How Businesses Can Discover and Prioritize AI Opportunities

While most organizations are aware of the benefits of AI and want to realize those benefits, many aren’t sure of the best use cases to direct their limited resources. In fact, the most common bottleneck holding enterprises back from adopting AI is difficulty in finding appropriate business cases.

Poorly planned AI solutions can harm businesses, so careful consideration is vital. Understanding AI risks, appropriate use cases, and their impact on traditional business models is crucial. Recognizing AI's potential and reimagining business operations are intertwined processes essential for effective AI deployment.

We’ve developed several processes to identify the business functions with the best opportunities for AI transformation and prioritize those opportunities based on feasibility, budget, and ROI.

Choose a Business Function for AI Transformation

Succeeding with AI requires a major organizational shift. This is why it is beneficial to start by transforming 5-10% of the business. Focusing on a single business function allows you to build up the culture of AI for a single department to a point where the entire organization can see the results.

Instead of doing single pilot programs in different areas of the business, fully transform one function such as customer service, production, or IT operations before moving on to another area. This will allow you to see the benefits much more deeply than a more isolated test case and allow the transformed function to develop a culture of fully embracing AI.

How do you identify which business functions have the greatest potential for AI transformation? Here are several steps to take to identify potential AI opportunities:

1. Determine Strategic Alignment: Begin by examining your company's overarching strategic priorities and evaluating your long-term goals. Choosing business functions that align with your company's overall strategy and represent the highest ROI potential ensures stakeholder and employee buy-in.
2. Identify Bottlenecks: Take a bottom-up approach by identifying operational challenges and bottlenecks that are limiting productivity, collaboration, or customer experience. Focus on efficiency gains, customer engagement, and risk mitigation.
3. Research Industry Trends: Keep up-to-date on industry trends and successful AI implementations within your market to gather inspiration and anticipate potential risks. Understanding the priorities of the industry as a whole can help identify areas where you’ll need to increase proficiencies or create a competitive advantage in the future. However, avoid blindly replicating competitors. Many companies are adopting AI without a clear strategy or objectives.

Use these steps to generate a large list of potential AI projects for your organization. Next, we’ll work on identifying the right project to start.

Prioritizing AI Opportunities

Société Générale, the sixth-largest bank in Europe, became a leader in AI innovation and digital transformation by developing a detailed process for identifying and evaluating AI opportunities. After identifying over 100 AI use cases, they used a formal prioritization and risk management process to focus on the most impactful implementations.

“When you go from number of use cases to value, you realize that in the end, there are a couple of use cases in the portfolio which tick the box of AI but have no real relationship to the strategy. It’s better to have [fewer] use cases with bigger value at stake than a broad range of use cases.” Noémie Ellezam, Chief Digital Strategy Officer

Evaluate each of the AI opportunities identified above using the following factors:

• ROI: If an opportunity is viable at all, it's pretty much a given that there will be a positive ROI. Go a step deeper to identify clear revenue or cost reduction goals and ensure there is alignment with the overall transformation goals.
• Stakeholder Buy-in: Will the project receive sufficient support from the necessary stakeholders? This includes C-suite executives and the leaders and managers of the specific business function who will be responsible for ensuring full adoption of the new AI solution.
• AI Maturity Level: The most important decision for which AI opportunity to approach is readiness and maturity. This may be specific to different departments, making it much easier to see which business function is most prepared for AI transformation. AI maturity doesn’t happen overnight and there are various stages your organization must progress through. Read our recent blog to understand your AI maturity level.
• Data Readiness: Data quality and availability is one of the most common bottlenecks to AI adoption. The business function you hope to transform needs quality data, a reliable data flow, and structured and unstructured data storage. Many of the steps to data readiness, such as removing data silos, should happen organization-wide. Improvements can be made within other departments to improve AI readiness before these departments become the main focus of AI transformation.

Developer Resources: Access to AI talent necessary to implement your desired transformation could be another major limiting factor. The AI skills companies are most deficient in are AI programming (66%), data analysis (59%), operations for AI and ML (54%), general AI literacy (52%), and infrastructure engineering (45%). Outsourcing AI development can help fill talent gaps and open up more opportunities.

• Time and Budget: What will the project cost and how long will it take to develop a solution? If the opportunity shrinks before you can complete your AI development project, it may not be a viable option. This is one of the risks of chasing AI opportunities without careful consideration. However, there are strategies to lower AI implementation costs.
• Scaling and Integration: Many opportunities check all the boxes and may even perform well as a proof of concept, but don’t scale when integrating into the larger organization. Consider how each AI transformation will integrate into the larger-scale workflows and silos.
• Risk: Finally, evaluate the risks associated with each potential opportunity and decide what level of risk your organization wants to carry. Unexpected outcomes and predictions (49%), security vulnerabilities (48%), safety and reliability (46%), fairness, bias, and ethics (46%), and privacy (46%) are the biggest risks for which AI adopters are testing.

Take Your AI Project from POC to Production

Once you’ve carefully evaluated each opportunity based on the factors above, choose a business function and initial AI project with the greatest potential. Now the real work begins.

Despite the rapid maturation of the technology, business outcomes take longer to materialize due to challenges such as idea generation, prioritization, organizational structure, employee skills, and risk management. This is why 34% of enterprises that have adopted AI are still working on an initial proof of concept.

If you’re ready to take your proof of concept into production, Gigster can help. Share your AI opportunity with us and we’ll develop an implementation plan to help you finally realize the value of AI. Share your proof of concept here.