Build a GenAI business strategy for value-driven outcomes while minimizing risk.
From experimenting to executing value-driven AI initiatives, we have come so far. Yes, 2024 is the year we put all the learning and experimentation to work.
GenAI has shown promising unprecedented productivity improvements and business transformation across industries. But to capture its value and manage risks more sustainably, businesses must follow a strategic plan to achieve their GenAI goals.?
However, as per Gartner, only 10% of organizations have successfully deployed AI initiatives while others are still experimenting. C-suite taking the responsibility to transform business operations must focus on a strategic approach towards feasible GenAI initiatives, as investing in AI is not just enough.
Let’s understand how to plan your GenAI execution within business.
GenAI Benefits and Adoption Prediction: 2024
From recalibrating employee and customer experience to business and IT operations, GenAI is taking charge.?
- Software engineering is expected to benefit the most, with productivity gains valued at 25%-45% of its spending.
- By 2030, AI could reduce global CO2 emissions by 5 to 15%.
- By 2025, 70% of support requests initiated through GenAI-powered chatbots will demand human oversight due to customers’ mistrust, increasing service costs by 40%.
- As per McKinsey, 75% of GenAI's value is concentrated in four areas: customer operations, marketing and sales, software engineering, and research and development (R&D).?
However, the major challenges for executives lie in identifying where and how to fit GenAI into current business models to make it future-ready using AI trends.
A Comprehensive GenAI Roadmap
As a CTO, you're at the forefront of technological innovation. Generative AI presents a powerful opportunity to transform your organization, but navigating this new landscape requires a well-defined strategy. Here's a detailed roadmap, incorporating best practices and addressing key considerations throughout each phase.
Phase 1: Discovery and Foundation (3-6 months)
- Industry Analysis and Goal Alignment (1-2 months): Deep dive into your industry's trends, and competitor landscape, and identify potential generative AI applications relevant to your specific business goals.
- Data Assessment and Ethical Considerations (1-2 months): Evaluate data quality, quantity, accessibility, and potential biases that could skew your models' outputs. Develop strategies for data augmentation if needed to address data gaps. Establish clear ethical guidelines for responsible AI development and use, considering potential biases and fairness concerns.
- Technology Readiness and Skill Development (1-2 months): Conduct a technology readiness assessment to determine the infrastructure and resources required to support generative AI initiatives. This includes considering factors like computing power, storage needs, and scalability requirements. Evaluate internal expertise in generative AI development and deployment. Identify skill gaps and potential needs for external partners or training programs to upskill existing employees. Research pre-trained models, cloud-based AI solutions, and custom development options based on your needs and project complexity.
Phase 2: Experimentation and Development (6-12 months)
- Proof-of-Concept (PoC) Selection and Development (2-3 months): Choose a single, impactful use case with well-defined objectives and success metrics for initial exploration. Examples might include generating product design variations, creating personalized marketing content, or developing synthetic data for training other AI models. Explore pre-trained models or develop a custom model based on feasibility and expertise. Prepare high-quality data for training, ensuring ethical sourcing and proper anonymization. Train the model while monitoring performance and adjusting hyperparameters for optimal results.
- Model Refinement and Evaluation (2-3 months): Based on PoC results, refine the model's architecture, training data, or hyperparameters to enhance performance and outputs. Conduct rigorous testing with diverse inputs and datasets to assess accuracy, generalizability, and potential biases. Gather user feedback on generated outputs and iterate on the model to improve relevance and user satisfaction.
Phase 3: Deployment and Scaling (ongoing)
- Pilot Project Deployment and Monitoring (2-3 months): Select a use case suitable for a pilot deployment in a controlled environment. This allows you to test integration, gather user feedback, and identify potential issues before wider rollout. Integrate the trained model with existing workflows or platforms for seamless data flow and output delivery. Continuously monitor the model's performance in the pilot, assessing key success metrics like accuracy, efficiency gains, or user satisfaction.
- Cost-Benefit Analysis and Sustainability (1-2 months): Calculate the potential return on investment (ROI) of the generative AI solution, considering development costs, operational expenses, and expected benefits. Develop a plan for ongoing model maintenance, upgrades, and resource allocation to ensure long-term sustainability.
As per Gartner, the primary focus of GenAI initiatives is cost optimization. However, most of the GenAI initiatives fail due to abandoning efforts.
Once deciding on the overall GenAI initiative cost, consider all possible factors.?
Phase 4: Continuous Improvement and Innovation (ongoing)
- Scalable Infrastructure and Security (ongoing): Evaluate existing infrastructure to ensure it can handle increased demands as the solution scales to larger data volumes and wider use cases. Implement additional security measures to protect sensitive data, intellectual property, and generated outputs at scale.
- Continuous Monitoring and Improvement (ongoing): Implement advanced monitoring tools and alerts to detect performance issues, data anomalies, or potential biases promptly. Establish monitoring systems to track the performance and usage of generative AI solutions in production, identifying opportunities for optimization. Continuously iterate on generative AI models and algorithms based on real-world feedback and evolving business requirements, aiming to improve performance and ROI. Schedule regular model retraining using fresh data to maintain accuracy and adapt to evolving requirements.
- Ensure Ethical and Responsible AI (ongoing): Develop and adhere to ethical guidelines for the development and deployment of generative AI solutions, ensuring fairness, transparency, and accountability. Implement measures to protect sensitive data and ensure compliance with relevant data privacy regulations. Integrate techniques to identify and mitigate biases in generative AI models, promoting fairness and inclusivity. Conduct periodic reviews and assessments of the generative AI strategic roadmap to track progress, identify challenges
4 Core Pillars of GenAI Strategies
Planning a GenAI strategy is an iterative approach, as it takes time to understand which initiative to adopt and how it aligns with your business goals.?
Gartner explains 4 core pillars to focus on while curating your GenAI initiative.
1. Vision
Setting clear goals organization-wide is important for collective GenAI adoption. Businesses must define AI objectives that align with their business goals. Everyone must understand how GenAI will drive those goals, what outcomes you would expect, and KPIs to measure success at every stage.
Having a clear vision will help you fund the right use case. You must also consider cost factors while budgeting your AI strategy, as 50% of organizations abandon their efforts due to increasing costs, risks, and complexity.
Choose the right AI technology to support your vision.
For example, if your goal is to improve customer satisfaction. You could use GenAI to conduct customer behavior analytics to increase proximity to the customer. For that, you can leverage GenAI tools like- virtual assistants, chatbots, AI-based security, and more.
To measure the success of your goal, you can see the engagement rate, cost required, high retention, revenue growth, and others.
2. Value?
Now the question comes to how you capture value. You must identify barriers to your GenAI initiative. It includes coming up with potential solutions and delegating responsibilities, and actions. This is where the C-suite (CIO, CFO, and CDO) and leadership come into the role. They are the core people who analyze the initiative, fund it, and ensure implementation throughout the organization.
They discuss what GenAI technology would fit the best to achieve goals, find potential solutions for barriers, and foresee development and execution.
3. Risks
As AI advances, it comes with new types of risks that businesses must focus on to ensure they drive value-driven results for the initiative. Some common risks associated with AI include- hallucinations, biases, deepfakes, and misinformation.?
Below are different types of risks.
- Regulatory Risks. Here, it would be best to understand an evolving regulatory landscape. Foster collaboration between AI practitioners and legal, risk, and security teams, and establish an AI governance office for independent audits with the help of CIO/CTO and CRO.
- Reputational Risks. Recognize threats to AI posed by both internal and external actors. Enhance security controls, data integrity, and AI model monitoring, and utilize external resources to secure AI systems with the help CIO/CTO.
- Competency Risks. Align AI strategy with cloud strategy. Develop a technology roadmap to modernize data and analytics infrastructures, and Initiate a startup accelerator program to reduce technical debt and drive innovation, with the help CIO/CTO.
Another risk is Copyright infringement. According to a survey, 70% of enterprises cite copyright issues as their main reason for avoiding generative AI. This is because GenAI models are trained on internet materials that often include copyrighted content, leading to potential legal complications.
How do you mitigate AI risks across the organization?
4. Adoption
Check the feasibility of each use case and work on the best GenAI initiative that brings value without failing. Businesses must focus on the following aspects to successfully execute the GenAI initiative.?
- What problem is the business trying to solve?
- Who will primarily use the technology?
- Which business process will use the AI technique?
- Which experts can help develop the solution?
- How will the impact of the technology be measured?
- How will the technology's value be monitored and maintained, and by whom?
Before implementing the GenAI strategy, you must experiment with it first. Build use cases, gather skills and data, choose the right technology, and educate the organization.
Checking feasibility is important to reduce unnecessary investments and efforts. Once you decide on the perfect use case, put your efforts there.?
Real-world Example
- Accenture continues to expand its capability to assess, design, implement, scale, and responsibly monitor AI systems to help its clients across industries drive value and growth. They have appointed Arnab Chakraborty, as its first chief responsible AI officer with a focus on enabling clients to innovate AI safely and prepare for new opportunities that AI will bring in the decades ahead.
- Fujitsu is integrating GenAI into enterprise infrastructure and operations to support their customers as software engineering assistant, support and operations assistant, data insights assistant, and more. They have created a comprehensive suite of end-to-end services, ranging from consultancy and strategic development to practical AI solution deployment through the innovation platform, Kozuchi.
There are more to this list of companies that have successfully implemented AI and GenAI to improve customer experience in business operations.?
Final Thoughts
AI and GenAI are crucial. Implementing it blindly is not a smart move. Many businesses are experimenting and some are implementing. If you are starting your AI initiative this year, you must learn from the experiences of companies and make better choices.?
Build a team of skilled AI professionals, and help to make AI awareness across the organization. Start making small goals to achieve AI success and envision it for the long term. Work on these 4 core pillars and see where GenAI fits within your organization and help improve its efficiency.?
