Recipe for AI Success: 10 Considerations for Businesses
Artificial intelligence (AI) is transforming industries, driving efficiency and enabling new business models. Gartner predicts that worldwide spending on AI software will grow from $124 billion in 2022 to $297 billion in 2027, a CAGR of 19.1%. For enterprises, embracing AI is no longer a strategic option, but a necessity for survival. Just as a chef learns different techniques to create a complete meal, enterprises must understand the different distinctive flavors of AI that best support their business strategies. This article outlines ten considerations organizations should consider during the planning and implementation phases of AI implementation in alignment with the pragmatic AI model.
AI encompasses a wide range of technologies and applications, each with their own uses and benefits. Generative AI and the use of large language models (LLMs) such as ChatGPT can automate and simplify content creation and customer interactions. This includes composing emails, generating reports, and providing customer support. Alternatively, predictive AI uses complex data sets to make recommendations and support decision-making processes. An example of this is the use of predictive analytics to accurately predict customer payments and optimize cash flow. The key to a successful AI strategy lies in evaluating pragmatic use cases and selective investments that will help achieve business objectives.
Director Solution Marketing at Certinia.
Pragmatic AI Maturity Model
A recent 2024 Global Service Dynamics Report found that adapting to AI is expected to be a top business challenge, outpacing competition and the shortage of skilled professional services providers. Using a simple model to assess their current AI maturity level, businesses can understand their capabilities and steer investments that fuel growth and ensure survival. The practice of implementing AI that is deployable, actionable, and closed to continuous improvement requires careful planning and gradual progression. That’s where the Pragmatic AI Maturity Model comes in, providing a five-stage taxonomy to understand an organization’s AI competency.
To know where an organization should pragmatically invest in AI, this maturity model helps determine where to grow and improve. The stages include:
– Phase 1: Initial – This is like having a pantry full of ingredients, but no recipe. Most organizations are here, developing isolated GenAI projects using fragmented datasets.
– Phase 2: Repeatable – Like cooking with a ready-made kit, this phase involves productive implementations of standalone solutions with AI integrated into them.
– Stage 3: Controlled – This stage is like cooking a complete meal based on a detailed recipe. Organizations have developed a unified data strategy, consolidating transactional and operational data into a single repository.
– Stage 4: Optimized – Like a well-stocked, organized kitchen, this stage has robust data infrastructures 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, here are 10 tips to climb the AI Maturity Model ladder and reach the top of continuous improvement:
1. Ensure ‘clean’ data Before jumping in, teams should take a hard look at their current assets. A clear sign that a company may not be ready for AI is if it doesn’t have “clean” data.
2. Develop a governance plan A governance plan is also necessary to manage AI data and broader initiatives that support business strategy. This plan should include policies for collecting, storing, accessing and using data. It is also essential to have a process for monitoring and updating AI models as the data changes. It is important that AI activity does not operate in a vacuum, but is designed to solve real problems that impact overall business performance. It also ensures that governance issues are not considered in a piecemeal manner, 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 problems. Companies should start by identifying the key challenges they face and then look for solutions that help solve them.
4. Integrate into existing workflows To ensure easy adoption and effective use within teams, pragmatic AI solutions must be easy to use and integrate into existing workflows. This means that the solution must fit seamlessly with the company’s existing systems and tools.
5. Define success Clearly defined KPIs aligned to specific AI goals are critical, and continuous measurement and iteration are essential to maximizing the success of a company’s AI journey. Teams need to be sure they’re tracking attributable cost savings, efficiency gains, and revenue growth.
6. Identify your implementation team members Identify who will help 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 advanced skills are scarce. Companies must determine where to supplement their internal resources with third-party expertise.
8. Create feedback loops Implement mechanisms to capture feedback from AI models and use this data to continuously refine and improve these models.
9. Develop training materials To address potential concerns about AI replacement, training and communications materials should make it clear which teams can leverage the technology to achieve company goals, improve jobs, and enable upskilling.
10. Ask for feedback regularly In addition to implementing a closed-loop model, it is crucial to get feedback from team members using AI. Is the technology easy to use, is it useful, is the training and rollout efficient? These are all factors that need to be considered through feedback so that the implementation team can act on them.
By considering these 10 points for successfully preparing and implementing AI, organizations can ensure they are not only implementing AI that is deployable, executable, and closed-loop, but also laying the foundation for continuous improvement. The Pragmatic AI Maturity Model emphasizes this journey from random ingredients to Michelin-starred organization. As organizations progress through the stages, the focus naturally shifts to 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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