Business use of artificial intelligence is reaching a tipping point in 2026. It is decreasingly a future-state discussion, but already embedded in day-to-day work, most often without formal approval.
If there’s any doubt that AI has touched your organization, ask a simple question: How many people on your team have their own ChatGPT subscription that they use for work? Or worse, how many on your team are using the free version?
You’ll likely discover AI adoption is already happening; informally, inconsistently, and without governance.
This is today’s reality. AI adoption is occurring at the individual level, which is why organizations must mature quickly from AI “hobby projects” to intentional, structured deployment. Otherwise, data privacy is put at risk, and ROI from investments in AI will be hard to come by.
AI is not just “a better Google.” For AI to deliver real value, it must be tied to business strategy, sequenced against priorities, and supported by the right data, infrastructure, security, and talent. The lures of shiny object syndrome are abundant.
To help organizations bridge the gap between vision and execution, we recently hosted a webinar on How to Develop an AI-Enabled Technology Roadmap, where we extended our time-tested planning model: align, assess, and advise.
Align → Assess → Advise
This framework has helped mid-market organizations move AI out of the uneven experimentation phase and into measurable, sustainable results. Below, we walk through each step and show how to structure a roadmap that actually works.
1. Align: Connect AI and Technology Investments to Business Goals
AI initiatives rarely fail because of technology limitations. They fail because leaders were never clear or aligned on what the investment was meant to achieve in the first place.
Every effective technology roadmap – AI included – starts with business clarity. The most important question is not “What can AI do?” but “What are we trying to accomplish as a business?”
Alignment means answering questions such as:
- What is our competitive advantage?
- Do we want to be first movers, fast followers, or deliberate adopters?
- How will success be measured?
The quality of a technology roadmap is directly tied to the clarity of the business strategy. Without alignment, every AI use case will sound compelling, and every vendor pitch will feel life-or-death urgent.
Common Planning Pitfalls
One common mistake we see across our client base is skipping reflection and jumping straight to tool selection. When technology planning is left solely to technical teams, it often leads to chasing brand names, or imitating a peer, rather than identifying and solving specific business problems.
The opposite mistake is equally risky. When vision lives only at the executive level, plans often lack operational context and organizational buy-in. Implementation slows, resistance increases, and momentum fades. AI transformation is unique in how it is driven both “top down” and “bottom up.”
Effective alignment lives in the middle: grounded in strategy, informed (often built) by operations, supported by leadership, and secured by IT.
You Don’t Always Have to Innovate
Not every organization needs to be an innovator. Some intentionally choose to be fast followers or proven solution adopters. That choice is strategic, not a weakness. An organization’s risk tolerance, culture, and pace of change should shape the roadmap. There is no one-size-fits-all approach. The values and character of the organization – not a single person or consultant - should be reflected in the plan.
2. Assess: Understand Where You Are Today
Once goals are clear, the next step is assessing reality.
At Kreischer Miller, we guide organizations using our Technology Roadmap, which organizes the work into five critical domains:
- Applications
- Infrastructure
- Cyber security
- Data
- People
A thorough assessment minimizes risk and ensures AI is introduced on a stable foundation. It also identifies strengths that can be leveraged—not just gaps that need to be fixed.
Why Assessment Must Come Before AI
Without proper review, AI can increase complexity. When teams adopt different AI tools independently, organizations end up with fragmented models, inconsistent security controls, and overlapping capabilities.
Before introducing AI, organizations must understand:
- Which systems are outdated or limiting progress
- Where data is inaccessible, unreliable, or unstructured
- What new security and compliance risks AI introduces
Legacy Environments
Legacy, or “old world” environments are often defined by outdated infrastructure, security gaps, homegrown applications, and frustrated users navigating VPNs and inefficient workflows. In contrast, “new world” environments rely on modern, cloud-based systems that function like utilities: accessible, secure, scalable, integrated.
If data maturity is strong but infrastructure is outdated, modernization may need to come before AI.
3. Advise: Prioritize, Sequence, and Assign Ownership
A roadmap is where vision turns into traction. An effective AI-enabled technology roadmap translates alignment and assessment into a prioritized, sequenced plan with timelines and clear ownership.
A strong roadmap answers:
- What should we do first?
- What dependencies exist?
- How long do we expect this to take?
The roadmap itself is intentionally simple: typically, a single page spanning 12-18 months. That simplicity makes complex planning easier to communicate while remaining flexible as priorities shift.
Technology roadmaps don’t fail because of technology. They fail due to misalignment, weak change management, and distractions. It is foundational for stakeholders to regularly together and report whether initiatives are on track, off track, done or not done.
Where AI Fits into the Roadmap
An effective AI-enabled roadmap:
- Connects AI investment to business strategy
- Prioritizes infrastructure modernization where required
- Eliminates shadow AI usage
- Builds organizational literacy and confidence
- Establishes clear accountability for outcomes
The most common AI mistake is trying to scale before the organization is ready. The second most common AI mistake is being reluctant to admit what you do not know. AI cannot be a side project, it requires governance, structure, and intentional discussion so that everyone learns and changes at a similar rate.
The Human Factor in AI Adoption
AI is not an initiative you complete. It is a capability you build. No matter how powerful the tools become, adoption depends on people. Largely driven by the high volume of competition between leading AI providers (OpenAI, Microsoft, Alphabet, Anthropic), technology has evolved much faster than people can learn it.
Because AI use has already arrived informally, mature organizations must bring it out of the shadows by:
- Establishing an AI Manifesto – the organization’s stance on the technology – and acceptable use policies
- Establishing a governance committee
- Creating centralized prompt libraries or knowledge sharing platforms (e.g. a Teams Channel)
- Offering training and office hours
- Starting with small pilots before scaling up
These controlled rollouts allow organizations to learn, refine, and build momentum without overwhelming teams or introducing unnecessary risk.
What an AI-Enabled Technology Roadmap Looks Like in Practice
Rather than a single-quarter sprint, most mid-market organizations should think in terms of a multi quarter AI adoption plan that builds foundations, governance, and confidence before scaling. The speed of this will vary based on organizational complexity and resistance to change.
Here is an example:
Phase 1 (Quarter 1 Goals): Foundational Governance & Guardrails
Early priorities typically include:
- Forming an AI governance group.
A small cross-functional team (IT, security, legal/compliance, operations, and a business sponsor) responsible for oversight, standards, and prioritization. - Defining acceptable use and risk boundaries.
Establish AI usage policies, data handling rules, and security guardrails to address shadow AI usage already occurring. - Inventorying current AI usage and data readiness.
Understand where AI is already being used, which data sources are involved, and where risk exists. - Identifying high-value, low-risk use cases.
Focus on internal efficiency and decision support—not customer-facing automation yet.
The goal of Phase 1 is not deployment; it’s control, clarity, and alignment.
Phase 2 (Quarters 2 and 3 Goals): Platform Modernization and Foundational Enablement
Infrastructure and application modernization does not happen overnight, but it’s often an overlapping pre-requisite for AI to scale beyond summarizing emails.
Common initiatives during this phase include:
- Modernizing identity, access, and security controls.
Strengthening identity management, data classification, logging, and monitoring. - Improving data accessibility and quality.
Consolidating key data sources and addressing gaps that limit AI effectiveness. - Introducing approved AI platforms.
Selecting sanctioned AI tools (e.g., within Microsoft 365 or ERP platforms) to replace ungoverned usage. - Launching limited AI pilots.
Controlled pilots with a small user group (often 2–5% of employees) focused on productivity, reporting, or internal workflows. - Initial AI training and literacy efforts.
Basic training, internal office hours, and early prompt libraries.
This phase is about creating safe, repeatable conditions for AI use, not scaling broadly. Be realistic.
Phase 3 (Quarters 4-5): Expansion, Integration, and Operating Model
With foundations in place, AI can begin moving deeper into business processes.
Typical activities include:
- Expanding AI access to additional teams.
Gradual rollout based on role, readiness, and risk. - Embedding AI into core workflows.
Integrating AI capabilities into ERP, CRM, finance, operations, and reporting processes. - Formalizing the AI operating model.
Defining ownership, intake processes, success metrics, and escalation paths. - Refining governance based on real usage.
Updating policies and controls based on lessons learned from pilots and early adoption.
At this stage, AI is less about experimentation and more about rolling out new tools, similarly to how we always have done with other software packages.
Phase 4 (Months 6-8): Optimization, Measurement, and Scale Decisions
The final/ongoing phase focuses on outcomes and sustainability.
Organizations do their best to:
- Measure business impact.
Productivity gains, cycle-time reductions, decision quality, and risk reduction. - Retire ineffective tools or use cases.
Not every AI experiment deserves to scale. - Refine training and role-based enablement.
Move from general literacy to role-specific proficiency. - Simultaneously invest and disinvest in AI. Unsubscribe from elements that are not working .
Based on value, risk, and organizational readiness.
By this point, AI is no longer a novelty or a better Google. It is a governed, measurable capability baked into the business.
Turning AI Hype into an Executable Plan
Ready to move from AI discussion to execution?
Kreischer Miller’s Technology Solutions team helps organizations develop AI-enabled technology roadmaps that reduce risk, eliminate technical debt, and accelerate performance.
If you’re ready to:
- Align leadership around a shared vision,
- modernize systems so AI can scale safely,
- identify quick wins that demonstrate value,
- and bring shadow AI usage into a governed structure,
Schedule a Technology Roadmap Discovery Call by contacting us today or exploring our Technology Solutions.