AI is no longer experimental. It’s embedded in everyday workflows, driving real business results and reshaping how work gets done.
Breakthroughs in generative and agentic AI and broader access to low-code and no-code platforms are accelerating adoption. Meanwhile, companies are expected to move quickly, coordinate at scale, and prove value across departments.
Work management platforms are critical to orchestrating this shift. From strategy to execution, they provide the infrastructure to align teams, systematize work, and integrate AI into everyday processes.
This article explores what AI-driven change looks like today, why momentum is building, and how your organization can lead with focus, clarity, and measurable impact using a platform like monday work management.
What is AI transformation?
AI transformation is a strategic initiative where businesses integrate artificial intelligence into their operations, products, and services to drive efficiency, innovation, and growth. Unlike general digital transformation, which focuses on adopting digital tools and processes, AI transformation specifically leverages machine learning, automation, and data-driven insights to create new business value.
AI and digital transformation often work together: digital transformation lays the groundwork by digitizing processes and data, while AI transformation builds on this foundation to unlock advanced capabilities.
Key components of AI transformation:
- Process optimization: AI automates and enhances workflows, reducing manual effort and errors.
- Data-driven decision making: AI enables better business insights by analyzing large volumes of data.
- Customer experience enhancement: AI personalizes interactions, improving satisfaction and loyalty.
According to Glide’s The state of AI in operations 2025 report, 73% of companies have already adopted AI or are actively planning to. The question is no longer when to adopt AI, but how to scale it.
AI transformation vs. digital transformation: What’s the difference?
AI is both a driver and a core component of digital transformation. Whereas digital transformation laid the foundation by moving operations to the cloud, connecting data sources, and automating workflows, AI transformation builds on this by creating a feedback loop. The data trains models, models drive action, and those actions generate new data to further refine performance.
This shift marks a new phase in enterprise evolution. Instead of simply digitizing workflows, organizations are now designing intelligent systems that respond to change and deliver higher-impact outcomes.
| Focus area | Digital transformation | AI transformation |
|---|---|---|
| Customer experience | CRM platforms, self-service portals | AI-powered support, personalized messaging |
| Operations | Process automation, task tracking | Predictive workflows, auto-routing, outcome optimization |
| Data usage | Dashboards, scheduled reports | Real-time analysis, next-step recommendations |
| Product innovation | Centralized tools for product teams | Generative tools for ideation and testing |
Once businesses integrate AI into their systems, the impact is clear: 52% report a transformational impact on operations, compared to 28% who expected that outcome. AI drives change across more areas, with more speed and visibility.
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Key AI technologies driving enterprise innovation
Organizations are moving past experimentation and building AI directly into how work happens. The result is faster execution, fewer manual tasks, and better decisions.
Google’s ROI of Gen AI report found that 45% of companies seeing productivity gains say generative AI has doubled employee output. This isn’t a marginal lift. It’s a complete shift in how teams produce, plan, and deliver work.
Here are 4 core technologies driving that shift:
| Technology | What it does | Use case | Real-world example |
|---|---|---|---|
| Machine learning | Finds patterns in large data sets | Forecasting, trend detection | Predicting pipeline risk with sales ops models |
| Natural language processing (NLP) | Understands and generates human language | Ticket triage, content summarization | Auto-tagging and routing customer requests |
| Computer vision | Interprets visual information | Quality checks, inventory tracking | Flagging product defects in packaging |
| Generative AI | Produces new content from prompts or data | Copywriting, idea generation | Creating blog outlines from support transcripts |
When deployed through low-code or no-code tools, these technologies scale faster and deliver higher returns. In fact, 59% of companies say AI built on no or low-code platforms delivers the most transformational impact.
That’s why AI integration matters. AI tools work best when they’re accessible, easy to adopt, and tied directly into how teams already get work done.
What agentic AI means for enterprises
Beyond individual tools, a new class of AI that can act autonomously is emerging. This is known as agentic AI and refers to autonomous systems that make decisions and execute tasks with minimal input. These tools don’t just assist — they act. They initiate systems, adapt to context, and evolve based on new information.
This isn’t a futuristic concept, though, with 47% of AI adopters already use agentic tools. Over half already report a transformational impact from automating tasks like data processing and support ticket handling.
Here’s what an agentic workflow could look like:
- Classifying a support ticket by topic and tone
- Searching knowledge bases for past resolutions
- Drafting a suggested reply
- Routing or close the ticket based on business rules
Work management platforms can power similar processes using automations, conditional logic, and AI tagging, without code or custom dev work. These systems evolve over time as more data flows in.
For enterprise leaders, agentic tools shift the model from task delegation to operational orchestration. They reduce time spent on repeatable steps, giving teams more bandwidth for strategic work.
Assess your organization’s AI transformation readiness
Before launching new AI efforts, it’s critical to understand your current maturity level. For most organizations, the biggest barrier is a lack of knowledge.
Use this short checklist to identify strengths and gaps. Rate each statement from 1 (strongly disagree) to 5 (strongly agree):
- Data: Our operational data is clean, accessible, and API-ready
- Talent: A cross-functional group leads internal AI skill-building
- Governance: We have documented policies for reviewing and managing AI systems
- Culture: Employees are encouraged to explore and propose AI use cases
- Tooling: We can prototype and deploy AI workflows without waiting on IT
Your responses can guide the next steps in your AI journey, but because benchmarks vary by industry and goals, a deeper assessment may be helpful.
How mature organizations master AI transformation: 5 strategies for success
Reaching AI maturity means more than having the right tools. It also means having the right systems, teams, and strategy in place. According to a 2025 McKinsey report, 92% of companies plan to increase their AI investment, but only 1% have achieved full operational integration, where AI actively drives measurable outcomes across functions and informs broader business strategy.
This maturity gap presents a strategic advantage. Organizations that align business models with strong infrastructure, project governance, and workforce readiness can scale faster, automate routine tasks more effectively, and surface valuable insights that shape long-term outcomes, while others remain stalled in pilot mode.
1. They break down silos and integrate AI across teams
Operational AI requires more than isolated deployments. Mature organizations implement AI across business units to streamline decision-making, eliminate redundant workflows, and unify data.
For example, Syneos Health has leveraged AI to accelerate clinical trials by improving patient recruitment and optimizing trial design. By embedding AI into their core processes, they’ve improved efficiency and reduced time to market, enhancing their overall business model.
While IT leads AI deployment (67%), momentum in departments like customer service, sales, HR, and finance still lags. Bridging these gaps strengthens enterprise alignment, boosts customer engagement, and unlocks broader business value. This foundation sets the stage for high-impact, AI-driven transformation.
2. They fund the foundation for scalable growth
AI success is based on smart investments. According to EY’s 2024 research, organizations allocating at least 5% of their total budgets to AI are significantly more likely to report positive returns across productivity, operations, and innovation.
But investment alone isn’t enough. The report also found that two-thirds of business leaders say infrastructure limitations are slowing them down, and 83% believe stronger data systems would accelerate adoption and support scalable business models.
To support sustainable growth, organizations are shifting focus to the essentials: better data architecture, stronger governance, and enabling employees to move away from administrative tasks and focus on high-value initiatives.
For example, Zalando, a European fashion retailer, has integrated generative AI to expedite content production for marketing campaigns. By replacing mundane tasks with AI-powered systems, they’ve cut production time and costs by more than 90%.
How mature businesses fund what matters
Mature organizations take a holistic approach to budgeting. They don’t just invest in tools, but also in people, time, and infrastructure. A comprehensive AI budget should include:
- People: Upskilling, onboarding, and change management
- Technology: Software, platforms, and licensing
- Time: Pilot cycles, iteration windows, long-term roadmap
- Integration: Connecting new tools with existing systems and workflows
And it’s not just about ROI. Long-term growth depends on responsible planning, readiness, and the ability to adapt over time.
3. They lead with transparency and trust
Leaders like Citadel approach AI governance as a core part of business strategy, emphasizing transparency, auditability, and bias prevention. Their governance framework includes model validation, behavioral monitoring, and ongoing risk assessments.
For enterprises in high-impact industries, ethical AI isn’t optional. Ensuring fairness in customer service interactions and data-driven decisions is critical. Solutions like monday work management support this with permission-based access, audit-ready workflows, and oversight tools that make it easier to govern AI-driven transformation at scale.
4. They invest in their people and prepare for change
As AI automates more routine and administrative tasks, the demand for human expertise shifts. The World Economic Forum reports that 50% of employees will need reskilling by 2025. Yet, 22% of employees say they’ve received little to no support.
This is why upskilling matters. Organizations that invest in employee growth are better positioned to thrive in an AI-driven workplace. For example, L&D teams that use platforms like monday work management can build scalable programs that prepare teams for AI-integrated workflows, empowering them to contribute meaningfully beyond mundane tasks.
5. They prioritize continuous improvement and feedback loops
A study by IBM found that AI systems evolve continuously. Feedback loops are built into operations, helping organizations uncover valuable insights and improve performance iteratively.
This model is essential to AI maturity. Companies like Qualtrics are adopting agentic AI for instant customer engagement, using dynamic feedback to shape outcomes in context. By moving beyond static dashboards, they personalize interactions and improve employee and customer experiences simultaneously.
To support this adaptability, teams rely on platforms like monday work management. Integrated dashboards, automation, and feedback tools enable continuous improvement without adding unnecessary complexity.
Common challenges with AI transformation and how to overcome them
Even with momentum and initial buy-in, many companies struggle to implement AI across their organization. Beyond poor financial planning or a lack of clear KPIs, here are some additional roadblocks that organizations have to consider:
Talent shortages
More than half of companies deploying AI agents say their biggest barrier is lack of knowledge, not budget or security, making upskilling critical to keep pace. Use monday work management to manage learning plans, track training participation, and monitor AI readiness across departments — all in a centralized system.
Data privacy and compliance
Half of organizations cite privacy and security concerns as top blockers, especially in early-adoption stages. In some cases, there is the challenge of shadow AI, or employees using unapproved tools that create visibility and governance issues. In highly regulated industries like finance, insurance, and healthcare, AI workflows must align with standards like HIPAA, GDPR, SOC 2, and internal controls.
Organizations use monday work management to meet those requirements with permission controls, audit trails, and customizable processes, without slowing innovation. Secure deployment is supported through the platform’s permission-based processes, admin oversight, and enterprise-grade compliance. Every automation, integration, and AI flow is auditable and easy to govern at scale.
Resistance to change
Introducing new tools often meets internal pushback, with teams disengaging when tools are introduced without clear value.
An effective rollout also depends on leadership buy-in and a structured change management framework that helps teams adjust with clarity and confidence. With monday work management, teams can build pilot projects with templates, track results in real time, and share early wins using dashboards.
AI transformation KPIs to measure success
Tracking the right KPIs (key performance indicators) is essential for teams to show progress, guide decisions, and earn buy-in. When those metrics are clearly defined and consistently tracked, they become a powerful tool for communicating outcomes, spotting roadblocks, and course-correcting early.
Organizations leading AI adoption focus on the following categories of KPIs:
Operational efficiency: Time saved, reduced manual work, lower operating costs
Example: Automating repetitive tasks in workflows leads to shorter project timelines
Customer outcomes: Satisfaction scores, retention rates, support resolution times
Example: AI-enabled ticket routing improves response time and customer feedback
Innovation: Time-to-market, product velocity, feature deployment
Example: Faster alignment between product, engineering, and marketing shortens launch cycles
Employee experience: Task load, role satisfaction, productivity confidence
Example: Reducing admin overhead with AI assistants and giving teams more time for meaningful work
According to McKinsey’s report, 63% of organizations report that generative AI has already driven business growth. This growth isn’t accidental; it stems from connecting everyday improvements to broader strategic outcomes. With live dashboards, customizable reports, and real-time process data, monday work management makes that connection possible by helping leaders track progress, identify what’s working, and adjust quickly.
Connect strategy to execution with monday work management
Connecting strategy to execution is where many AI initiatives stall. It’s not for a lack of ideas, but because priorities get lost in day-to-day operations. That’s where monday work management comes in with centralized dashboards and cross-functional visibility, teams can turn high-level goals into actionable plans.
Start by capturing AI use cases in one place, prioritizing the highest-impact opportunities, and assigning clear ownership across departments. When strategy is visible, shared, and tied directly to ongoing work, alignment becomes action.
Prepare data and workflows for AI integration
Bring together disconnected tools, documentation, and processes into one workspace. With integrations and unified systems, teams set a strong foundation for reliable AI inputs and efficient collaboration.
Build and test with agility
Use no-code automation and work hubs to experiment with AI-powered processes without needing a full dev team. This speeds up iteration and gives departments the flexibility to refine execution as they go.
Pilot and measure impact with dynamic insights
Monitor early-stage pilots through live dashboards and shared KPIs. With customizable reports and up-to-date insights, leaders can track what’s working, communicate outcomes, and build internal momentum. As early-stage results take hold, the focus shifts to building scalable, compliant systems.
Scale and govern across the business
As organizations grow more confident, templates, admin permissions, and audit tools make it easier to expand adoption securely. Whether deploying AI in ops, support, or marketing, monday work management provides governance at every level.
AI where it matters most
AI should fit naturally into everyday work. Built-in capabilities within monday work management make that possible:
- AI Assistant: Generates content, summarizes updates, and suggests next steps.
- Smart replies: Helps service teams resolve requests faster with recommended responses.
- 200+ automations: Trigger actions, assign owners, and reduce manual work across processes.
By integrating directly into current operations, these tools support smarter decisions without adding complexity.
What’s next: modular AI and agentic workflows are becoming the norm
AI adoption is entering a new phase. Businesses are moving beyond isolated tools and toward dynamic systems that act, adapt, and scale with minimal oversight. Modular architectures and agentic capabilities are driving this evolution.
Over 90% of AI-using businesses already using AI report plans to deploy AI agents this year. These agents support everything from automated decisions to multi-step task execution.
In many industries, AI agents are already supporting outcomes, like manufacturing (predictive maintenance), healthcare (patient intake), and financial services (risk modeling), by powering use cases that previously required significant manual oversight.
New capabilities under active development at monday work management include:
- Smart triggers that adjust workflows based on changing data
- Built-in governance tools to manage AI activity and results
- Modular automations that connect processes, platforms, and priorities
Soon, enterprise teams will define a goal, configure the logic, and let AI take action while maintaining full oversight and control. This shift will move AI from a support role to a strategic enabler.
Take the next step with monday work management
Enterprise AI is reshaping how companies deliver results. The real challenge is execution: connecting strategy to action at scale. Bridge the gap with monday work management to align initiatives, earn buy-in, and accelerate impact.
Whether you’re surfacing opportunities, managing pilots, or scaling governance, the platform gives you the tools to lead. Start building smarter systems today, because the businesses that act now will define what’s next.
FAQs
What’s the difference between AI transformation and AI implementation?
AI implementation refers to introducing specific tools or technologies to solve defined problems. AI transformation is broader. It means embedding AI across core workflows, strategies, and decision-making processes to drive organization-wide change.
How do you prioritize AI use cases across departments?
To prioritize AI use cases across departments, start by mapping business objectives, then evaluating use cases based on potential impact, feasibility, and alignment with strategic goals. Prioritize those that deliver measurable outcomes and can scale across teams.
Do I need in-house AI experts to start?
No, you don’t need in-house AI experts to start your AI transformation journey. Many companies begin with external partners, no-code platforms, or off-the-shelf AI tools. As adoption grows, internal expertise becomes more valuable for scaling and governance.
How can companies ensure ethical AI use during transformation?
To ensure ethical AI use during transformation, establish clear governance policies, monitor AI outputs regularly, and involve diverse stakeholders in model development and review. Transparency, accountability, and bias testing are key practices for ethical AI deployment.
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