Leadership teams are considering how their organisation can start to make effective use of AI, and pressure is mounting on IT teams to deliver improvements to customer service and user productivity through AI-powered innovations.
Yet these initiatives often stall before they reach meaningful value. In fact, while half of organisations say they’ve already achieved some level of AI adoption, many still struggle with return on investment because of unclear AI roadmaps, data complexity, or limited in-house expertise.1
That’s why, regardless of current AI adoption maturity, businesses of every size need a clear plan. Without the right AI framework in place, projects risk burning through budget and time without ever scaling successfully. This blog, the first in a five-part series, sets out the essential stages of a successful AI adoption journey.
Why AI adoption needs a roadmap
AI is not a technology that’s simply “plugged in” and left to deliver results. Success depends on marrying investments to specific outcomes outlined as part of the business’ wider strategy. A clear AI roadmap helps define priorities, distil activity into manageable stages, and connect key stakeholders on unified goals.
Each organisation will find its own route, but those that follow a structured business AI framework aligned to the steps below are far more likely to achieve long-term success. At each stage, we’ll also outline how our AI expertise and AI Ideation Workshops, delivered in collaboration with Lenovo and NVIDIA, help you navigate the process and accelerate business value.
1) Planning and strategy
The most successful journeys start with a clear direction. The first stage of any business AI programme is deciding what problems the technology should solve, or where it can add the most value from day one.
Repeatable processes that are typically time or resource intensive are where many organisations identify immediate AI value. Take an example shared in this solution brief from Lenovo, where an AI-driven quality inspection solution – developed by Trifork and powered by Lenovo ThinkEdge servers and accelerated by NVIDIA AI Enterprise – enables manufacturers to automate quality checks with high-speed imaging and real-time analytics.
Involving stakeholders early through structured internal sessions such as use case exploration workshops will identify and prioritise the best AI opportunities. These conversations often reveal overlooked pain points and clarify which parts of the business are most ready to pilot new capabilities.
Our AI Ideation Workshops guide this with collaborative sessions that surface relevant use cases and draw on industry‑specific examples to inform discussions. This grounds planning in real‑world success stories to drive business impact.
With Lenovo Hybrid AI Advantage™ with NVIDIA businesses can access a flexible AI framework – built on AI infrastructure and devices, together with NVIDIA AI Enterprise, accelerated computing, and networking. It includes a library of pre-validated use cases that can be customised by function or vertical, and expert AI services that deliver real business value in under 90 days. This provides a strong foundation for your AI roadmap and helps break down the barriers to ROI from AI.
2) Pre‑deployment preparation
Once a defined strategy is in place, attention turns to AI readiness — ensuring that data, skills, and infrastructure can support sustainable AI adoption. Consider this stage like building the runway before take‑off. Without it, the journey ahead quickly becomes turbulent.
76% of organisations don’t have an AI-ready governance, risk, and compliance (GRC) strategy, and data management causes hurdles for those who are yet to deploy AI, and early adopters alike.1 Poorly governed or disconnected data makes it impossible to build or train dependable models.
There can also be misconceptions around quantity over quality. More data is not always best, especially if the data being shared with an AI system is not relevant or of the desired quality.
Thought must also be given to where your data lives today, and how this is best collected and collated to ensure it can be appropriately accessed by AI. It might be that you need to migrate to remove any siloes that could present a blocker.
Infrastructure plays an equally vital role. Businesses need environments that are scalable, secure, and optimised for demanding workloads. Crucially, any infrastructure must meet the demands of any AI deployment today, while also aligning with your current IT estate.
Through our AI Ideation Workshops, experts from Lenovo and NVIDIA evaluate your current environment and recommend the best hardware and software stack to meet your desired use case, leveraging Lenovo AI-optimised infrastructure to support long-term scalability. These pre-validated configurations feature ThinkShield™ security for enterprise-grade protection, responsible AI frameworks, integrated governance, and AI-powered data orchestration, so you can safely manage large scale AI with confidence, meeting data privacy, security, and compliance standards.
3) Deployment and implementation
Once data and infrastructure are ready, an initial deployment takes AI from concept to reality. Targeted pilots and Proof-of-Concept (PoC) deployments, focused on a specific use case, help to strengthen decision‑making and deliver measurable insights within a controlled environment.
In practice, this might be an initial test case with a particular user group or department, such as the deployment of an AI-powered document analysis tool for a legal team that reduces time spent reviewing and organising contracts and other long-form documentation. These kinds of use cases can deliver real impact, with Lenovo and NVIDIA’s AI Knowledge Assistant Solution delivering up to a 45% improvement in accuracy and an 80% increase in data re-use for contracts, helping to streamline time-intensive workflows.2
During the PoC stage, monitoring and feedback loops are crucial. AI solutions cannot operate as “set‑and‑forget” systems. Keeping a human in the loop to apply oversight at critical stages provides valuable context, drives appropriate ethical usage, and ensures business judgment of every output. This principle applies not only to specific AI workflows but also to larger frameworks such as agentic AI solutions that could operate semi‑autonomously across multiple functions.
PoC sessions or rapid prototyping form an important phase of our AI Ideation Workshops, focusing initial deployments around key uses, including proven, production-ready use cases available through Lenovo Hybrid AI Advantage™ with NVIDIA, to quickly assess suitability and gauge potential business value.
The idea of AI deployment can also be unsettling for users who see a threat to their roles, so change management is another deployment consideration. Internal communications help users understand how AI tools complement their expertise rather than replace it. When implemented thoughtfully, a strong deployment phase builds both trust and momentum, preparing the ground for long‑term adoption and future growth.
4) Post‑deployment and optimisation
Once AI is in operation, focus can shift to ongoing utilisation and continuous improvement, and the reward for optimisation is clear. Studies suggest that AI can boost productivity by up to 80%,2 reduce costs by 70%,3 and handle up to 40% of customer queries through self‑service.4
AI models evolve as new data becomes available and business needs change. Regular governance reviews help to counter this, ensuring compliance, security, and ethical responsibility as systems scale. You can achieve trusted, compliant and responsible AI by managing and protecting data with integrated validated solutions with Lenovo Hybrid AI Advantage™ with NVIDIA.
Beyond our AI Ideation Workshops, our expert team can support you post deployment to help anticipate and manage and ongoing regulatory, ethical and operational considerations that might challenge long-term AI usage.
The strict enforcement of new AI-focused policies in areas such as data usage, access and retention is also important to ensure that your AI continues to effectively leverage only relevant and accurate data, avoiding any extensive data hygiene processes once foundations are in place.
Ready to build your AI roadmap
If you take anything away from this piece, it should be that AI adoption isn’t about chasing trends but about aligning technology with clear business goals and measurable value. By following a structured AI roadmap, business leaders can build momentum, remove adoption barriers, and maximise long‑term ROI.
Wherever you are on your AI journey, we can use our expertise alongside Lenovo and NVIDIA’s innovative solutions to solve challenges that matter most to you.
Book your AI Ideation Workshop today and break through the boundaries to AI success.
Sources
1. IDC (2025), CIO Playbook 2025: It’s Time for AI-nomics. (Sponsored by Lenovo).
2. Lenovo (2025), Lenovo Hybrid AI Advantage™ with NVIDIA Solutions: AI Knowledge Assistant.
3. Lenovo (2025), Lenovo Hybrid AI Advantage™ with NVIDIA Solutions: AI Content Generation.
4. Lenovo (2025), Lenovo Hybrid AI Advantage™ with NVIDIA Solutions: Customer Service.