Executives keep declaring that AI is the future. Reports show that adoption is rising. Yet only a tiny fraction of enterprises ever reach real impact. The numbers look impressive until you ask why so many AI pilots stall before they scale.
In our conversation with Shubham Gupta, CTO at ToolJet, he argued that the real barrier is not model quality or governance. It is the quiet weight of operational debt that sits inside every enterprise. Most critical workflows still run on spreadsheets, disconnected files, and email threads. Gupta put it bluntly that "Our biggest competitor is Excel."
Once you see the problem through that lens, a sharper question appears. If internal operations remain stuck in manual systems, what does it take for an enterprise to adopt AI native internal tools at scale?
This article brings together industry research and Gupta’s firsthand experience to answer that question. It explores the obstacles slowing internal AI adoption and the role of AI native internal tools in removing those obstacles. It then lays out the five-phase roadmap Gupta believes leaders can use to achieve real progress rather than more pilots.
What the industry currently gets wrong about internal AI adoption
IBM reported that enterprise-wide AI adoption stalls due to poor model customization, complex infrastructure, and regulatory requirements. Gupta argues that the real reasons for internal AI adoption failure are legacy systems, fragmented processes, and deprioritization of internal tool development.
For example, team approvals for new product launches still move through email threads. Employees cannot act on data buried in unstructured processes, and the workflows that AI should automate still exist in spreadsheets and email chains.
The industry sees AI sophistication as the main hurdle, but Gupta insists that the true limitation to internal AI adoption is the lack of software.
"Companies with large operational requirements need software for employees to do their work more efficiently," said Gupta. "Imagine there are about 5,000 employees, in the absence of proper internal software, chaos will ensue," he added.
Relegation of internal tools is another adoption obstacle that industry reports often overlook, and Gupta says most enterprises have yet to overcome. With engineers working on customer-facing assets, internal tool development often takes a back seat.
"Engineering teams are always juggling between delivering consumer-facing products and prioritizing these internal tools, which ultimately causes a lot of operational inefficiency," Gupta shared.
To effectively adopt AI internally, organizations must update their manual practices and increase development time for internal software using low-code platforms like ToolJet.
Why internal tools are the natural first home for AI
Analysts often point out that AI succeeds fastest in environments where work follows a repeatable pattern. Gupta sees the same reality inside enterprises.
“Internal tools are repetitive and do not require a lot of contextual thinking, which makes AI well placed to build and manage them."
Employee sentiments also support this direction. A survey by Talker Research revealed that 62% of employees want to use AI in their roles to sort data in spreadsheets. Routine tasks such as data sorting, CRUD operations, approval chains, and inventory tracking are examples of workflows that follow a fixed pattern. They are the logical starting point to introduce AI-powered internal tools because they generate compounding value quickly and provide a deterministic process that enables AI algorithms to function reliably.
The industry view and Gupta’s experience point toward the same starting point for AI adoption. Enterprises waste momentum when they try to automate strategic or ambiguous work first. Gupta suggests focusing on the internal tools that already run on predictable workflows because these are the environments where AI produces reliable gains and compounds value quickly.
The determinism challenge most AI tool builders ignore
Determinism in AI-native internal tools
Enterprises fear AI's inherently non-deterministic nature. Executives want consistent and auditable outcomes from the AI tools they adopt within their organizations because AI unpredictability triggers mistrust, poses compliance issues, and can harm decision-making.
For example, a single hallucinated permission can expose an entire customer dataset. Analysts focus on establishing evals and guardrails, but the industry still lacks a shared standard for how they should work.
Gupta says the solution is an architecture with three interconnected layers, as illustrated in the image above: a multi-agent structure, an eval system, and a human-in-the-loop process. Determinism increases when enterprises break automation into smaller, single-responsibility agents.
"If you spread every AI action into multiple single responsibility prompts, the agent's output quality improves," he said.
The second layer, an eval system, serves as a feedback loop that ensures behavioral consistency, security, and reliability.
"It puts guardrails in place, so when AI responses fall below a certain standard, organizations can re-loop their agents," Gupta shared.
The final layer, human checkpoints, is what locks the architecture into place.
"No AI action on data should be performed automatically. A human should always authorize and test those actions in a development staging environment before they go to a production environment," Gupta highlighted.
This approach to achieving predictable AI outcomes should serve as a framework for enterprises that want to build trustworthy AI-powered internal tools. But tackling non-determinism is only one part of mitigating the vulnerabilities that can affect an organization's trust in AI tools.
Enterprise architecture is the real battlefield
The hardest part of AI adoption isn't the model, but the architecture that surrounds it and the expectations enterprise teams refuse to compromise.
Enterprises want:
- Data control for proprietary data and isolated tools that span different departments
- Governance to ensure a tool works transparently and meets regulatory requirements
- Predictable Software Development Life Cycle (SDLC) for engineering teams that are resistant to disruptive workflows
- Integration into existing systems to accommodate tailored workflows
Analysts believe AI tools will replace traditional workflows within enterprises. Rather than outright replacement, Gupta contends that internal AI tooling must respect an organization's existing architecture, protect that foundation, and extend the system when it requires further customization.
In practice, the right internal AI tools offer self-hosting options, provide flexible SSO, and support extensibility through code, plugins, or third-party integrations.
"You can self-host ToolJet internally inside your VPN or deploy it in your existing cloud services to make sure that none of your sensitive data escapes or reaches our servers. The data stays in your own cloud or database, so you can completely air-gap ToolJet inside your network," Gupta noted.
Opinionated platforms lock organizations into vendor-controlled environments, which risks data exposure, while enterprise-friendly AI tools give organizations full control over where their data resides.
Internal tools often span departments, and each team might build its own application while still requiring cross-departmental coordination. Enterprises want unified access control, and a strong Single Sign-On (SSO) provides secure access to the tools.
"Whatever SSO a company supports is supported by ToolJet. Whether they prefer multiple SSOs for multiple departmental tools or a single SSO to rule them all, that also works with ToolJet," said Gupta.
Opinionated platforms typically require organizations to conform to their specific authentication protocols, while adaptive AI tools integrate with multiple identity providers.
Respecting engineering workflows is another priority for internal development teams that have established processes. Gupta explained,
"Engineering teams are very picky with tools because they have a certain way of working and they don't enjoy people messing with it."
Opinionated platforms dictate a new SDLC model, but flexible AI platforms like ToolJet allow engineers to follow their own SDLC while the platform adopts it. Enterprise AI tooling succeeds when platforms adapt to an organization’s constraints, rather than fighting them.
Understanding the real obstacles to internal AI adoption is an important first step, but enterprises also need an action plan that actually drives adoption.
The roadmap to adopting AI native internal tools
Leadership wants results with AI, but often without a clear strategy or a full picture of how to achieve it. Gupta provided a phased approach for business leaders to know where to begin the adoption process and how to scale it across their organization.
Roadmap to adopting AI native internal tools
Phase 1: Audit where manual work still lives
Industry reports often advise executives to conduct an AI readiness assessment that evaluates whether their organization is ready to integrate and adopt AI. But this assessment is a broad and ongoing process.
Instead, Gupta recommends that executives revisit the manual, high-friction, and data-heavy parts of their existing workflows, then redesign them so AI becomes a natural part of the lifecycle.
"Check how many people are handling a bunch of Excel sheets overflowing with customer records or transactional data in your company," he said.
Engage with teams across product, engineering, operations, or sales departments to identify bottlenecks. For example, customer success teams may be losing track of approval requests buried in emails.
Gupta suggested this is how to spot inefficiencies and opportunities for automation. Transforming this manual process into an internal software will ensure that every request has timestamps and owners attached. Teams still make the higher-level decisions, but with an AI-powered internal tool, they start benefitting from automated routine tasks and smoother feedback.
Phase 2: Ship the first AI-generated internal app fast
Analysts say business leaders should define Key Performance Indicators (KPIs) before introducing an internal AI tool. While KPIs outline business objectives, they can create an illusion of clarity. Instead, Gupta says executives should choose one painful workflow and build a solution fast using platforms like ToolJet, which cut time-to-app through natural-language app creation.
The real success metric to track is "shipping a working internal app in one to two weeks, depending on the use case. Then, iterate based on feedback," he said. When employees experience optimized workflows and regain time to focus on higher-value work, internal tool adoption gains momentum.
Phase 3: Create a repeatable build system
Industry experts debate which build system is superior between AI, low-code, and pro-code development. But Gupta says executives must approach their decision as a development spectrum.
"Some enterprises need AI-powered development with a prompt-specific approach for rapid prototyping. Others want low-code solutions, where non-technical teams can drag-and-drop and customize pre-built components in a visual builder. Engineering teams may want to customize features even further and add custom connectors for specific functionalities using pro-code development," he said.
A practical approach is to reduce development backlogs and tool build time using AI and low-code platforms, then leverage the flexibility and precision of pro-code for custom workflows and complex features.
Phase 4: Harden with governance and access control
Reports show that the timeline for implementing and scaling AI governance in an enterprise can take 6 to 18 months. Gupta suggests a different approach: build trust into the tool from day one through determinism and access control. Start with Role-Based Access Control (RBAC) for tools that handle significant business processes, manage sensitive data, and report critical information. IT teams should limit users' permissions to only what is necessary to perform their roles.
"If a specific team doesn't need to have access to a tool, they shouldn't have access to it because the principle of least privilege and regulatory adherence is very critical to large enterprises," said Gupta.
Also, implement SSO early with strong encryption to protect authentication data. Above all, maintain human ownership throughout the entire lifecycle of developing and scaling AI internal tools. Human employees should validate decisions and provide ethical oversight, while AI apps or agents handle the operational load and execution.
Phase 5: Scale through internal evangelism
Industry reports often attribute poor internal tool adoption to governance. But Gupta challenges this view, arguing that scaling is a value-proof problem. When teams can see a tool's value quickly, adoption spreads naturally, and scaling becomes feasible.
Gupta encouraged executives to "regularly share wins and updates on a successful internal tool to build trust, and create internal programs like Hackathons which reward employees who actively build tools that contribute to enterprise-wide integration."
A McKinsey survey reinforces Gupta's suggestions, stating that "employees believe access to AI tools in betas or pilots, along with incentives such as financial rewards and recognition, will accelerate AI adoption." A successful pilot within one team will encourage a culture of experimentation, where employees feel empowered to explore AI solutions across organizational processes.
Gupta shared that the ToolJet team uses its own platform internally across different business functions, achieving productivity gains and reducing development costs.
"We used ToolJet to build an internal CRM that handles licensing keys creation, payments, refunds, and recurring invoices for our customers. This used to take a lot of our manual effort, and if we had to build this product from scratch, it would have taken us another two months. But with ToolJet, we were able to launch the product faster, and our dev team spends more time on actual customer-facing products."
He also shared that the team built their release notes engine on ToolJet in one week.
"We evaluated external software, but it was costing us upwards of $1,000 a month. So we decided to build internally using ToolJet, and it took us a week to spin it off. And now we are completely relying on our internal software to manage and publish release notes."
Enterprises that build internal AI tools early for high-friction tasks will gain faster data analysis, reduced manual error, clearer communication, improved IT support, and productive employees who can focus on more strategic tasks.
What it means to build an AI native internal tools strategy
Organizations that want to be ready for an AI-driven future must start with their internal systems. The roadmap presented in this article provides executives and team leads with concrete steps towards building an AI-native internal tooling network that puts them at an advantage to win at AI.
It doesn't require a radical company-wide overhaul. The key is building targeted internal tools that automate mechanical, multi-step work to compound wins quickly. Then, teams can iteratively expand those tools into well-scoped tasks while investing in guardrails.
The leaders who successfully implement this roadmap will move from pilots to full-scale impact.
Top comments (0)