Chart showing the gap between AI adoption and AI production readiness in enterprises

Ninety-two percent of organizations reported an AI-related breach and lacked proper AI access controls. That number, from IBM’s 2026 Cost of Data Breach Report, should stop every technology leader mid-scroll. It is not a story about AI failing to deliver value. It is a story about AI outrunning the guardrails built to manage it.

Here is the paradox we see with nearly every client conversation right now: the appetite for AI has never been higher, and the ability to operationalize it has never been more strained. Thirty-four percent of companies are using AI to transform their business, according to Deloitte’s 2026 AI Report. But Forrester’s State of AI 2025 Report puts the number that successfully moves from experimentation into production at just 10-15%. Gartner goes further: half of all generative AI projects are abandoned after proof of concept, killed by poor data quality, inadequate risk controls, escalating costs, or unclear business value.

Those are not technology problems. They are readiness problems. And readiness is exactly where River Point Technology (RPT) has built its AI practice.

This post breaks down what the data says about the current state of enterprise AI, why so many initiatives stall between pilot and production, and how RPT’s AI Developer Lifecycle Platform is designed to close that gap for our clients.

The Adoption Gap Is Not About Ambition

Enterprises are not short on AI ambition. They are short on AI operating models.

Deloitte’s quarterly survey of 2,800 C-suite executives found that only 25% feel prepared to manage AI governance and risk. That gap between adoption intent and operational readiness is where most AI budgets go to die. Teams stand up a pilot, get a working agent or model in front of stakeholders, and then hit a wall: no defined access controls, no clear model service catalog, no repeatable path from a single prototype to a fleet of agents running across production environments.

Three data points from the current research make the shape of the problem specific:

Read together, these numbers describe an industry that has solved the “can we build this” question and has not solved the “can we run this safely, at scale, with a defined return” question.

Governance Is the Bottleneck, Not the Model

There is a second, related data point worth sitting with: 92% of organizations reported an AI-related breach and lacked proper AI access controls (IBM, 2026 Cost of Data Breach Report). That is not a small subset of laggards. That is nearly every organization that has deployed AI at any meaningful scale.

At the same time, 77% of surveyed companies now factor an AI solution’s country of origin into their vendor selection decision (Deloitte, 2026 AI Report), a signal that AI sovereignty and supply chain trust have moved from a compliance footnote to a board-level criterion. And 81% of leaders still say people remain essential to agentic AI, reinforcing that “right people in right positions” is not a soft HR line, it is an operating requirement for any organization deploying autonomous agents.

Put these three together and the picture is clear. AI initiatives sit at the intersection of technology, data, people, and strategy. Organizations are chasing AI capability without tying it back to defined business value, and the security, governance, and access control layers are being built after the fact instead of embedded from day one.

That is the exact problem RPT built its AI practice to solve.

How RPT Closes the Gap: The AI Developer Lifecycle Platform

RPT’s AI Developer Lifecycle Platform is built around a simple premise: agents should move from first prototype to industrial scale without the organization having to re-architect governance, security, or cost controls at every stage. The platform is structured around four pillars.

AI Readiness. Before a single agent goes into production, RPT delivers a prioritized roadmap, a reference architecture, and clearly defined AI outcomes. This is the step most of the 50% of abandoned projects skipped. If you cannot state the business value an agent is meant to produce, you cannot measure whether it worked, and the project stalls exactly where Gartner’s data says it stalls.

Unified Platform. A hybrid platform to deploy and run a fleet of agents across multiple cloud platforms, with governance and security embedded on day one rather than bolted on after a breach. This includes a model service catalog for consumers, giving business units a controlled, self-service way to access approved models instead of shadow AI spreading unchecked.

AI Prototype to Production. RPT ships the first set of agents into production with real-world guardrails already in place, closing the gap between the 34% of companies using AI to transform their business and the much smaller share that get those initiatives to a durable, governed production state.

Enablement Accelerators. Self-service onboarding for consumers, MCP and Skills artifacts, and FinOps and governance add-ons. This is where the 77% sovereignty concern and the 92% access control gap get addressed directly, with cost governance and access control built into the platform rather than treated as a separate project.

The platform is powered by IBM Bob and Watson Orchestrate, giving clients an enterprise-grade orchestration layer without requiring them to build one from scratch.

What This Looks Like for Your Organization

If your organization is sitting in that 50% that stalled after proof of concept, or in the 75% of executive teams that do not yet feel prepared to manage AI governance and risk, the path forward is not a bigger pilot. It is a defined operating model that treats readiness, governance, and cost control as part of the build, not an afterthought bolted on after the first incident.

RPT’s AI Readiness assessment is designed to answer exactly that: what outcomes are you targeting, what does your reference architecture need to support them, and where are your access control gaps today. For teams further along, our platform engineering practice extends that same governance-first approach into the unified platform layer, and our FinOps and AI cost governance service addresses the escalating-cost failure mode Gartner flags directly.

Ready to Move From Pilot to Production?

The data is consistent across four independent research firms: the gap between AI ambition and AI readiness is the single biggest reason initiatives fail to scale. Talk to an RPT engineer about an AI Readiness assessment and see how the AI Developer Lifecycle Platform can take your first agent from prototype to industrial scale, with governance, security, and cost controls built in from day one.

Kevin Hospodar leads sales and partnership strategy at River Point Technology, where he works across RPT’s HashiCorp Vault, Red Hat OpenShift, IBM co-sell, and Platform Engineering practices to bring AI initiatives from concept to measurable business outcomes. Connect with him on LinkedIn.

Kevin Hospodar is a contributing author sharing expert insights on industry strategy and modern technology.

Many infrastructure teams rely on powerful CLI tools. However these tools are often limited to experts who already understand the commands and workflows. At scale this creates a gap between capability and accessibility.

IBM Watsonx Orchestrate lets you build AI agents that can call tools through natural language. Instead of rewriting those tools, you can wrap your existing binaries as Python tools and expose them directly to Watsonx.

User -> Watsonx Agent -> Python Tool (@tool) -> CLI Binary -> Output -> Agent -> User

The Problem

At River Point Technology, we have migrated many Terraform Enterprise organizations to HCP Terraform, spanning more than 1,500 workspaces. Our solutions architects have developed a custom CLI to handle complex migration steps with copying workspaces, variables, state files, teams, and policy sets between organizations. The CLI works well, but it assumes command-line fluency. We wanted the same migration engine available through a guided, conversational workflow so more teams could run tasks safely.

How Watsonx Tools Work

Watsonx tools are Python functions decorated with @tool from the ibm_watsonx_orchestrate SDK. Tool functions are a thin wrapper that takes agent-supplied parameters, maps them to your binary’s flags and environment variables, executes the command with subprocess, and returns output for the agent to summarize. The agent reads the function and docstring to understand what the tool does and when to call it. To deploy a tool, package your Python file, dependencies, and (in our case) the compiled binary, then upload everything with the orchestrate CLI.

Prerequisites

Before you start, make sure you have:

Writing a Tool That Wraps a Binary

Here is a trimmed-down version of our skill. The full file has 25 tools covering the list, copy, lock, unlock, validate, and core migration commands.

import os

import stat

import subprocess

from pathlib import Path

from ibm_watsonx_orchestrate.agent_builder.tools import tool

BINARY = Path(__file__).parent / "skybridge"

def run(args: list[str]) -> str:

    BINARY.chmod(BINARY.stat().st_mode | stat.S_IEXEC | stat.S_IXGRP | stat.S_IXOTH)

    result = subprocess.run(

        [str(BINARY), "--json", *args],

        capture_output=True,

        text=True,

        env=os.environ.copy(),

    )

    if result.returncode != 0:

        return f"[exit {result.returncode}]\nSTDOUT: {result.stdout}\nSTDERR: {result.stderr}"

    return result.stdout or result.stderr

@tool

def skybridge_copy_workspaces(

    src_token: str,

    dst_token: str,

    src_org: str,

    dst_org: str,

    src_hostname: str = "app.terraform.io",

    dst_hostname: str = "app.terraform.io",

    dst_project_id: str = "",

    workspaces: str = "*",

) -> str:

    """Execute skybridge to copy workspaces from the source org to the

    destination org and return the results immediately.

    Pass workspaces as a comma-separated list of names (e.g. 'ws1,ws2,ws3') or leave as '*' to copy all workspaces."""

    workspace_args = ["--workspaces", workspaces] if workspaces and workspaces != "*" else []

    return run(["copy", "workspaces", *workspace_args])

A few patterns matter in production:

Building and Deploying

Deployment is two steps. First, compile the binary for Linux (the Watsonx sandbox target).

GOOS=linux GOARCH=amd64 go build -ldflags="-s -w" -o watson_skill/skybridge_package/skybridge .

Then upload the tool to Watsonx:

orchestrate tools import -k python \

  -f watson_skill/skybridge_package/skybridge_skill.py \

  -r watson_skill/requirements.txt \

  -p watson_skill/skybridge_package

The flags break down as follows:

After import completes, the tools are available to any configured agent. Watsonx picks up function signatures, docstrings, and parameter types automatically. You do not need to redesign your system to get value from AI agents. In many cases, a thin wrapper plus a well-defined interface is enough.

Using It

Once deployed, you can ask:

The agent chooses the right tool, asks for missing parameters, and runs the command.

Security Considerations

When you expose infrastructure operations through AI tools, guardrails matter:

Eight months into leading River Point Technology’s AI Center of Excellence, the framework shaping our approach predates AI entirely — and that is precisely the point.

I joined River Point Technology last September as Director of Product and Business Solutions and was recently appointed the leader of our AI Center of Excellence, RPT Labs. The question I most often field from my fellow Product peers is some version of: “Where do we begin?”

The answer I have arrived at is not a tool, a vendor, or a model. It is a system.

Organizations that fail to incorporate AI into the core of how they operate will be outpaced — not in a decade, but in the present cycle. That is not a slogan; it is the operating reality of every services and product company. The firms that figure out how to be faster, more productive, and measurably smarter — both internally and in service of their clients — will outrun those still treating AI as a side initiative.

Yet, the failure I see most often is not inaction — it is the impulse to pursue everything at once.

The Wishlist Problem

Most AI Centers of Excellence I’ve witnessed or heard from firsthand are stuck in the same pattern. A backlog of ideas. A queue of vendor demonstrations. A leader who has lost the ability to say no because every prospect or idea feels as though it could be the one. Six months in, nothing has shipped. Twelve months in, the CoE is being reorganized.

This is not a talent problem. It is a portfolio management problem. AI generates an extraordinary volume of plausible ideas absent of a system to triage them.  Every CoE becomes a scattered wishlist with no true process or direction. 

VCT: A funnel, not a roadmap

At River Point Technology, we apply the same framework to AI that we apply to our core solutions business. We call it VCT — Value Creation Technology — developed by our founder, Jeff Eiben, well before AI became the question on every executive’s desk. That timing matters. VCT was built to discipline all portfolio decisions.  We did not need to invent a new framework for AI. We only to apply the one we already trust.

VCT has three stages, and it operates as a product management funnel: Choose, Incubate, Scale.

Choose. Ideas arrive from every direction — our solution pillar leaders, our delivery engineers, our clients, the market. We require each idea to pass through the same evaluation before resources are assigned or funding is allocated. What value is created if this works? What does it cost to find out? What is the smallest version we can credibly test? What do we already possess that uniquely positions us to win? An idea that cannot answer these questions does not advance — regardless of who proposed it. Let’s also not forget AI isn’t necessarily the best answer to all problems.  Not everything is a nail, so each hurdle/gap must be critically evaluated to determine if AI is truly the right answer – not just the new “easy button.”

Incubate. Ideas that survive Choose receive small, time-boxed investments with a hypothesis, budget, and deadline. The discipline at this stage is what separates serious CoEs from the rest: a willingness to kill an incubation early when the evidence is absent, and to invest more heavily when it is present. Most CoEs skip this stage and move directly to deployment. That is how many organizations end up with tools or so called “solutions” no one uses.

Scale. Only the ideas that survive Incubate — with demonstrated value and a clear path to repeatable economics — are scaled. Scale is where the meaningful capital goes, and where the wrong investment is most costly. VCT exists to ensure that capital is deployed only against bets that have already been validated.

Two streams, one funnel

Applying VCT to AI differs from applying it to a traditional offering in one important respect: we run it across two streams in parallel. Our solution pillar leaders are divided between internal AI initiatives and external AI offerings.

Internal is the operation of RPT itself — our back-office practices, delivery workflows, knowledge management, and sales operations. Faster, more productive, sharper about our own business.

External is how we serve our clients — the agents, integrations, and AI-augmented delivery patterns that appear in client engagements, alongside the platform, policy, and infrastructure work our delivery organization is bringing to market.

These are not separate strategies. They are the same funnel applied twice. The point I would press hardest with any CoE leader is this: an organization that has not made its own business faster and smarter with AI has no standing to advise its clients to do the same. Internal credibility precedes external credibility. Be your own client zero.  The pillar split ensures investment in both, every quarter, with equal rigor.

Product management discipline, applied to a moving target

The deeper value of the VCT funnel — and this matters more in AI than in any category I have worked in — is that it imposes product management discipline on a market determined to behave like a technology fad. New models emerge weekly. New vendors arrive daily. The pull to chase is significant.

Product management has spent decades developing methods to evaluate ideas under uncertainty: funnel principles, hypothesis testing, kill criteria, stage gates. None of this is novel. What is novel is the conviction required to apply these methods to AI even as the noise outside argues for speed at any cost.

Eight months of clarity

What I have learned in my first eight months at River Point Technology is not that AI is difficult. It is that the organizations that will win at AI are the ones that resist the urge to treat AI as exceptional. Treat it as the most consequential portfolio decision your business is making — because it is — and apply the same disciplines you would apply to any other portfolio.

Choose. Incubate. Scale.

Without a funnel, you have a wishlist. And the market will not wait for you to sort it out.

If you are standing up an AI CoE — or refining one that has stalled — I welcome the conversation. DMs are open.