Generative AI Strategy - Navigating Business Opportunities and Risks

This blog is multi part, starting with building an organisational strategy and roadmap for generative AI and then looking at specific technical guidance for some common use cases. Hope this helps you with formulating your own Generative AI strategy and executing it in a more structured way.

Business Context

As the Chief Data and AI Officer of a large successful organization, you're at a crossroads with generative AI. Your initial experiments have yielded mixed results due to overoptimism and misalignment between technology and business goals. Now, you aim to develop a structured & robust strategy to scale generative AI effectively, delivering on business opportunities it brings while mitigating risks. You face three main challenges:

1. Your teams lack extensive experience in generative AI
2. You are uncertain which initiatives to prioritise
3. Rapid evolution of field

Your goal is to create a strategic plan that addresses these challenges and positions your organisation for success in the generative AI landscape.

Handling Uncertainty

When creating your generative AI strategy, categorise potential use cases into these five buckets:

1. Wait and Watch

Observe market developments before acting. Ideal for promising but nascent use cases. Example: Autonomous Agents

2. Experiment and Learn

Conduct small-scale tests based on informed hypotheses. Be prepared to pivot if results don't meet expectations. Focus on quick tests to determine production readiness and business value

3. Hedging

Prepare for multiple possible outcomes. Pursue both generative AI and established approaches for critical areas. E.g. For customer service modernisation, modernise using both existing and GenAI technology

4. Big Bets

Make significant investments in high-impact, feasible use cases. Pursue these strategically for potential high returns. E.g. This could be coding assistants and sales and marketing use cases

5. No-Regret Moves

Implement foundational improvements in data, AI, and generative AI. Build capabilities that will benefit your organisation regardless of specific outcomes. Prepare for rapidly improving technology and organisational maturity

This balanced approach combines caution with innovation, positioning your organization to adapt to various scenarios in the evolving generative AI landscape.

Opportunities and Risks

Strategy for generative AI should align with key business objectives while mitigating potential risks.

Business Opportunities:

1. Cost Reduction
2. Growth of Existing Business
3. Launch New Business Models

Key Risks to Address:

A more comprehensive exploration of legal and compliance issues is toward end of the blog. Here we look at top areas of risk.

1. Legal and Compliance Issues - Intellectual property concerns. Bias and inaccuracy in AI outputs. Evolving regulatory landscape. Mitigation: Stay informed on AI regulations and implement robust governance frameworks.
2. New Competitor Threats - Agile startups leveraging AI to disrupt the market. Mitigation: Monitor the startup ecosystem and be prepared to adapt or acquire innovative solutions
3. Established Competitor Advancements - Industry peers gaining a competitive edge through AI. Mitigation: Benchmark your AI initiatives against industry leaders and invest in strategic AI capabilities

By balancing these opportunities and risks, you can develop a comprehensive generative AI strategy that drives business value while safeguarding your organisation's interests.

Use Case Exploration

Let's look at some high level use cases for generative AI opportunities below. In your own organisation, each of these high level use case could branch into dozens or more concrete use cases with specific business units and teams.

One way to discover the concrete use cases would be to

1. Hold a discovery workshop or
2. Hold interviews with key stakeholders.

Opportunity - Reducing Costs with Generative AI

Generative AI can significantly reduce operational costs through two primary mechanisms:

1. Productivity Enhancement and

2. Automation.

Productivity Enhancement

Generative AI can boost productivity across various business functions:

a) Software, Data, and AI Engineering

Use AI coding assistants (e.g., Claude Sonnet 3.5, GPT-4) to accelerate development. Experts expect that developer productivity will improve by 56%. This can be transformational in impact. Benefit: Faster delivery of digital, data, and AI transformation projects

b) Sales and Marketing

Create marketing assets and refine content using AI tools. This includes using GenAI assistant as useful communication critique and coach. Use AI for strategy discussions and refinement. Improved sales and marketing efficiency can help increase business.

c) Communication, Strategy, and Coaching

Employ AI as a critique and coach for documents, emails, and presentations. Enhance soft skills and strategic thinking across the organization. Result: More effective communication and improved strategic decision-making. This can make organisation more competitive.

d) Office Productivity

Integrate AI into document creation, presentations, email and other daily usage tools. Benefit: Increased efficiency and effectiveness in knowledge work of knowledge workers

e) Knowledge Management

Implement AI-powered systems using Retrieval Augmented Generation (RAG). Enable quick access to internal knowledge, reducing research time from days to minutes.

f) User Interface for Data and AI

Provide natural language interfaces for data analysis and insights. Use AI agents to simplify interaction with complex AI models and data systems. Benefit: Democratize data analysis across the organization

2. Automation

Generative AI can automate complex tasks, further reducing costs:

a) Structured Data Extraction

Use AI vision models to extract data from scanned documents and PDFs. Implement human-in-the-loop verification for accuracy. Result: Faster processing times and improved customer experience

b) Agentic Workflows

Deploy AI agents that collaborate, plan, and execute tasks using APIs. Note: While nascent, this technology shows promise for complex process automation. Benefit: Streamlined operations and enhanced customer service

By strategically implementing generative AI across these areas, organizations can significantly reduce costs while improving efficiency and service quality. However, it's crucial to address potential challenges such as data security and the need for human oversight in automated processes.

Opportunity - Grow existing business

1. Improve Customer Experience
2. Improved Sales, Marketing and Strategy Functions

Improve Customer Experience

1. Agentic Process Automation - With faster processes, business can respond faster to needs and requests of customers, improving their customer experience.
2. Customer Communication - Such as customer service and website can be more effective, helpful and easier to understand with help of GenAI assistant. Provide right information in real time to customer service representatives and improve the website content.

Improved Sales, Marketing and Strategy Functions

1. Marketing and Sales Content Generation Tools
2. Strategy Critic and Coach
3. Communication Critic and Coach

Opportunity - Launch new business

1. New Generative AI Service for Customer - With Generative AI, end customers too could have a new touchpoint with business using generative AI assistant. This would need lot more effort to reduce risks. However, if well implemented, such assistant be very useful in establishing a new stream of revenue and a new business model.
2. Digital Transformation - With coding assistant for applications, your organisation can more productively create and update software products for internal and external consumption.

Use Cases Prioritisation & Roadmap

Just across 1) cost reduction, 2) grow existing business and 3) launch new business, there are total of 14 high level use cases of generative AI we noticed above.

This brings us to another challenge facing the chief data and AI officer, that of prioritizing business use cases.

A useful framework to prioritise these would be to use a 2x2 matrix such as shown below. Arrange your use cases around x axis as impact and y axis as feasibility. Then take the high impact, high feasibility quadrant on top right as the use cases to focus on.

Feasibility could be determined using

1. Information from industry reports
2. Quick representative experiments

Additionally, you can take those use cases and arrange by effort as below. You can then pick the low effort + high impact (and high feasibility) use cases as the quick wins you can prioritise to deliver first.

Delivering the quick wins can help gain business confidence in outcomes and invest further for items on roadmap. Even for high impact, high effort use cases you should deliver interim outcomes in the roadmap to keep delivery visible to business.

Delivery, KPIs, and Measurement in Generative AI Projects

To successfully execute generative AI use cases, we recommend using the DMAIC framework: Define, Measure, Analyze, Improve, and Control. This approach ensures a structured and data-driven implementation process.

DMAIC Framework for Generative AI Projects:

1. Define - Clearly articulate the business problem, context, constraints, and scope. Align with key business sponsors and stakeholders. Set appropriate Key Performance Indicators (KPIs) Best Practices: ? Focus on business outcomes rather than technical deliverables ? Keep problem definitions neither too narrow (not flexible) nor too broad (not useful) ? Avoid overpromising; aim to exceed expectations ? Be aware of cognitive biases (e.g., optimism bias, confirmation bias, availability bias, anchoring, loss aversion/sunk cost bias, authority bias) and actively work to mitigate them.
2. Measure - Establish baseline metrics for chosen KPIs before implementation. Ensure data collection methods are reliable and consistent.
3. Analyze - Conduct in-depth analysis of the problem. Test hypotheses to determine the best approach. Be prepared to pivot based on findings.
4. Implement - Execute the project plan. Provide regular updates to management on progress and risks. Leverage sponsor support for addressing critical blockers. Share progress metrics consistently.
5. Control - Continuously measure improvements post-implementation. Establish processes to prevent reverting to old approaches. Implement ongoing monitoring and adjustment mechanisms

Key Considerations for Successful Implementation:

1. Stakeholder Alignment Identify and engage all key stakeholders early. Ensure shared understanding of success criteria. Maintain open communication throughout the project
2. KPI Selection Choose KPIs that reflect business value, not just technical metrics. Examples: Improved customer experience, higher NPS scores, increased efficiency. Focus on directional improvements rather than specific numeric targets.
3. Risk Management Regularly assess and mitigate potential risks. Be particularly aware of AI-specific risks (e.g., bias, data privacy).
4. Scalability Design pilots with potential for scaling in mind. Document learnings and best practices for future implementations.
5. Continuous Improvement Treat the implementation as an iterative process. Encourage feedback and be ready to make adjustments.

By following this structured approach and keeping these key considerations in mind, organizations can significantly increase the chances of successful generative AI project implementation and achieve meaningful business outcomes.

Evolving Strategy for Generative AI

Generative AI is evolving fast, making it more cost effective, capable and useful in lot more use cases rapidly. This requires revisiting the Generative AI strategy regularly such as every 6 months.

According to McKinsey's State of AI in Early 2024 report, 65 percent of respondents report that their organisation is regularly using gen AI, nearly double the percentage from their previous survey just ten months ago. You can also see the popular use cases in this report. This, and other such reports can help you to see what industry is doing in GenAI space to inform your own decision. This is in addition to gathering first hand information.

Risks and Governance

Risks

Traditional machine learning had risks and governance frameworks which revolved around data privacy, fairness, explainability. Generative AI extends and changes risks that need to be managed further.

Strategic Risk - Companies that take a wait and watch for not just one use case of generative AI but whole technology itself stand a strategic risk of being left behind significantly. It is no longer a technology that companies can wait out completely.

Regulatory Frameworks. Unlike GDPR for data privacy, which set a standard for global regulations to follow, there is no such standard framework in place for generative AI. The regulatory framework in different jurisdiction are taking different approach to regulations. Organisations need to monitor the regulatory landscape to handle proper response.

Explainability - For example, it is known that generative ai models are intrinsically hard to look inside and explain, unlike traditional models. In such scenarios, it may be more about looking at outcomes instead of outputs. Newer models that include thinking and chain of thought reasoning could become more explainable.

Fairness - Similar to explainability, fairness evaluation would also have to be measured using outcomes and not output. This is due to intrinsic un-explainability of generative ai models. Newer models and techniques such as planning, chain of thought reasoning can improve this factor.

Third Party Concentration Risk - Additionally, there is a third party concentration risk with limited number of large language model providers. This means a better management of third party risk is needed.

Malicious Security Risk - Generative AI can be used to effect security/vulnerability attacks that are more effective. It can also be used to prevent such attacks.

Intellectual Property Risk - Lot of newer models are not only trained on internet public data but also on books. This opens up a possibility of intellectual property risk. Providers of models are trying to mitigate this risk by considering various steps such as training data transparency, opt out mechanisms, licensing model and legal discussions. This is evolving area.

Sustainability Goals Risk - Generative AI is power hungry. This creates sustainability goals risk for organisations.

Work Reshaping Risk - Generative AI would significantly reshape work. While many new jobs would be created This significant shift and how organisation handle is important

Mitigation

Human in the loop - Many of the use cases involve or should involve human in the loop to reduce risks.

Explainability and fairness - It would be interesting to see how explainability and fairness is handled by different constituents. Some organisation may measure outcomes instead of outputs. Generative AI could also be used to test for fairness rapidly. Additionally reasoning models or chain of thought models can show reasoning behind a output, promising higher explainability. Some of this could be handled by proper prompt engineering.

Guardrails - Evolving generative AI systems provide various mechanisms to put guardrails around output of generative AI.

Risk and Compliance Teams - Investing in risk and compliance team and responsible AI programs.

Identity and Access Management, Cybersecurity - Data privacy, security, access control & cybersecurity.

Watermarks - Watermarks, specially in generative AI generated images, videos and audio could help to identify that a content was created using generative AI. This can be used to mitigate some risks.

Practical Deep Technical Guidance

This was part one of five part blog. This sets the stage with generative AI strategy.

In subsequent four blogs we would look at technical guidance for following use cases

1. Software Engineering - Generative AI Coding Assistants (Evaluation, Security, Privacy and Networking concerns)
2. Automation - Vision Generative AI for Document Processing. Agentic Workflows using LangGraph and AutoGen
3. UI for Data and AI - Using tool use and modern analytics systems to hide inner details of Data and AI
4. Knowledge Management System - Advanced RAG (fine tune embedding, hybrid search ( BM25 + Vector Search ), reranking, chunking etc)

Share your views on this blog below in comments and let me know if you want to cover anything else