So, your company has decided to embrace generative AI—the exciting, much-discussed technology driving innovation across industries. Naturally, you want your first project to succeed and deliver real value, not just hype and sunk costs. To set up for success, your company needs a clear AI strategy that aligns with its business goals. This article will help you start meaningful conversations among business and IT leaders about implementing generative AI initiatives that produce tangible, achievable results.
AI impacts corporate growth strategies, with AI investments expected to double over the next year. Just three years ago, corporate leaders across industries noted that AI spending comprised only 5% of total budgets. By 2025, investments will grow, and half of the surveyed companies plan to increase that share to as much as 25%.
Generative AI investments are already showing promising returns, particularly in operational efficiencies (77%), employee productivity (74%), and customer satisfaction (72%). But to achieve these impressive results, organisations need well-defined AI implementation plans—and, most importantly, a clear vision of where AI will take them.
Without a solid strategy, an AI pilot project can easily lose momentum before it’s scaled across the organisation. Once the initial excitement fades, leaders might begin to question whether AI on the scale is truly needed in their processes and what value it can bring.
To avoid this common challenge of digital transformation, start with developing an AI strategy. This structured plan outlines how AI will be integrated into your organisation to support your broader business goals.
An AI strategy planning includes the following:
- Defining business-related metrics
- Assessing organisational readiness
- Identifying high-impact AI use cases
- Launching pilot projects
- Scaling AI deployment
According to Gartner, only 10% of companies experimenting with the new technology are fully mature in their AI adoption. Aspiring generative AI adopters have a lot to learn from these leaders. The key? Taking a strategic, phased approach.
Solving problems that really aren’t significant in business can drain valuable resources and dampen enthusiasm for future investments, putting companies at risk of falling behind their competitors.
To encourage organisation-wide adoption of AI tools, start by clearly defining your goals. This will help you identify the right use cases, ensure a strong return on investment, and lay the groundwork for scaling AI in business strategy.
Key questions to consider include:
- Will AI deliver better business value in these areas?
- How can AI help achieve this business goal, and how will success be measured?
- Which use cases will maximise that value?
To measure the value of individual use cases, tie them to success metrics that align with your overall business objectives. Choose metrics that relate to success factors and define a timeframe for demonstrating measurable value. Below is an example of key metrics and the ways AI can help enhance them.
Key AI success metrics to consider
|
| Business goal |
AI feature |
Success metric |
Use case example |
| Revenue growth |
Business model changes, predictive analytics and new product ideation |
Product-specific performance tracking |
AI-driven predictive models to forecast sales trends, identify high-value customers, and predict purchasing behaviour |
| Improved customer satisfaction |
Enhanced customer support, customer behaviour analytics, personalised communications |
Customer satisfaction score, net promoter score |
AI voice bots for personalised inbound and outbound communication |
| Reduced costs |
Operating expenses, ROI, customer acquisition costs, etc. |
AI automation to reduce operational costs |
Enforcing regulatory compliance for legal teams |
| Increasing staff productivity |
Automating mundane tasks |
Time to complete tasks, overtime hours, task completion rate, etc. |
AI-powered search tools for employees, meeting summaries, document generation automation |
| Improved service availability |
Automated monitoring and alerts, predictive maintenance |
Availability percentage, incident frequency, service recovery time, etc. |
Predictive maintenance for manufacturing and energy, etc. |
| Product/service development |
Market research automation and consumer insights |
Time to market, development cost, ROI, etc. |
Automated prototyping and predictive analytics with AI tools |
The next step in a generative AI strategy is understanding where your company stands on the AI readiness scale. To help with this, we’ve put together a comprehensive guide to assess your company’s readiness for deploying generative AI technologies. It highlights areas that need attention and supports strategic planning to ensure a smooth and successful AI adoption.
Assess your company’s readiness to buy or develop the necessary AI technologies and platforms. This helps identify any limitations or challenges that must be addressed before moving forward with AI initiatives.
Statistics show that most companies consider infrastructure constraints a central barrier to AI implementation. Therefore, ensuring sufficient data storage and processing power is essential for smooth AI applications. Additionally, ensure your data formats and structures are compatible with AI to streamline integration and lay the groundwork for successful AI deployment.
2) Organisational culture and leadership
Does your company have C-suite support for AI initiatives and digital transformation? Is your company culture focused on innovation? Are leaders committed to investing in AI training and development for employees?
These questions are critical for successful enterprise AI strategy development. C-suite solid support is essential for AI initiatives to succeed, with most executives linking AI adoption to increased company productivity.
Organisations that invest in employee AI training and development show leadership’s commitment to empowering teams and driving AI success. As we’ve seen, companies with a culture of innovation built into their DNA are now leading the way in generative AI adoption.
3) Employee engagement and training
It’s normal for employees to feel uncertain about new tech, especially generative AI, which also raises concerns about job security. The fear that AI will replace human jobs can often overshadow its benefits.
To address this, companies need to create an open AI policy and communicate transparently. Break down common misconceptions and habits that could slow down AI adoption. The goal is to help reshape attitudes before they turn into roadblocks. Make sure employees understand the advantages of AI: how it can fuel growth and productivity and why sticking to manual processes can hold the company back.
Consider having employees who are already using AI share their hands-on experiences rather than relying only on presentations from tech experts. Hold webinars and training sessions where employees can try out AI tools themselves. Encourage them to brainstorm ways AI could help in their daily work. (We’ll cover use case ideation in the next section.)
An AI implementation strategy is only as effective as the team behind it. To bring your AI vision to life, be ready to address the three key areas: hiring the right talent, developing tech skills internally, and building a culture that supports AI.
Start with a skills gap analysis to pinpoint where AI expertise is needed. This will guide you on tech training and hiring priorities to support your AI goals. Invest in a well-rounded AI training program for current tech employees to help build the in-house skills essential for long-term AI success.
For a full checklist on AI readiness, download our free whitepaper. It covers how to use generative AI with corporate data, along with practical tips to get started. Visit the page to download your copy.
Before launching a pilot project, shortlist the most relevant applications of generative AI. Generative AI has incredible potential in business automation, efficiency, and cost reduction. However, to get the best return on your investment, focus on the use cases that offer the most benefits for your company’s unique processes and structure.
At Tovie AI, we guide companies in identifying where AI can add the most value. Our AI consulting services begin with a series of workshops involving key stakeholders and teams. These sessions are designed to help everyone understand generative AI applications and to map out potential AI use cases specific to your business needs.
In the workshops, teams brainstorm ways AI could make their daily tasks easier or more effective. Next, we assess each suggested use case, looking at complexity, implementation speed, and cost. From there, we select the most promising AI applications that have the potential to make a real impact across your organisation.
After identifying a top use case, we run a three-month pilot with the relevant team. At the end of the pilot, we provide a detailed business case to help you decide on a full-scale deployment.
Companies across sectors — finance, healthcare, and beyond — have used our AI strategy consulting services to speed up their AI adoption journey. for a free consultation and discover how generative AI can support your business goals.
Choosing the right tools and vendors can save you from unnecessary costs and ensure your project aligns with your AI business strategy. Start by working with specialists who can evaluate your processes and assess your AI readiness.
When it comes to choosing the right vendor, there are a few key factors to keep in mind:
Consider the deployment options the vendor offers. Do they provide private cloud or on-premise solutions for better control, security, and customisation? You’ll also want to understand the infrastructure requirements — what computing resources are needed for deployment?
Data security should be a top priority. Look for vendors with strong safeguards to protect sensitive information, including features like data anonymisation, consent management, and strict compliance with data protection regulations.
3) Customisation and integration
Check if the solution allows for customisation. In some cases, access to multiple large language models (LLMs) might be crucial. Companies with large datasets may need AI solutions to be trained securely on their data and integrated seamlessly across corporate platforms. This is especially valuable for use cases like enterprise-level search.
Budgeting for AI initiatives is just as important as choosing the right tools. with our team to get a clearer picture of the costs and technical requirements. We can help evaluate your project and guide you through the key financial and technical aspects of AI implementation.
Preparing your data is one of the most important—and challenging—steps in your AI strategy roadmap. The effectiveness of AI tools depends heavily on the quality of the data they work with.
Think about your own organisation: you might have PDFs, complex spreadsheets with multiple tabs, JSON or XML files, images, videos, and audio. This diversity in formats creates potential bottlenecks and makes pre-processing essential for successful AI implementation.
Before using this data, it must go through a pre-processing pipeline—a detailed process to clean, structure, and organise it. Techniques like optical character recognition (OCR) and agentic processes help convert unstructured data into formats that AI tools can process effectively. This is particularly valuable for companies dealing with complex documents, such as reports with graphs, charts, and other visuals.
At Tovie AI, we’ve developed expertise in handling this process for our clients. We also offer this as a service—feel free to .
To avoid delays, we recommend planning your data strategy early. If you can capture and structure data adequately at the source, you’ll save significant time and effort later.
Data governance isn’t just a technical challenge—it’s a human one. Educating employees on the importance of clean, well-structured data and equipping them with the right tools can turn them into key contributors to your AI success.
With AI tools here to stay, preparing your data now will set the foundation for long-term growth and greater impact in the future.
With the groundwork in place, it’s time to roll out your AI initiative. As part of a consulting project, Tovie AI takes the shortlisted use case and deploys the pilot on cloud infrastructure, running it over three months. During this phase, we provide ongoing training to the teams involved, encouraging them to experiment and get hands-on with the new tool. This practical experience not only makes the benefits of AI more tangible but also turns participants into advocates for change within your organisation.
A successful pilot lays the foundation for scaling AI and achieving company-wide adoption. So, aim to empower the teams involved and create a positive attitude toward AI adoption. Define clear success metrics upfront and share them across the company to ensure everyone understands the business value of the outcomes.
At the end of the pilot, Tovie AI delivers a comprehensive business case to support future production deployment. We also guide you in using the technology effectively to maximise its impact.
Scaling generative AI transforms pilot successes into real-world value, expanding its impact across your organisation. However, moving from small-scale pilots to full deployment isn’t without challenges. Issues like governance, risk management, workforce readiness, and data trust often come into sharper focus at this stage.
To succeed, organisations need a balanced approach that addresses the AI strategy framework, processes, people, data, and technology. Scaling generative AI requires investments in foundational elements like data modernisation, infrastructure, and talent.
Scaling comes with hurdles—some technical, some cultural. For example, while off-the-shelf AI tools for productivity are easier to deploy, more complex, strategic solutions require more significant effort and deliver higher returns. The key is to manage risks effectively while fostering widespread but responsible use. Clear policies, ongoing training, and open access to AI tools can help teams become more comfortable with the technology and its potential.
Organisations that follow an AI strategy plan and embrace scaling today position themselves to lead tomorrow, unlocking new opportunities for transformation and long-term value creation.