Artificial intelligence (AI) is transforming industries, driving efficiencies, and enabling new business models. Gartner forecasts global spending on AI software will increase from $124 billion in 2022 to $297 billion in 2027, a 19.1% CAGR. For enterprises, embracing AI is no longer a strategic option—it’s necessary for survival. Just as a chef learns various techniques to create a full-course meal, enterprises must understand the various distinctive flavors of AI that best support their business strategies. This piece will outline ten considerations for organizations to consider throughout the planning and deployment phases of implementing AI in accordance with the pragmatic AI model.
AI encompasses a broad range of technologies and applications, each with their own uses and benefits. Generative AI and the use of large language models (LLMs) like ChatGPT can automate and simplify content creation and customer interactions. This includes drafting emails, generating reports, and providing customer support. Alternatively, predictive AI leverages complex data sets to make recommendations and support decision-making processes. An example of this is using predictive analytics to accurately forecast customer payments and optimize cashflow. The key to a successful AI strategy lies in the evaluation of pragmatic use cases and selective investments that will help to deliver business objectives.
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Pragmatic AI maturity model
A recent 2024 Global Service Dynamics Report revealed that adapting to AI is expected to be a leading business challenge, surpassing competition and the shortage of skilled professional services labour. Using a straightforward model to assess their current AI maturity level, companies can understand their capabilities and guide investments that enhance growth and ensure survival. The practice of implementing AI that is deployable, actionable, and closed-loop for continuous improvements requires careful planning and gradual progression. That’s where the Pragmatic AI Maturity Model comes in, providing a five-stage taxonomy for understanding an organization’s AI competence.
To know where an organization needs to invest in AI pragmatically, this model of maturity helps determine where to grow and improve. The stages include:
-Stage 1: Initial – This is like having a larder full of ingredients but no recipe. Most organizations are here, developing isolated GenAI projects using fragmented datasets.
-Stage 2: Repeatable – Like cooking from a boxed kit, this stage features productized deployments of standalone solutions with AI integrated into them.
-Stage 3: Controlled – This stage is like cooking a full meal from a detailed recipe. Organizations have established a unified data strategy, consolidating transactional and operational data into a single repository.
-Stage 4: Optimized – Like a well-stocked, organized kitchen, this stage features robust data infrastructures in place that enable the use of advanced AI models for complex predictions and insights.
-Stage 5: Continuous Improvement – This is the Michelin-star stage. Organizations operate in the ideal state: a closed-loop system with clean, real-time data that continuously improves AI models.
10 tips
Once the maturity stage has been assessed, there are 10 tips to climb the AI Maturity Model ladder and reach the pinnacle of continuous improvement:
1. Ensure “clean” data Before jumping in, teams must take a temperature check on their current assets. A clear signal a business might not be AI-ready is if it lacks “clean” data.
2. Assemble a governance plan A governance plan is also imperative to manage AI data and broader initiatives supporting the business strategy. This plan should include policies for data collection, storage, access, and use. It is also essential to have a process for monitoring and updating AI models as the data changes. It’s important that AI activity does not operate in a vacuum, but is designed to solve actual problems that impact the overall business performance. It also ensures that governance issues are not considered in a piecemeal fashion, but at an organizational level.
3. Identify the business problem In a pragmatic approach, the key to success is to focus on solving current business issues. Businesses should start by identifying the most important challenges they face and then look for solutions that help solve them.
4. Integrate into existing workflows To ensure ease of adoption and effective use among teams, pragmatic AI solutions should be user-friendly and straightforward to integrate into existing workflows. This means the solution should seamlessly connect with the company’s existing systems and tools.
5. Define success Clearly defined KPIs tailored to specific AI goals are crucial, and continuous measurement and iteration are essential for maximizing the success of a business’ AI journey. Teams must be sure to track attributable cost savings, efficiency gains, and revenue growth.
6. Identify your deployment team members Identify who will help to roll out the technology at each step. Which stakeholders will be involved and when during the rollout process?
7. Consult the experts AI is a rapidly evolving technology and leading edge skills are in short supply. Companies should determine where they need to supplement their in-house resources with third-party expertise.
8. Create feedback loops Implement mechanisms to capture feedback from AI models and use this data to refine and improve these models continuously.
9. Develop training materials To address possible concerns of replacement by AI, training and messaging materials must highlight who teams how to leverage the technology to meet the company’s goals, enhance jobs and enable upskilling.
10. Request regular feedback In addition to deploying a closed-loop model, securing feedback from the team members using AI is crucial. Is the technology easy to use, is it beneficial, is the training and roll-out efficient? These are all factors that should be considered through feedback, so the deployment team can adjust accordingly.
By considering these 10 points to successfully prepare for and implement AI, organisations can ensure they are not only implementing AI that is deployable, actionable, and closed-loop, but also laying the groundwork for continuous improvement. The Pragmatic AI Maturity Model highlights this journey from random ingredients to Michelin-star organisation. As companies progress through the stages, the focus naturally shifts towards building a culture of continuous learning and adaptation. This ensures they stay ahead of the evolving AI landscape and unlock the technology’s full potential.
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