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November 20, 2023

Generative AI Strategy Is a Must-Have: How to Build It

Author:




Edwin Lisowski

CSO & Co-Founder


Reading time:




12 minutes


In the ever-evolving landscape of Artificial Intelligence (AI), generative AI stands as the cornerstone for innovation, transformative growth, and long-term success. A recent analysis by McKinsey shows that implementing generative AI technology can increase sales productivity by about 3-5% of the current global sales expenditures [1].

However, despite the many benefits of generative AI, some businesses are still struggling to integrate the technology into their operations effectively. This is often due to a lack of a clear generative AI strategy and a deep understanding of the technology.

Read on to learn more about what generative AI is, how to build a robust gen AI strategy along with capabilities that you can take advantage of.

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Understanding generative AI

This term refers to algorithms or models that can be used to produce high-quality images, texts, audio, and other content based on the data they’re trained on. These models typically work by predicting the next piece of data in a sequence to produce highly realistic content that mimics human creativity – this could be the next pixel in an image or the next word in a sentence.

Although gen AI was used in chatbots as early as the 1960s, it was only until 2014, after the introduction of generative adversarial networks, that the technology could create authentic content [2]. Fast forward to today, and many organizations have created their own foundation models used to perform various tasks.

For example, GPT-3, a foundation model trained on massive datasets, can be adapted to perform tasks such as language translation, text summarization, text generation, question answering, and even sentiment analysis. On the other hand, OpenAI’s DALL.E can be adapted and fine-tuned to generate realistic images in a wide variety of styles. It can also be used to rearrange and manipulate objects in images and even expand images beyond their original size.
For such a model to be considered successful, it must fulfill the following requirements:

Quality: A great gen AI model must be able to generate high-quality output that is realistic, useful, and informative. In speech generation, the model should generate high-quality human-like speech. In image generation, a good model should generate realistic images that are indistinguishable from natural ones.
Diversity: The model must be able to generate a wide variety of outputs without sacrificing output quality.
Speed: The model must be able to generate outputs quickly and efficiently for a better user experience. Additionally, fast gen AI models are more scalable than slow ones. Therefore, they can be used to process large datasets and generate high-quality outputs in real-time.

How to build your generative AI strategy: A step-by-step guide

Here are steps to follow to build your own strategy:

How to build your generative AI strategy infographic

Assess opportunity areas

The first step when constructing a strategy is to identify areas where the technology will drive the most value in your organization. That said, you need to create a list of application areas with the highest value potential based on company objectives, priorities, goals, strengths, and pain points.

You also need to set up cross-functional teams that will work together to determine the possibilities of a robust strategy in areas such as customer service, product development, quality assurance, marketing, research and development (R&D), sales, finance, IT, and many others. Proceed to create a list of the various value drivers that will help enhance your organization’s operations and processes.

Afterward, you need to sequence your organization’s generative AI priorities based on implementation complexity, value potential, the availability of foundational resources, and time to value realization. The main idea is to focus primarily on quick wins, where the AI strategy will help fix pressing problems in the company.

For example, if your organization has been struggling with long wait times in your customer support department, consider prioritizing generative AI-powered chatbots and virtual assistants. These will help you respond to many customer queries simultaneously, thus taking the pressure off your customer service agents. Such an application area will help deliver quick returns to your organization before you start focusing on bigger and more ambitious projects.

Notably, it’s important to ensure that all stakeholders are on the same page regarding generative AI priorities. If the Chief Technology Officer (CTO) and the Chief Marketing Officer (CMO) are not in agreement regarding these priorities, it will end up derailing the entire project. Therefore, before the project reaches critical stages, you need to ensure all stakeholders agree on the highest potential priorities.

Chart an adoption roadmap

Once the high-potential opportunity areas have been identified, the next step is to map out a phased rollout plan across the organization. Ensure this adoption roadmap aligns with strategic priorities and manages potential risks. Trying out simple use cases such as marketing will allow your organization to gain much-needed expertise before integrating the technology into bigger and broader processes. Most importantly, deploying such a strategy first internally will help mitigate external risks in case problems arise along the way.

The adoption roadmap should also indicate for every pilot project when to expand, modify, or even abandon based on the impact achieved. For example, if a generative AI model delivers fragmented value, you need to keep iterating and testing before deploying it organization-wide.

It’s highly recommended to take an agile approach when it comes to charting an adoption roadmap. Start by deploying pilots, refining them based on customer feedback received, and scaling iteratively until you attain your desired goals. Notably, moving too slowly with generative AI will result in diminished value, while moving too quickly will increase risks. The only way to find the best place for adopting artificial intelligence is by strategic road mapping.

Ensure responsible governance

There is no denying that generative AI can easily raise several ethical concerns such as bias, disinformation, and misuse of personal data. That said, developing a robust governance framework will ensure the use of generative AI models is ethical and responsible.

The ideal AI strategy must establish responsible governance to help mitigate risks, maintain transparency, and harness the technology’s full potential. Governance entails ethical deployment and the use of robust monitoring systems to assess the performance of the strategy.

For example, if AI will be used to create customer communications, ethical guidelines, and governance frameworks must be used to address concerns related to potential biases and data privacy issues. Human oversight is also important in the governance of generative AI models because it helps increase the accuracy and safety of these systems and uphold human values.

That said, you should make governance an important part of your AI strategy from the start. This way, you can be assured of safe, fair, and effective implementation of this technology in your organization.

Build capabilities and minimize risks

Building your ideal generative AI strategy will give rise to various capability gaps that must be addressed across the technology, processes, and skills. As a result, you need to invest in various key aspects such as data management, MLOps engineering, scalable infrastructure, change management, risk management, and performance metrics.

The accuracy and reliability of generative AI models mainly depend on the quality of data. Therefore, you need to address all gaps to ensure your generative AI strategy has access to high-quality and well-organized data.

MLOps engineering ensures the efficient deployment, management, and optimization of generative AI models. This leads to enhanced accuracy, speed, and affordability. On the other hand, performance metrics are crucial in evaluating the overall quality and performance of your AI models.

Define progress metrics and tracking

Generative AI is an ever-evolving field. Therefore, you need to continuously monitor and track your gen AI strategy to ensure you’re getting the most value out of your investment. The best way to track the performance of your strategy is by using performance indicators such as sales conversion rates, resolution time, First Response Time (FRT), agent call time, Customer Satisfaction (CSAT) scores, and Continuous Learning Index (CLI). [3]

By tracking these performance indicators, organizations can easily refine their strategies and increase their chances of success.

Interested in learning more about Generative AI development company and its services? Reach out to our team, and we’ll guide you in creating a robust generative AI strategy.

Why is a generative AI strategy essential?

Most of the time, adopting any new technology exposes organizations to many challenges. Generative AI is no different. That said, having a robust AI strategy is vital because it helps organizations get the most value out of the technology, minimize risks, and stay ahead of the competition.

Here are other reasons why every organization needs to have a generative AI strategy:

Realize maximum business potential

As aforementioned, generative AI is projected to add trillions of dollars to the global economy across a wide variety of use cases such as marketing, software engineering, and many others. [4] However, this can only be achieved with high-level coordination. Without the proper generative AI strategy, success will be limited, and many resources will be wasted.

A good generative AI strategy can have a great impact on software development and other IT solutions. When generating code, software developers and programmers only need to tell generative AI models what they want to do, and the models will produce high-quality code bits or full functions. Consequently, this helps automate repetitive tasks and accelerate the entire coding process. According to a recent McKinsey study, programmers and software developers are likely to code up to twice as fast with generative AI models. [5]

However, if software development teams start using generative AI models to generate code without the proper strategy or governance, the results are likely to be inconsistent and risky. This is because the teams may end up using models with different data privacy and Quality Assurance (QA) protocols. On the other hand, having an AI strategy in place ensures unified policies and maximum business impact for organizations.

Another McKinsey study indicates that marketing and sales, product and service development, customer service, and software engineering represent about 75% of the total annual value from generative AI use cases. [6] That said, the proper generative AI strategy should identify organization-wide priorities in high-potential areas like these. Such strategies will help organizations realize the maximum value potential and increase their revenue.

Gain a competitive edge

In today’s ever-evolving business landscape, organizations are moving quickly to maximize generative AI capabilities. Companies that will not adopt generative AI in the near future risk falling behind as the technology has become a must-have in most modern industries. As a result, building an AI strategy is now more important than ever, especially if you want to compete.

For example, chatbots and virtual assistants are revolutionizing customer service through automation. These chatbots can easily process customer queries and deliver appropriate responses at scale. By reducing response time and delivering consistent customer service, chatbots can help improve overall customer satisfaction and loyalty. This level of automation also leads to operational efficiency and cost savings for businesses.

On the other hand, organizations without AI chatbots and virtual agents will likely struggle to provide the level of service their customers expect. This will result in low conversion rates and less revenue for the businesses. To avoid this, many organizations have already started building robust strategies to make AI an integral part of their business operations. These organizations have learned that lacking such a strategy will make them lose their market share to competitors.

Read also more about Generative AI Implementation: A step-by-step guide

Proactive generative AI risks management

Despite all the benefits generative AI offers, the technology comes with several risks, including embedded bias, misinformation, unique cyberthreats, privacy concerns, over-automation, and hallucinations. Generation AI strategies allow organizations to monitor and manage these risks preemptively.

For instance, chatbots and virtual agents understand and respond to customer queries in real time, but they still lack the creativity, judgment, imagination, and intelligence of a human being. This is where a generative AI strategy comes into play. These strategies clearly define where human customer support agents should take over from the chatbots and where the chatbots can self-operate.

Additionally, generative AI technology can be used to create personalized marketing content for audiences based on their preferences and behavior. However, generative AI models still need human involvement to ensure the content generated is safe and unbiased. As such, ethical guidelines and rules need to be incorporated into AI strategies. This way, organizations can mitigate bias, toxicity, and harmful outputs that can damage their reputation.

Facilitate smooth workforce changes

Although generative AI will not cause widespread job loss, it will transform job roles and required skills in various industries. This makes it necessary to have a strategic plan that will smoothly migrate workers amid work changes instead of causing disruption. In cases where generative AI models take over repetitive and monotonous tasks, employees can focus their time and effort on more important work. However, executing this successfully requires extensive planning and training.

With the help of generative AI strategies, organizations will be able to transition their workers to their new roles without any complications. These strategies help facilitate upskilling and retraining, allowing employees to adapt to more advanced roles and advance their careers.

Final thoughts

Generative AI is a groundbreaking force that has the potential to revolutionize many industries and transform the future. However, to stay ahead of the curve and reap the benefits of this technology, organizations need to invest in a robust and effective generative AI strategy. Such a strategy will help them implement and integrate generative AI technology into their existing business processes.

By following the above guide, you’ll be able to build a robust strategy that aligns seamlessly with your unique business needs and helps you achieve your long-term business goals.

References

[1] Mckinsey.com. The Economic Potential of generative AI. URL: https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/the-economic-potential-of-generative-ai-the-next-productivity-frontier. Accessed November 10, 2023
[2] Techtarget.com. The Evolution of Chatbots and generative AI. URL: https://www.techtarget.com/searchcustomerexperience/infographic/The-evolution-of-chatbots-and-generative-AI. Accessed on November 10, 2023
[3] Linkedin.com. Reimagining Technical Support KPIs. URL: https://www.linkedin.com/pulse/reimagining-technical-support-key-performance-kpis-ai-priyadarshi/. Accessed on November 10, 2023
[4] Mckinsey.com. The Economic Potential of generative AI. URL: https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/the-economic-potential-of-generative-ai-the-next-productivity-frontier#business-value, Accessed on November 10, 2023
[5] Mckinsey.com. Unleashing Developer Productivity With generative AI. URL: https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/unleashing-developer-productivity-with-generative-ai. Accessed on November 10, 2023



Category:


Generative AI