projected global software spending of $1.468 trillion in 2026, up 15.5% year over year. IDC separately projected AI infrastructure spending of approximately $497 billion in 2026, growing about 56% annually and surpassing $1 trillion in 2029.

Manish Bhardia, Think AI’s Managing Partner, framed the commercial risk this way: “Disconnect is where people are not clear on what we are trying to get out of AI,” he said.

Entrepreneur contributors use those forecasts to frame a substantial spending opportunity while noting that they describe different markets. Their warning is practical: buying AI access is straightforward, but placing AI in a business requires thought. A polished demo does not remove the operational work that follows in an inbox or team queue. The article also notes that opinions expressed by Entrepreneur contributors are their own.

Leaders from Queue Associates, Envision IT and Think AI have explored how technology partners build, adapt and grow. Their shared question is how to turn AI interest into software customers use, value and keep buying. The contributors start with routine work: updating a logistics order, managing external collaborators and choosing which tasks to automate.

What do the spending forecasts mean for software vendors?

The article says Microsoft’s Partner Program can help small and medium-sized software development leaders pursue broader markets through Microsoft’s cloud, marketplace and AI solutions. Entrepreneur contributors say a bigger market creates room to compete, while a clear business case gives customers a reason to choose a vendor.

For founders, the article recommends replacing feature-heavy sales presentations with questions about the customer’s operations:

• Where does work slow down?
• Which tasks require repeated manual updates?
• What would a meaningful improvement look like?

The contributors frame implementation as a sequence. Vendors first identify a problem, then fit the solution into daily operations and prove that the improvement justifies the investment.

What did Think AI’s logistics deployment prove?

Manish Bhardia described a 300-person logistics company where approximately 100 employees manually tracked orders and updated customer relationship management records using CultureBanx. Think AI introduced an assistant within Outlook to summarize tracking information and update the CRM.

The reported improvement was approximately 20 to 30 seconds per task. The customer estimated roughly $1,000 in annual savings per affected employee, or about $100,000 across that group. The article labels these as customer estimates rather than independently verified financial results.

Unlike a broad AI demo, the project named an identifiable task, a defined group of employees and an improvement the customer could estimate. That specificity gave the software a measurable business case. It also connected a specific workflow to a defined customer group and a financial estimate.

When does custom software become a repeatable product?

Peter Carson’s experience offers another growth lesson: repeated requests for the same solution can expose a common pain point. Carson said: “Once you’ve built the same solution for half a dozen clients, you think, you know what, is there a common pain point here?”

Envision IT’s offerings include Extranet User Manager for external collaboration and Tenant Dashboard for Microsoft 365 management and governance. Carson described the central challenge of turning custom solutions into products that teams can repeat, support and configure for each customer.

Repeated demand can reveal a product opportunity, according to the article. The next decision depends on how much of each custom solution customers actually share. Vendors reviewing recent engagements should:

• Separate common requirements from customer-specific requests.
• Test whether a standard offering can solve the core problem.
• Establish how onboarding, configuration and support will work.
• Build a delivery model the vendor can sustain as customers multiply.

The source frames this as the path beyond selling project hours. Common infrastructure can become a product only when the vendor can deliver it repeatedly without rebuilding the solution for every client.

Why should vendors use AI internally before selling it?

Jeffrey Goldstein, Co-Founder and Managing Director of Queue Associates, said internal AI adoption prepares a company to serve customers. He explained: “The applications that we are recommending and supporting for our customers are the applications that we’re using ourselves,” and linked internal experience directly to customer engagements.

According to the article, that approach creates a learning loop. Employees encounter implementation problems, identify useful applications and carry that experience into customer projects. That operating knowledge can shape later recommendations and product development.

Goldstein advocated a gradual rollout:

• Introduce existing capabilities.
• Help customers understand what those capabilities can do.
• Develop customized agents around clearer business needs.

The sequence moves adoption from familiar tools toward purpose-built workflows. It also gives software vendors direct experience with the implementation problems they later ask customers to manage.

What will turn AI spending into software growth?

Across all three examples, the contributors’ shared mechanism is customer work first, measurable performance second and repeatable delivery third. Software development companies can win AI business, according to the article, by connecting technology to specific workflows and measurable customer outcomes.

IDC expects AI infrastructure spending to pass $1 trillion in 2029, while Gartner’s 2026 software forecast points to 15.5% annual growth. Those forecasts expand the spending pool, but the article says customer proof remains the gating factor.

The next product decision should start with a named customer task and end with evidence that the software improves it. The contributors’ growth model depends on repeatable proof, not demo polish, as AI spending moves from infrastructure budgets into day-to-day operations.