AI Readiness: Establishing A Strong Foundation for AI Success

Introduction: Why AI Readiness is a Leadership Priority

Artificial intelligence has moved quickly from experimentation to expectation.

Executives are under pressure to show progress. Boards are asking for results. Teams are testing tools at a pace that often outstrips an organization’s ability to deploy them with confidence.

What’s becoming clear is that AI readiness isn’t just a technology milestone: it’s an operational one. It’s the difference between running AI in real workflows—with control and accountability—and running disconnected AI pilots that never become durable outcomes.

The Execution Gap Between AI Ambition and Results

The execution gap is real. Many organizations struggle to realize ROI from their AI initiatives due to significant data-related barriers to true AI readiness, such as data silos, fragmentation, and organizational challenges that impact data quality, accessibility, and compliance.

Gartner research found that only 48% of AI projects make it into production, and moving from prototype to production takes about eight months on average.

Salesforce research points to one of the most common constraints: 62% of IT leaders say their organization isn’t equipped to harmonize data systems to fully leverage AI.

What Separates Durable AI Success from Stalled Initiatives

Organizations that achieve sustained value from AI tend to share a common foundation: data they can trust, clear ownership across teams, and the ability to move insights into real workflows without introducing unnecessary risk.

When those elements aren’t in place, AI initiatives struggle to progress beyond early pilots or produce outcomes leaders can confidently stand behind.

That’s why AI readiness has become a leadership concern, spanning data, analytics, technology, and execution. It requires deliberate decisions about how information flows, how decisions are made, and how AI-enabled work fits into the broader operating model well before models or tools are put into production.

What Is AI Readiness?

AI readiness is the set of organizational, data, and operating conditions that allow AI to function reliably and responsibly in real business workflows at scale.

These conditions extend beyond core technology like data platforms, integrations, and security. They include the ownership, governance, change management, and process alignment required for AI to perform consistently once it is embedded in day-to-day operations.

When those elements move together, AI becomes a practical capability that teams can rely on. When they don’t, organizations often end up with fragmented AI pilots, isolated AI tools, and “interesting demos” that never translate into sustained business outcomes.

AI readiness starts with a strong AI-ready data foundation

AI places new demands on data, especially around consistency, accessibility, and trust. IBM describes AI-ready data as data that is high-quality, well-governed, and accessible across the organization, so it can be confidently used for training, inference, and decision support.

When those conditions aren’t met, AI outputs become difficult to validate, explain, or operationalize—particularly in environments where accuracy, compliance, or customer trust matter.

The business impact is significant. Gartner estimates that poor data quality costs organizations $12.9 million per year on average—a cost that becomes more visible as AI expands into revenue operations, customer service, forecasting, and decision-making.

AI Readiness vs. AI Adoption

Many companies are actively introducing AI right now; testing copilots, trialing generative AI tools, and experimenting with analytics use cases. While this activity is important, it can also create a false sense of progress and success.

AI adoption is what happens after those tools and pilots are introduced into the business. It shows up in the day-to-day reality: do teams actually use the capability, do they trust it enough to rely on it, and does it improve how work gets done—or does it get bypassed when things get busy?

AI readiness is what determines whether adoption has a chance to stick. It reflects what must be true before scaling AI across critical workflows: trusted data, clear ownership, appropriate controls, and operational fit inside the systems where work happens.

When organizations are truly AI-ready, they are positioned for successful AI projects that deliver measurable business outcomes, thanks to strong data preparation, strategic model selection, and alignment with business goals.

A practical way to think about the difference:

• Adoption is the outcome: consistent usage, trust, and impact after rollout
• Readiness is the prerequisite: the foundation that makes those outcomes achievable

Without readiness, organizations can “implement AI” quickly but struggle to make AI dependable at scale. The result is often fragmented pilots, isolated tools, and promising demos that never translate into sustained business outcomes.

A structured approach to AI readiness reduces the gap between AI implementation and lasting operational value.

Why AI Readiness Becomes the Deciding Factor

Most leaders don’t need another reason to be excited about the potential of AI. They need confidence that AI initiatives will deliver results they can stand behind.

That confidence comes down to a few practical questions:

• Will this improve a measurable business outcome?
• Is the data reliable enough to support real decisions?
• Does it operate within governance, security, and risk tolerances?
• Does it fit naturally into the workflows teams already use?
• Will it continue to perform as conditions, data, and priorities change?

When those questions remain unanswered, AI initiatives struggle to move beyond early use. Adoption becomes inconsistent. Trust erodes. What looked promising in a pilot becomes difficult to defend at scale.

AI readiness is what closes the gap between investment and impact. It creates the conditions for AI to function as a dependable capability, not a one-off experiment. That distinction becomes especially important when AI touches customer-facing processes, regulated data, or revenue-critical decisions, where errors and uncertainty carry real consequences.

Where AI Initiatives Typically Stall

AI momentum often slows down in the same places. It usually happens after early excitement, once teams try to move from “we tested it” to “we can run this in the business.”

This is not a reflection of effort or intelligence. It is what happens when AI meets real systems, real data, real workflows, and real accountability.

1) The data isn’t trusted, or it isn’t usable

All organizations have data. Fewer have data that is consistently:

• Accurate enough for automated decisions
• Unified across systems and teams
• Accessible with the right permissions
• Governed with clear definitions

When data definitions vary across departments, even something as simple as “active customer” or “qualified lead,” AI outputs become hard to validate and even harder to operationalize.

IBM’s research reflects how common this challenge is at the leadership level. Only 26% of Chief Data Officers say they are confident their organization can use unstructured data to deliver business value.

2) Ownership is unclear, so decisions get delayed

AI initiatives touch multiple teams: IT, data, security, functional leaders, legal, and often customer-facing groups. Without clear ownership:

• Initiatives drift
• Approvals stall
• Risk decisions get avoided instead of made
• Pilots linger without a path to production

AI moves faster when there is a named business owner, a technical owner, and a clear decision path.

3) The use case is interesting, but it isn’t operationally viable

Some AI use cases look compelling in isolation but do not survive production constraints:

• The necessary data is not available in a usable form
• The workflow has too many exceptions
• The outcome is not measurable or actionable
• The process cannot absorb changes without disruption

A strong readiness approach does not start with what AI can do. It starts with what the business needs and what can realistically be implemented and supported.

Gartner research has cautioned that 30% of generative AI projects will be abandoned after proof of concept by the end of 2025, often because value is unclear or the foundations are not ready.

4) AI governance shows up late, after risk has already accumulated

Governance is not paperwork. It is how organizations stay in control when systems begin generating insights, recommendations, or actions.

When AI governance is considered late in the game, teams often end up backtracking to answer questions like:

• Who is accountable for AI-driven decisions?
• Where must humans review or approve outputs?
• What data is allowed, and what is off-limits?
• How will we monitor accuracy, drift, and unintended outcomes?

Those are leadership questions, and they are best answered early.

5) Teams underestimate what it takes to integrate AI into workflows

AI creates value when it shows up where work happens: CRM, service platforms, marketing systems, analytics environments, and operational tools.

If AI sits outside the day-to-day workflows, adoption becomes optional, results become inconsistent, and workarounds multiply.

The Salesforce Connectivity Report points to why this is so common. Only 28% of applications are connected on average, and 95% of IT leaders say integration issues are already impeding AI adoption.

6) Change management is treated as a nice-to-have

Even the best AI solution will underperform if teams do not trust it, do not understand it, or do not know how it changes their work.

AI readiness includes planning for:

• AI training and enablement
• AI adoption and role changes
• How feedback is captured and improvements are made
• How success will be measured for AI initiatives over time

AI is not a one-time rollout; it is a capability you build, refine, and optimize.

The good news is that many of these issues are predictable and preventable, particularly when readiness is treated as a cross-functional discipline.

The Cost of Skipping AI Readiness

When organizations move into AI initiatives without the right readiness foundation, the impact is rarely limited to “wasted tool spend.” The higher costs tend to show up later, when early pilots run into production realities, and teams are forced to backtrack.

Gartner has been direct about what drives abandonment after an AI proof of concept: poor data quality, inadequate risk controls, escalating costs, and unclear business value. When those issues surface late, organizations pay twice: first to experiment, then again to rebuild what should have been addressed upfront.

The hidden costs leaders often underestimate

Skipping AI readiness work tends to create after-effects that are harder to measure, but very real:

• Lost momentum and extended timelines as teams pause to resolve foundational gaps that were not visible during early experimentation
• Duplicate work across departments when AI use spreads informally and different teams reinvent workflows, controls, and approaches
• Erosion of trust when outputs are inconsistent, hard to explain, or difficult to validate against business reality
• Leadership credibility risk when “AI progress” is announced, but results cannot be sustained in the systems and workflows where work actually happens

Gartner research reveals that GenAI failures can cost organizations millions and damage their reputation.

Why does this risk increase as initiatives become more autonomous?

As organizations experiment with more advanced AI approaches, including agentic AI efforts, the cost of overlooking AI readiness increases.

Gartner predicts over 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 opportunity cost is one of the highest costs

Conducting AI readiness work is not “slowing down.” It is what enables speed that holds up. When readiness steps are skipped, even strong models underperform in real workflows, and trust erodes quickly.

The path forward is to strengthen the foundations that make AI dependable in the real world.

The Building Blocks That Make AI Work in the Real World

Once AI moves beyond experimentation, the bar changes. Leaders are no longer evaluating whether AI is possible. They are evaluating whether it is dependable, controllable, and worth scaling.

Organizations that make that shift tend to build a foundation that supports AI as an operating capability. The building blocks below determine whether AI becomes durable in the business.

1) Leadership alignment and outcome clarity

AI scales well when leadership aligns on what matters, what success looks like, and who owns outcomes.

A strong foundation includes:

• A measurable business outcome that leaders can validate over time
• Shared prioritization so AI efforts do not become disconnected experiments
• Clear accountability for business results and operational impact

2) Use case alignment that fits real constraints

The best early AI initiatives are not always the flashiest. They are the ones that fit the organization’s data, workflows, and capacity to operationalize.

A strong foundation includes:

• A defined decision context and a clear next action after AI output
• Known workflow constraints such as exceptions, approvals, and edge cases
• A scoped approach that can be supported without destabilizing operations

3) Data you can trust and explain

AI outputs are only as dependable as the data underneath them, including structured and unstructured data. Readiness improves when leaders create clarity around data quality and processes that stakeholders can rely on consistently.

A strong foundation includes:

• Defined sources of truth for key entities and metrics
• Consistent definitions across teams (for example, “active customer” or “qualified lead”)
• Documented data means that stakeholders interpret outputs consistently
• Known data limitations so teams understand where AI guidance is reliable and where it is not

IBM’s guidance on AI-ready data reinforces this focus on quality, governance, and accessibility as prerequisites for scalable AI.

4) Connected systems and usable data flow

AI creates value when it shows up inside real business workflows. That requires the right data to move reliably across the systems that enable and manage revenue, service, operations, and analytics.

It also requires consistent context, so teams are acting on the same version of the customer, the same definitions, and the same operational reality.

A strong foundation includes:

• Reliable data flow across core platforms through integration
• Shared customer and operational context across functions
• Repeatable integration patterns that reduce one-off workarounds
• Operational visibility into what data is used where, and why

The Salesforce Connectivity Report reinforces the importance of integration as a practical requirement for making AI usable at scale across the enterprise.

5) Technology infrastructure that can support AI in production

As AI moves from pilots to production, infrastructure decisions become operational decisions. What matters most is whether the organization can run AI workloads with predictable performance, cost, and security over time.

A strong foundation includes:

• A hybrid deployment strategy that balances cloud, private, and edge environments based on performance, cost, and latency needs.
• Capacity and cost planning for AI workloads at scale, not just experimentation
• Security, identity, and access controls that hold up across environments
• Operational monitoring for performance, drift, reliability, and cost

This is not a “cloud versus on-prem” debate. It is about building an infrastructure model that can sustain AI as a dependable capability.

6) Clear ownership and decision rights

AI initiatives span organizational boundaries. When accountability is explicit, execution speeds up, and risk is easier to manage.

A strong foundation includes:

• Named executive ownership tied to measurable outcomes
• Assigned technical ownership for delivery and production performance
• Clear decision rights for workflow changes and risk tradeoffs
• Defined escalation paths so decisions do not linger in ambiguity

7) Talent and skills to run AI as a capability

AI does not scale if understanding and ownership are trapped inside a small technical team. AI readiness includes the skills and data literacy needed to use, govern, and improve AI over time.

A strong foundation includes:

• Role-based enablement aligned to how teams will use AI outputs
• Baseline data and AI literacy across business and technical stakeholders
• A plan to address capability gaps as AI adoption expands

IBM’s research shows how leadership is responding: 81% of Chief Data Officers prioritize investments that accelerate AI capabilities, and nearly half identify advanced data skills as a top challenge.

That’s a strong signal that organizations see AI as strategic, but recognize the operational lift required to make it real.

8) Practical governance that matches risk

Governance is how leadership stays in control as AI begins shaping recommendations and decisions. It works best when it supports speed and accountability.

A strong foundation includes:

• Approved data boundaries and prohibited data use
• Human review requirements aligned to risk level
• Auditability expectations for traceability and accountability
• Monitoring and change discipline so performance holds up as conditions evolve

Gartner’s guidance on governance and data quality reinforces the importance of controls that support trustworthy decision-making at scale.

9) A path from pilot to production

An AI pilot can prove a concept. An AI production capability must prove reliability, accountability, and repeatability. That requires planning for the operational reality of AI, not just the initial build.

A strong AI foundation includes:

• Success measures that remain meaningful beyond the pilot phase
• Operational support expectations for teams that will run it
• Feedback loops to improve performance over time
• Change management that supports consistent adoption

These foundations do not require a multi-year transformation to improve. They do require focus, sequencing, and a clear view of where the organization is ready versus exposed.

How to Build AI Momentum Without Overcommitting

Once you solidify the foundation, the next challenge is sequencing. You want progress, but you also want to avoid expanding AI scope faster than the organization can support.

The most effective path forward is disciplined and outcome-driven. This path focuses on making AI usable in real workflows, with controls that hold up as adoption grows.

What this looks like in practice:

• Anchor AI initiatives to business outcomes and decision points. This keeps investment tied to measurable impact and reduces tool-first momentum that stalls later.
• Prioritize AI use cases that match current readiness. Early efforts work best when they fit the data reality, workflow constraints, and risk profile that the organization can manage today. Organizations should prioritize high-ROI use cases that address specific high-impact problems, using AI-ready data efficiently to accelerate value.
• Set guardrails early so scale does not create rework. Clear boundaries around data use, review expectations, and accountability reduce friction as initiatives expand across teams.
• Plan for adoption as part of execution. AI becomes valuable when teams use it consistently in the flow of work, with clarity on how outputs inform decisions and when human judgment stays in the loop.
• Use a structured lens to sequence what comes next. As demand grows, leaders need a consistent way to prioritize AI initiatives, identify readiness gaps, and build a realistic path from pilot to production.

How Summit Helps Organizations Become AI-Ready

At this point, it should be very clear that AI readiness is not a single initiative or checklist item.

It spans data foundations, technology and infrastructure, operating model decisions, governance, and enablement. This is also very complex to navigate, and organizations rarely have the expertise or bandwidth in-house to manage and oversee such a massive undertaking on their own.

This is where many organizations can benefit from proven guidance and expertise that connects all of these elements into one coordinated AI readiness plan. Without that connective lens, efforts tend to move forward in pieces. Progress becomes harder to sequence, and risk increases as initiatives scale.

Summit’s Data, Analytics, and AI Advisory team helps organizations evaluate readiness holistically, align AI initiatives to business outcomes, and define a practical path forward that balances speed with control.

The Value of the Summit VECTOR Framework

Our team uses the Summit VECTOR framework as a structured readiness lens to bring clarity across the dimensions that typically determine whether AI becomes a scalable operating capability.

At a high level, VECTOR helps to evaluate:

• Vision and value: How AI initiatives tie directly to measurable business outcomes
• Ethics and enablement: How trust, responsibility, and adoption are built into execution
• Compliance and control: How governance, security, and risk tolerances are applied in practice
• Technology and talent: Whether platforms, infrastructure, and skills can support AI in production
• Operations and outcomes: How AI integrates into real workflows and improves the metrics the business runs on
• Resilience and responsibility: How AI capabilities are sustained, monitored, and adapted over time

Rather than treating these as separate workstreams, VECTOR connects them into a single readiness view. That allows leaders to prioritize effectively, identify where foundational work is required, and move forward with confidence.

What leaders gain from this approach

Organizations engaging the Summit for AI readiness advisory services gain:

• A clear view of current AI readiness and material gaps
• A prioritized set of AI initiatives aligned to real constraints
• A sequenced path from AI pilot to production that supports scale, governance, and adoption

If AI is on your 2026 roadmap, now is the time to validate readiness.

Learn more about Summit’s Data, Analytics, and AI Advisory Services and connect with Summit today to align outcomes, controls, and execution into a practical path forward.