AI Readiness: The DOs and DON'Ts That Determine Success

The groundwork organizations skip—and the price they pay

TL;DR

Most AI initiatives don't fail because of the technology. They fail because organizations skip the readiness work. The companies getting real value from AI picked the right use cases, cleaned up their data, fixed broken processes, built guardrails, and measured results from day one. Readiness, not the AI technology, is what separates the winners.

AI is powerful, but only if you’re ready for it

AI is reshaping how businesses operate, and organizations are investing accordingly. Yet most AI initiatives still fall short of delivering the value they promised. Some quietly stall. Others get canceled outright. A few become public cautionary tales.

The reason usually isn't the technology. As TechTarget put it, "AI can optimize the wrong thing just as easily as the right one." That insight applies far beyond customer service. AI isn't a magic wand. It's an amplifier. And if what you're amplifying is a chaotic process, fragmented data, or unclear objectives, AI will just help you make mistakes faster.

Organizations that are getting real value from AI aren’t necessarily the ones with the best tools or the biggest budgets. They're the ones that did the thoughtful prep work first.

For a practical look at what that value looks like across the business, our companion guide, 10 Ways AI Helps You Build What Your Business Needs, walks through ten real-world applications where organizations are seeing the biggest impact."

This guide is for business leaders, IT teams, technology advisors, and anyone steering their organization toward AI. We'll walk through the conditions that make AI projects succeed, with clear DOs and DON'Ts you can act on across the business.

The organizations that win won't be the ones that deploy the most AI. They'll be the ones that deploy it thoughtfully, with clear goals, solid data, optimized processes, governance, human oversight, and readiness at the center of every decision.

Why AI projects struggle, and what differentiates the ones that succeed

The data tells the story. Here's what's happening across the AI landscape right now.

AI deployments aren’t keeping up with customer expectations

The AI customer experience bar is rising faster than most organizations are clearing it. AI is being asked to do more, in front of more demanding audiences, with less margin for error. According to Zendesk, 83% of consumers think experiences should be better than they are today, even with AI in the mix. Three in four now expect customer service to be available 24/7, and 88% expect faster response times than they did just a year ago.

A lot of AI projects don't make it to production

According to Gartner's June 2025 forecast, more than 40% of agentic AI projects will be canceled by the end of 2027, due to escalating costs, unclear business value, or inadequate risk controls.

The story isn't that AI doesn't work. It's that too many organizations are deploying it without a clear strategy, without understanding the complexity, and without the governance to manage what happens when something goes wrong.

Many AI projects that launch fall short of their ROI targets

AI ROI often falls short when organizations expect too much, too fast. A Gartner survey of 782 I&O leaders, published April 2026, found that only 28% of AI use cases in infrastructure and operations fully succeed and meet ROI expectations, while 20% fail outright. Among the 57% who reported at least one failure, many pointed to the same issue: expectations that outpaced reality.

Expecting AI to immediately automate complex tasks, cut costs across the board, or fix long-standing operational issues is less a strategy than a shortcut to missed expectations.

Why many AI investments aren’t paying off yet

The MIT NANDA State of AI in Business 2025 study found that 95% of organizations deploying AI saw zero measurable return on their investments.

The researchers were clear about why: the core issue isn't talent, infrastructure, or regulation. It's the lack of learning, integration, and contextual adaptation, especially when AI tools don't connect to real workflows or adapt to how the business actually operates.

Notably, the same study found that purchasing AI tools from specialized vendors and building partnerships succeed about 67% of the time, while internal builds succeed only one-third as often.

The failure isn't AI itself. It's how organizations are approaching it, and that's entirely fixable.

Companies are stepping back from AI initiatives that aren’t delivering

The retreating companies aren't proving AI doesn't work. They're proving that AI without preparation doesn’t.

According to S&P Global Market Intelligence, 42% of companies abandoned most of their AI initiatives in 2025, up sharply from just 17% the year before.

That's a sharp jump, and it signals that the early-experimentation phase is ending. The next phase will belong to those who get the foundations right.

A small group of high performers is demonstrating what good AI looks like

According to McKinsey's State of AI 2025 report, real enterprise-wide bottom-line impact from AI is still rare. Only about 6% of organizations (the AI high performers) are pushing for transformative innovation, redesigning workflows, scaling faster, and investing more. And they're nearly 3x more likely than their peers to have fundamentally redesigned how work gets done as part of their AI efforts.

The winners aren't winning because they have better AI. They're winning because they did the readiness work first, especially around workflows, data, and integration.

The good news? The path forward isn't a mystery. High performers aren’t relying on fancier models. They’re relying on strategic planning, solid data, well-designed processes, disciplined execution, and a clear-eyed approach to risk and change.

Here's where most organizations get tripped up, and how to avoid it.

The DON'Ts of AI deployment for business

Before diving into what to do, let's look at what not to do. Many organizations stumble because they treat AI like a software deployment, ignoring its unique risks and requirements.

Don't treat AI like just another technology project

Seeing AI as an isolated technical solution to be “plugged in” overlooks its widespread impact on processes, people, and organizational structure. AI deployments that don’t consider business strategy and cross-functional collaboration from the ground up struggle to gain adoption or deliver meaningful value.

Don't deploy customer-facing AI without robust planning, guardrails, and clear escalation paths

The allure of automating customer interactions is strong, but premature deployment without proper controls can lead to highly visible public embarrassments your brand won’t easily live down. AI systems, especially generative models, can hallucinate, make erroneous commitments, or adopt inappropriate tones if not explicitly constrained and supervised.

Here are just a couple of real examples of what can happen when proper guardrails are missing:

• After a chatbot invented a refund policy that didn’t exist, a tribunal ruled that the business was responsible for what its AI told customers.

• A car dealership's website chatbot was talked into agreeing to sell a vehicle for one dollar, an event that went viral within the hour.

The fix to issues like these requires input validation, output filtering, tone control, and human oversight before anything goes live.

Forrester notes that AI success in customer service operations also requires solving deeper issues, like simplifying tech stacks, consolidating vendors, improving data quality, optimizing knowledge bases, and fostering adaptable cultures to manage this complexity.

Don't skip the data readiness work

AI large language models (LLMs) are only as good as the data they're trained on and operate with. Rushing into AI initiatives without first ensuring data quality, accessibility, and governance is a recipe for biased outcomes, inaccurate predictions, and unreliable automation.

For example, an HR department attempting to screen resumes using AI might find their system exhibiting bias if the historical data used for training reflects past hiring prejudices. Similarly, a supply chain optimization AI built on fragmented, inconsistent, or stale inventory data will likely generate flawed recommendations, leading to stockouts or overstock.

Don’t fall off the automation cliff by expecting too much, too fast

Driven by hype, organizations can easily fall into the trap of expecting AI to instantly automate complex tasks or fix long-standing operational inefficiencies without significant upfront effort.

For example, a finance team might implement an AI for anomaly detection in transactions, expecting it to eliminate all manual fraud review from day one. But if the AI generates a high volume of false positives or misses subtle new fraud patterns, the team may well become disillusioned and abandon the tool, concluding AI doesn't work for their needs.

Don't set it and forget it

AI models are dynamic. Their performance can degrade over time (model drift) as real-world data evolves or as the system encounters novel scenarios it didn't see during training. Failing to continuously monitor, retrain, and update AI systems ensures they’ll fall behind, or worse, start working against you.

For example, a fraud detection model trained in 2024 won't catch the fraud patterns of 2026 unless it's continuously retrained on new data.

Key takeaway

The good news: none of these pitfalls are about the technology itself. They're about preparation, process, and discipline, all things within your control. Recognize them early, and the same readiness work that prevents the pitfalls will unlock the upside.

The DOs of getting your organization ready for AI success

Avoiding pitfalls is only half the battle. Real AI success comes from proactive planning, the right foundations, and an AI strategy that's responsible and built to last.

According to the McKinsey 2025 State of AI Global Survey, 80% of respondents said their companies set efficiency as an objective for their AI initiatives, but the high performers who experience the most value also set growth and innovation as additional objectives.

Start with the problem, not the technology

Successful AI deployments begin with clearly defined business problems, not a desire to use AI because everyone else is. Prioritize use cases that align with business goals, have measurable outcomes, and give AI a clear role to play.

Instead of asking, "Where can we use AI?" ask, "Where are our biggest bottlenecks, and how could AI help in measurable ways?" For a legal team, using AI to flag clauses and discrepancies in contracts offers a clearer path to value than a vague exploration of "AI in legal."

Make sure your data house is AI-ready

Data is the fuel for AI. Your LLMs will only perform as well as the data they consume. Focus on data quality, accessibility, integration across systems, and governance before scaling anything up.

What this looks like in practice: An e-commerce company planning AI-driven recommendations may discover that customer purchase history is fragmented across systems, full of duplicates, and inconsistently tagged. Before building a model, the team needs to clean and consolidate the data, define ownership and quality standards, and put ongoing data hygiene in place.

Design the process and optimize workflows and journeys first

As noted earlier, AI won't fix a broken process. It will just scale the dysfunction. Before applying AI, map out existing workflows to identify bottlenecks, unclear handoffs, and upstream issues, then optimize them manually. Only once you have an efficient, well-understood process should AI be introduced to amplify it, rather than automate dysfunction. The same principle applies to customer journeys, internal operations, and product development cycles.

For example, a manufacturing firm with frequent production delays might assume AI predictive maintenance is the answer. But a closer look may reveal the real culprits are inefficient material handling and poor cross-team communication. Fix those first, then layer in AI where it can enhance a healthier system instead of patching a chaotic one.

Build responsible AI from day one, with robust governance and guardrails

AI introduces unique ethical, compliance, and security risks, so establishing governance frameworks, ethical guidelines, and security protocols before deployment is non-negotiable. This means defining accountability for AI decisions, managing data privacy, mitigating bias, and ensuring transparency. For customer-facing systems, layer in guardrails, input validation, and human-in-the-loop oversight.

In practice, a financial institution using AI for loan review would involve legal, compliance, ethics, data science, and business leaders early. It would anonymize data, audit for bias, require human review of high-impact decisions, and harden cybersecurity around models and data.

Empower your people by investing in change management and AI literacy

AI is as much about people as it is about technology. Successful adoption depends on preparing your workforce for new ways of working, building AI into daily routines, and growing a culture of AI literacy. That takes clear communication, real training, and honest conversations about how roles will evolve. Engage employees early, show them how AI helps with their actual work, and give them the skills to use it well.

What this looks like in practice: When rolling out an AI assistant for project managers, explain the why, not just the how. The goal is to reduce admin burden, not replace PMs. Train people to evaluate AI output critically, write strong prompts, and blend AI insights with their own judgment. Designate "AI champions" to support colleagues and gather feedback.

Define KPIs and measure continuously: Test, monitor, and iterate for value

Set clear KPIs tied to business goals before deployment, monitor performance and model drift, and gather user feedback as you go. Be ready to refine, iterate, or sunset what isn't working. AI is never a set-and-forget system; it needs ongoing tuning to stay aligned with your strategy.

An example of what this means in practice: A marketing team using AI for email personalization sets KPIs upfront, such as open rates, click-through, and conversion, then A/B tests AI-generated variations. They monitor results and adjust prompts, models, or approach based on the data. If something underperforms, they refine it, swap it out, or revert to a human-led approach.

Our companion blog, 10 Ways AI Helps You Build What Your Business Needs, is a mine of valuable examples of how organizations can deploy AI strategically.

A case in point: AppDirect’s large-scale AI engineering transformation

In his blog, How to Adopt AI Development at Scale: AppDirect’s Leap from 0% to Over 90% AI-generated Code, AppDirect’s SVP of Engineering and Technology, Mathew Spolin, provided a prime example of the thinking, planning, hurdle-jumping, guardrail-building, and teamwork that was involved in a major AI project his team led for the company.

It’s a story of how his global engineering organization with hundreds of engineers adopted AI development tools—not as a pilot, nor as an experiment, but as the way the company now builds software.

To quote Spolin, “The hard part wasn't the technology. It was the organizational change. The metrics. The process evolution. The mindset shift.”

Post launch: What AI value actually looks like

Readiness isn't the destination, it's the launchpad. Our companion guide, 10 Ways AI Helps You Build What Your Business Needs, explores the full range of what becomes possible once the foundations are in place.

Organizations that put the foundations in place are the ones positioned to capture AI's real upside, often faster than they expected. Here's what that looks like in practice.

Routine work gets done faster

Conversational AI, intelligent document processing, and workflow automation take repetitive tasks off employees' plates. Invoices get processed without manual entry. Meeting notes get summarized automatically. Customer inquiries get answered instantly. Teams spend less time on administrative friction and more time on the work that actually requires human judgment.

Customer experiences get more efficient, more consistent, and more personal

AI-powered interactions resolve common issues in seconds rather than minutes, surface the right answer at the right moment, and tailor recommendations to individual behavior. Conversational AI alone reduces average handling time and significantly increases first-time resolution rates. Customers spend less time waiting and more time getting what they came for.

Cost-to-serve drops without sacrificing quality

Traditional live-agent interactions can cost $3.50 to $6.00 each. Well-implemented AI agents can deliver comparable outcomes for under $0.40, and they scale instantly during demand spikes without overtime or hiring lead times.

Decisions get sharper

AI surfaces patterns in customer behavior, operations, and market dynamics that humans can't catch in real time. Marketing teams personalize at scale (McKinsey research shows this can lift revenue by 15% to 20%). Finance teams forecast more accurately. Supply chains anticipate disruptions before they hit.

For a deeper look at how to identify high-impact use cases, build a phased roadmap, and choose the right AI partner, read our companion article, Fast-Tracking AI Value: Strategies for Immediate Impact.

The bottom line: AI is an amplifier, not a replacement

AI is one of the most powerful tools available to improve how your business operates and serves customers. Experiment, explore, and get hands-on with the technology.

But when AI starts interacting with the people who matter most to your business, customers and employees alike, intentional planning is essential. The organizations that win won't be the ones that deploy the most AI. They'll be the ones that deploy it thoughtfully, with clear goals, solid data, optimized processes, governance, human oversight, and readiness at the center of every decision.

AI works best as an amplifier of a healthy system, not as a substitute for building one.

Essential reading on AI readiness

For more about specific aspects of AI readiness and deployment, explore these resources:

• Fast-tracking AI value: strategies for immediate impact

• 10 Ways AI Helps You Build What Your Business Needs

• How to build purpose-built AI agents that actually solve business problems

• Building a human-oriented AI strategy: elevating workplace interactions with AI companions

• The 4 keys of AI innovation—A Blueprint for AI-driven IT