Generative AI strategy: Best practices to implement 

AI technologies (machine learning, large language models, natural language processing, neural networks, and others) have made a robust advent into multiple spheres of human activities, revolutionizing customary approaches and business processes on a large scale. As a Gartner poll reveals, generative artificial intelligence is considered the most popular know-how among the latest AI tools an average organization adapts in its pipeline routine.  

The scope of Gen AI’s practical applications across various verticals is so vast that companies that lag behind in the generative AI journey risk being left out of the picture in the contemporary highly competitive business and tech landscape.  

However, harnessing the immense potential of generative AI initiatives is a tall order that can’t be performed on a hunch and in a random fashion. The successful implementation of generative AI solutions largely depends on a comprehensive approach to their onboarding and an effective generative AI strategy developed by key decision-makers and senior leaders of enterprises.  

This article clarifies the essence and major benefits of a generative AI strategy, offers a plan for devising it, showcases examples of organizations that successfully leveraged generative AI capabilities to boost business growth, and outlines prospects of Gen AI applications in industrial settings. 

Generative AI strategy made plain

A generative AI strategy is a detailed and well-thought-out set of internal policies honed to implement AI technologies into an organization’s pipeline operations to enhance the company’s operational efficiency and support its sustainable growth. However, it is not only about embracing cutting-edge tools as AI complements existing systems the enterprise relies on in its digitally-driven workflows. The strategy presupposes establishing clear guidelines for integrating generative models in all core business processes. This allows stakeholders to balance innovation with conventional approaches, ensure transparency in performing everyday tasks, boost workforce productivity, improve supply chains, and more. 

What are the staples of a compelling Gen AI strategy? 

Strategic alignment with business goals 

This is the point of departure of a strong strategy. You should shape a clear vision of your enterprise’s strategic goals and understand how the new technology can be instrumental in it. To do that, you should take four steps. 

At first, you should come up with a list of use cases (upgrading customer support, creating personalized content, streamlining personnel management tasks, etc.) that will benefit most from opportunities presented by generative AI tools. Then, you should check how solving these specific problems contributes to reaching the company’s broader goals and integrate gen AI solutions into enterprise-wide development strategies. Finally, you should determine investment areas where the onboarding of generative AI will usher in novel business models and bring greater ROI.  

Actionable roadmap 

To draw it, you should first assess the roster of your tools and systems as well as business data maturity within the enterprise database. Another mission-critical component of the organization’s readiness to embrace generative and traditional AI is your employees’ technical skills in this field.  

Having a 360-degree view of your current position, you can pinpoint gaps and inadequacies in the existing technologies or workforce expertise and devise a structured action plan containing a prioritized sequence of steps, resources needed to accomplish them, and specific metrics to gauge the efficiency of outlined measures. 

Optimized technological ecosystem 

You can’t hope to succeed in harnessing generative AI if you focus exclusively on embracing the novel technology. Instead, your strategy should create a scalable, agile, and flexible digital infrastructure where Gen AI tools are pivotal. Such an ecosystem should have a modular architectural design, allowing for easy replacement, fine-tuning, and updating components, play well with all disruptive technologies (not only generative AI), and leverage highly adaptable serverless and cloud-native solutions on a large scale.  

While implementing generative AI, it is vital to remember that you should employ prototyping cautiously because the pilot sample in such projects doesn’t factor in production environment complexities. That is why problems will likely be encountered when the prototype is scaled to a fully functional product.  

One more aspect to bear in mind is the question of cost. As with any other cutting-edge know-how, generative AI is a big-ticket item. You should carefully exercise efficient cost management and direct your major investments to improve productivity in the most revenue-generating operations. 

Effective data governance 

Since the accuracy of generative AI models critically depends on adequate, prompt engineering and training data quality, data governance becomes the centerpiece in the right strategy for implementing Gen AI. It goes beyond providing rock-solid data security and quality and paves the way to responsible AI employment. This means creating a digital and physical space where the ethical use of customer and proprietary data is the topmost concern, data privacy laws are upheld, and intellectual property rights are respected. 

Several organizational and ethical guidelines can help you build such an environment.  

• Develop a tailored and straightforward data governance framework that manages all data handling aspects (quality, security, metadata) and allows for incremental improvements. 
• Maintain an ongoing and iterative stance, enabling you to update security measures, keep abreast of evolving regulations, and react to challenges on short notice. 
• Be ready for trade-offs within your operating model that should envisage a healthy balance between embracing state-of-the-art innovation and retaining necessary control to avoid data leakage and ensure regulatory compliance. 
• Focus on people and processes, not the technology, since the efficient usage of generative AI is possible only when the personnel steers by prescribed rules and acts within well-established responsibilities.  

Empowered workforce 

Any strategy is doomed to failure if it lacks qualified people to implement it. You should define roles and responsibilities for the personnel who will handle generative AI tools and provide training to equip them with technical expertise in leveraging state-of-the-art solutions and in-depth knowledge of the ethical use of AI. Moreover, you should design an efficient operating model and ensure adherence to enterprise policies and legal framework.  

The core team necessary for the execution of the outlined generative AI strategy includes an AI engineer (who will choose the appropriate large language models, fine-tune them, and perform prompt engineering), a data engineer (who will prepare data for LLMs), and a solution architect (who is responsible for defining the system’s architecture and its integration with other software). 

While assembling your generative AI implementation team, it is important to remember that you are planning not only for current but also for future talent needs, ensuring the staff is prepared to navigate opening opportunities and address upcoming challenges in this highly dynamic technology realm. 

You can proceed to its implementation as soon as you have all these non-negotiable components of a successful generative AI strategy.  

Developing a generative AI strategy: Key guidelines 

When consulting on embracing generative AI, we recommend enterprises follow a four-step implementation algorithm. 

Step 1. Assess your business needs 

Onboarding Gen AI just because everybody else does typically results in wasting your time and money, to say nothing, fundamentally disappointing your employees in the value of innovative tools. Instead, you should conduct an out-and-out audit of your current workflows and operations to identify aspects (existing content enhancement, new content creation, boosting customer experience, revolutionizing customer service, augmenting marketing campaigns, automating routine tasks, accelerating software product development, etc.) that can be improved by harnessing generative AI tools.  

Since AI-powered solutions are not chump-change items, you should prioritize high-value use cases that generate the most profit and are sure to yield the greatest ROI.  

Step 2. Select proper AI tools and technologies 

Business goals defined at the previous stage condition the choice of the tech stack for their achievement. When you understand what kind of generative AI models and tools you will utilize for your use cases, search for relevant offers in the respective market niche and create a list of technologies to consider. How to choose the best tools among multiple options?  

First, you should hunt for features to help you solve your tasks. Then, look at UI and assess the learning curve of each tool to see whether your technical expertise is sufficient for handling them. Inquire about such solutions’ scalability, integration potential, and customization opportunities. Compare pricing models offered by vendors, paying attention to the scope of features and usage limits you will receive for your money. Finally, to get a taste of leveraging the tool, explore demo versions or free trials and determine whether they meet your expectations concerning functionality and usability.  

Step 3. Draw the implementation plan 

This is the basic document that will ultimately determine the success or (God forbid) failure of your generative AI strategy, so you should be meticulous about it, adopting a holistic and cross-functional approach to its creation and aligning it with the organization’s business objectives.  

The plan should contain explicit implementation milestones and a realistic timeline for each phase of the process, guidelines and standards for data usage, model training, generated content monitoring, oversight mechanisms, the list of stakeholders with clearly defined roles and responsibilities, resources the implementation will require, performance metrics, expected deliverables, risk management measures, and expansion prospects.  

It is better to start small, carrying out the plan on a narrow-scope pilot project to understand the impact and tweak your approach with an eye to large-scale initiatives.  

Step 4. Monitoring and optimization 

You can’t rest on your oars once the generative AI solution is implemented. Your specialists should constantly monitor and evaluate its functioning, track regulatory compliance, set up feedback loops, introduce adjustments and updates, and ensure it works smoothly across its use cases.  

Alongside taking care of the technical aspects of the software operation, you should tackle the human side of the project. It means fostering enterprise-wide cross-functional collaboration among the staff, promoting knowledge sharing and continuous learning, encouraging organizational change management, and encouraging an innovation culture.  

When thoroughly devised and adequately implemented, an efficient generative AI strategy ushers in numerous boons for companies in various industries.  

Benefits of generative AI strategy for businesses

What do companies obtain by developing a well-thought-out, straightforward, and far-reaching strategy for making generative AI a pivotal element of their IT environment? 

Strategic vision and risk management 

A comprehensive generative AI strategy is a documented guide that serves as a reference point for addressing challenges and adapting to the company’s everyday routine changes. Thanks to it, employees can solve issues and seize new opportunities, which allows the company to obtain agility and hone a competitive edge over its rivals whose generative AI initiatives lack consistency and concerted effort.  

Besides, a special section of the strategy focuses on identifying and forestalling various risks (ethical, cybersecurity, compliance, etc.), thus arming personnel with a contingency plan they should adhere to in case something goes wrong.  

Data strategy 

A generative AI strategy is not only about onboarding a new technology. Since AI thrives on data, enhanced data management is a bonus by-product of the strategy yields. Its general impact on the organization’s data infrastructure consists of bringing order to the internal database and improving its structure. Plus, the document stipulates measures aimed at providing exclusive data quality and security, augmenting data accessibility and visibility, and ensuring the reliability of data analysis outcomes.  

Revenue increase 

When composing a generative AI strategy, organizations identify high-value workflows, directing the lion’s share of their efforts and resources to those areas. As a result, they capitalize on profitable use cases, increasing revenues and boosting the cost-efficiency of the entire enterprise.   

Clear leadership 

Embracing novelties is possible only through consistent efforts of transformation drivers. The decision-makers and executives implementing the strategy become generative AI advocates, ambassadors, enablers, and promoters. Moreover, their deep immersion into the domain increases their awareness of the technology’s nuts and bolts. In contrast, executives’ visible commitment encourages others to share their enthusiasm and join the endeavor the whole nine yards.   

Innovation culture 

Organizations harnessing state-of-the-art know-how are on the winning side, not only technology-wise. An all-encompassing generative AI strategy institutionalizes experimentation, thus unlocking hidden creativity, fostering an innovative mindset among employees, inspiring them to explore new functions and applications of Gen AI tools, and creating a universal buy-in across the company regarding disruptive advancement in various sectors.  

Scalability and sustainable growth 

Any strategy presupposes thinking ahead and bolstering foresight in decision-making. Being guided by the provisions of the generative AI strategy, companies build for scalability with the organization’s future growth in view. Besides, a long-term plan considers AI’s multiple implications, enabling businesses to introduce sustainable practices that will benefit both the enterprise and society in the long run. 

Armed with a workmanlike strategy, companies across various verticals implement generative AI solutions that bring them value. 

Successful generative AI implementations showcased

Let’s find out how blue-chip brands stand to gain from onboarding Gen AI tools. 

Walmart 

This global retail corporation is one of the technological trailblazers in the industry, with AI implementation considered a pivot. Intelligent algorithms on which generative AI hinges have allowed the company to reinvent its supply chain management by accurately forecasting demand, increasing timely product delivery, optimizing inventory, improving transportation route and fleet utilization, boosting risk assessment, and augmenting overall efficiency across multiple branches and outlets.  

Unilever 

Staying ahead of the curve in the beauty industry requires an all-out human effort reinforced by cutting-edge technologies. Unilever succeeds in this competition not in the least thanks to its robust Gen AI platform that optimizes formulations, forecasts product performance, and generates new product ideas relying on ingredient data, consumer preferences, and market trends. 

Toys ‘R’ Us 

One of generative AI’s most wanted capabilities is content creation in various formats – from texts and images to videos. The popular toy brand Toys ‘R’ Us leverages this potential on a great scale. The company even employed a new text-to-video tool by OpenAI to create a promotional film at the Cannes Lions Festival in France last summer.  

Whether you are a global brand with a million-dollar-strong budget or a startup on short commons, being poised for success spells paying close attention to current trends in generative AI’s commercial usage that will stay relevant in years to come. 

Generative AI in enterprise settings: Identifying prospects

As a qualified expert in developing generative AI solutions, DICEUS keeps its fingers on this technology field’s pulse, watching the following trends unfold and maturation.  

Conversational AI 

AI-driven chatbots and virtual assistants will dominate the customer service sector in the foreseeable future. The improvement of Natural Language Processing mechanisms will empower them with greater abilities to understand versatile voice commands, maintain human-like conversations, and handle complex queries. 

Human-level performance 

Generative AI will come closer to humans not only in its ability to speak but also in completing some technical tasks. McKinsey and Company predicts that by the end of this decade, it will reach an average level of human performance. By 2040, generative AI will compete with the top 25% of human personnel who are now doing these jobs.  

Hyper-personalization 

As generative AI models become more sophisticated, Gen AI tools better identify granular preferences and patterns, allowing for tailoring products and services to the needs of individual consumers. Such capabilities can be helpful in almost any industry – from retail and insurance to banking and education.  

Multi-modal AI 

Machine learning and deep learning algorithms will allow generative AI to simultaneously process information from various sources and in different formats (texts, images, videos, audio). The implications of these advancements will be the most significant for the educational and retail industries, where they will pave the way to greater personalization of services. 

Agentic AI 

This technology can dynamically respond to environmental changes and autonomously adjust its actions. When bolstered by generative AI, it can swiftly and accurately translate insights into meaningful steps without human intervention, thus contributing much to robotic process automation of repetitive tasks.   

Decentralized AI 

When blended with blockchain technology, generative AI can attain higher transparency and security since it won’t rely on centralized data storage. Such advancements will allay widespread concerns about the privacy of training and personal data utilized by Gen AI models.  

AI democratization 

As the technology gets more traction, it will become more affordable, with open-source generative AI models competing with proprietary ones. Consequently, smaller ventures will get wider access to low-cost Gen AI tools and leverage them for their narrow-scope projects and use cases. 

AI for creative industries 

The entertainment realm will witness a greater influx of generative AI initiatives across its various branches (TV, filmmaking, video games, art, crafts, you name it). Gen AI tools will broaden their application scope, particularly in design, streamlining, and facilitating creative content production.  

Generative AI in healthcare 

Medicine has relied heavily on human expertise and intuition to diagnose and outline treatment plans. Down the line, Gen AI-based tools will become more instrumental in analyzing relevant patient data and suggesting the proper treatment and care in specific situations. Besides, generative AI can facilitate drug discovery by exploring new chemical compounds and supporting advanced research.  

Generative AI has tremendous potential in multiple industries and use cases. However, developing and implementing an efficient Gen AI strategy is challenging for companies lacking professional expertise in this domain.  

DICEUS’s seasoned professionals possess adequate theoretical knowledge and a broad range of hands-on skills to perform a full-cycle development of a robust generative AI strategy and assist with its implementation. Contact us to bring your company to the technology frontier and sharpen your competitive edge.  

Key takeaways

Generative AI is a disruptive technology utilized by organizations across multiple industries in numerous use cases. To get the most value from it, you should have an efficient implementation strategy. It relies on five pillars: alignment with strategic business goals, actionable roadmap, robust technological ecosystem, adequate data governance, and knowledgeable workforce.  

When you develop a Gen AI strategy for your enterprise, you should obtain a clear vision of your business needs, select proper tools, come up with a detailed implementation plan, and exercise post-deployment monitoring and optimization.  

A successful generative AI strategy is one that enables advanced risk management, offers a reliable data strategy, fosters leadership and innovation mindset, provides scalability and sustainable growth, and paves the way to the increase of revenues. It can be devised only by a qualified AI development company that knows the nitty-gritty of the domain and steers by the latest trends in the niche. 

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