Developing AI Strategy for Your Organization

Understanding organization vision and goals:

Generative AI has the potential to radically transform existing economic and social frameworks, as did the internet and earlier innovations such as electricity. The question for your business is how AI will support enterprise ambitions and drive stronger results.?

Deployed well, Gen AI will become a competitive advantage and differentiator, building on the ability of?AI, in general, ?to automate repetitive and tedious tasks and generate new insights, ideas and innovations with predictive analytics, machine learning (ML) and other AI methods.?

Generative AI could significantly impact shareholder value by creating new and disruptive opportunities to drive enterprise goals such as:

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• Increase revenue.?AI will help enterprises create new products more quickly. Pharma, healthcare, and manufacturing (CPG, food and beverages, chemicals, and materials science) will become AI-first industries as they develop new drugs, less-toxic household cleaners, novel flavors and fragrances, new alloys, and faster and better medical diagnoses.?
• Create greater customer engagement.?By disrupting existing value chains and business models and enabling organizations to create and distribute content directly to consumers, thus bypassing traditional intermediaries such as publishers and distributors, generative AI can improve customer engagement.
• Reduce costs and improve productivity.?Gen AI capabilities can simplify processes and speed up results, whether by augmenting the efforts of human workers (e.g., summarizing, simplifying, and classifying content), generating software code or optimizing chatbot performance. Gen AI can also make use of previously unused (and thus wasted) data.

?Value:

Identifying what value Gen AI uses-cases will provide and how should we measure the AI success measures. ?A recent Gartner survey of more than 600 organizations that have deployed AI shows those with the widest, deepest, and longest experience with AI do not measure success by project volume, tasks completed or output. Instead, they:

1. Focus more on?business metrics?than financial metrics and follow specific attribution models and ad hoc measures tied to each use case.?
2. Benchmark?both internally and externally.
3. Identify metrics early and measure the success of AI use cases quickly and consistently.
4. Business metrics include those focused on:

• Business growth, e.g., cross-selling potential, price increases, demand estimation, monetization of new assets
• Customer success, e.g., retention measures, customer satisfaction measures, share of customer wallet
• Cost-efficiency, e.g., inventory reduction, production costs, employee productivity, asset optimization

?Risks:

It is important to create awareness among the CEO and C-suite of executives on the potential benefits and risks of transforming the business with generative AI.

This will enable the organizations assess the risks associated with your proposed generative AI use case and determine a corresponding acceptable use policy for generative AI and a plan to improve organizational data security.

This will involve assessing the suitability of AI for your use case and assessing the risks to data confidentiality, integrity, and the AI system.????

Adoption Strategies:

• Understanding what AI can do and what AI cannot.
• Identify and analyze current business problems.
• Ensure leadership buy-in and commitment in every phase.
• Adopt strong data – driven culture focusing on how to establish a data, analytics, and AI infrastructure that is efficient, scalable, well-governed and future-proof.
• Strategies for striking a balance between leveraging third-party capabilities and developing in-house models.
• How to choose between open source and propriety technologies.
• Techniques for identifying suitable use cases, and delivering tangible business value.

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