Lessons Learned from Generative AI Early Adopters

Early users of generative AI have discovered the technology’s strengths. With fewer companies remaining on the far side of the AI adoption curve, now is the time to implement these solutions in your companу.

However, generative AI adoption without guidance can lead to expensive mistakes. Luckily, forward-thinking companies had plenty of tіme to grapple with integration challenges and technical limitations. You can and should learn from them.

As one of the early adopters, our software development company has helped numerous clients implement the latest AI technologies. In this article, we will share tips to help you adopt the technology with minimal friction.

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Understanding the AI Adoption Curve

The AI adoption curve illustrates how organizations take on generative tools over time. Based on the willingness to embrace AI-based systems and tools, we divide companies into five groups:

• Innovators. These companies are eager to invest in emerging generative AI tools. They are willing to take risks and experiment with new technologies.
• Early adopters. Forward-thinking leaders who recognize the potential of new technologies and adopt them to get ahead of the competitors.
• Early majority. This group uses new technologies after clearly seeing their benefits and limitations. They make up a large part of the market and advocate new tools.
• Late adopters. These organizations wait for new technologies to become well-established before implementation to avoid risks.
• Laggards. This group is most resistant to change and prefers older-generation AI technologies until the market forces them to innovate.
  

McKinsey's The State of AI in Early 2024 report shows a 72% surge in the adoption of generative AI tools worldwide. About 65% of respondents say their organizations use generative AI regularly in at least one business function—32% more than a year before.

It’s safe to say that late adopters and laggards are a minority. Compared to the usual AI technologies, generative tools resemble a line rather than a curve. The farther you are on the line, the harder it is to implement the technology.

The rise in adoption shows that more businesses see its value in different areas. And that’s not the only takeaway. The earlier you implement generative AI, the more competitive advantages you will enjoy, especially finance-wise.

Benefits Experienced by Early Generative AI Adopters

Companies that started using generative AI early have identified tangible positive changes and potential future impacts.

• Revenue growth. Enhanced customer insights and personalization contribute to profitability. LTIMindtree’s 2023 Survey The State of Generative AI Adoption shows that 43% of early adopters have increased their revenue by 20% or more.
• Elevated customer experience. Generative AI models help companies generate more insights from data and tailor their products, services, and marketing strategies. Context-aware chatbots enable more engaging interactions, increasing customer satisfaction.
• Fast content generation. AI adoption allows companies to produce more documents, reports, and code. Aside from generating redundant data, gen AI tools are useful for ideation, brainstorming, and content creation.
• Lower operational costs. About 62% of LTIMindtree's survey respondents foresee a 5-20% cost reduction due to generative AI. Moreover, about 8% of innovators and early adopters reduce up to 40% of their operational expenses.
• Operational efficiency improvements. Productivity improvements come primarily from automating redundant and manual tasks. The 2023 Navigate the Gen AI Revolution report confirms that 59% of American companies increase operational efficiency by up to 20% with generative AI.

Case Studies of Successful Generative AI Adoption

Generative AI adds value in many ways, but we can distinguish four key categories:

1. Automating repetitive tasks.
2. Personalizing experiences with chatbots, data-based analytics, and targeting.
3. Adapting to specific environments through retraining
4. Generating ideas, code, and original content

Let's look at how companies use generative AI tools across industries.

Retail and consumer goods

About 40% of early adopters of generative AI represent retail and manufacturing sectors, where this technology excels at automating repetitive work.

Retail companies use generative AI to anticipate consumer preferences and enhance personalization. Machine learning (ML) models offer dynamic web interfaces, personalized product designs, and tailored recommendations. Meanwhile, AI-based chatbots and voice assistants handle customer queries.

Example:

• Walmart adopted an AI chatbot that provides answers to employees and customers. The pilot project saved an average of 1.5% in costs.

Manufacturing and energy

In manufacturing and energy industries, generative AI excels at predictive maintenance. The ML models analyze real-time and past IoT data to forecast when assets need repairs, minimizing the risks of unexpected downtime.

Generative AI helps improve product design and planning. Manufacturers use it to create custom 3D models, experiment with design, and recommend materials. Energy companies use gen AI to optimize drilling plans, assess environmental impact, and create detailed subsurface models.

Example:

• CITIC Pacific Special Steel predicts blast furnace operations in real time. They increased throughput by 15% and reduced energy consumption by 11%.
• ACG Capsules deployed an AI copilot that cut repair time and technical onboarding to nearly 40%. The technology only took five weeks to develop and deploy.

Healthcare

Electronic Health Record (EHR) management automation reduces administrative burden in healthcare. Gen AI can automatically recognize handwritten notes, extract information from patient records, and fill in forms.

In pharmaceutical research, AI adoption helps design new molecules and virtual screening to accelerate the discovery of new drugs. AI is also used to train healthcare professionals, create 3D educational anatomy images, and simulate medical procedures.

Example:

• Sutter Health uses Abridge's gen AI to improve doctor and patient experience. Healthcare practitioners use the system to create detailed summaries and instructions based on conversations with patients.

Finance and banks

Financial companies use generative AI to detect fraud. Advanced systems can identify patterns to spot anomalies that signify fraud scenarios. Combined with synthetic data generation, companies can test new anti-fraud measures without the risk of revealing sensitive customer information during training.

AI adoption can help identify issues with tax and compliance policies, saving time and reducing human error.

Example:

• 57% of banking and financial CEOs believe that having advanced generative AI will provide a competitive edge, with 66% willing to accept significant risks to boost productivity through automation.

Entertainment

The entertainment industry uses AI to come up with new ideas, customize stories for different audiences, and create subtitles from audio. Marketing departments rely on AI-based recommendation engines and virtual assistants to suggest relevant content to customers.

Example:

• Media workers expect their roles to change due to generative AI within the next two years. Animators, VFX artists, game developers, voice actors, and storyboard artists should expect the biggest changes.

Government

Government agencies often use generative AI for public research, as it can analyze large amounts of data to provide reports in various formats. Examples include population censuses, forecasting models, and statistical reports. AI-based anonymization and synthetic data generation tools also help them stay compliant when publishing these materials.

Example:

• Estonia has implemented AI projects in around 60 public services. For example, automated border control gates at Tallinn Airport and the Narva border crossing point use identification algorithms to catch fake documents and process each person in just 15 seconds.
  

About 76% of companies are experimenting with new possibilities. As the generative AI adoption curve flattens and the technology becomes more reliable, companies will find many other ways to use it.

However, early adoption comes with unexplored challenges, and innovators have already faced a few.

Fundamental Challenges of Generative AI Adoption

Organizations using generative AI for a while have moved past the initial excitement. They are now working around several limitations. Let’s highlight the main ones you should know before adopting the technology:

• Infrastructure challenges. According to LTIMindtree, infrastructure challenges are some of the biggest obstacles to generative AI adoption. Implementing AI in your current systems often requires overhauling your infrastructure and legacy applications, which can cause technical problems and disrupt work.
• Shortage of specialized skills. Many businesses can't find or keep employees who know how to engineer practical prompts, maintain training data, and gather insights from the output. Over 57% of companies think that skilled personnel is a crucial success factor for the adoption.
• Data management complexity. Accurate AI models need high-quality data. However, proper training data collection, processing, and management requires time and resources. Data availability is a major challenge for the retail and consumer packaged goods sector.
• Ethical concerns and bias. Nearly 69% of companies struggle with compliance, while 59%—with bias in AI output. Companies must invest in data privacy and model oversight to avoid ethical problems that can diminish customer trust or result in regulatory fines.
• Mode collapse. AI tools trained on inaccurate or scarce data can produce repetitive or low-quality results, making the output less diverse and useful.
• AI hallucinations. Generative AI can sometimes produce incorrect or completely irrelevant data, leading to poor decision-making if not detected. What’s worse, large language models (LLMs) often present wrong information with confidence.
• Implementation strategy gap. Many companies find it hard to match their AI projects with their business goals, which creates a disconnect between what AI can do and what top management expects.
• High costs for late adopters. Setting up and running AI systems becomes more expensive further along the AI adoption curve. You need to invest in the technology, skilled data scientists, prompt engineers, and monitoring. LTIMindtree found that 85% of late adopters worry the most about high expenses.

Early adopters have it more accessible now, having solved some of these challenges. The good news is that laggards can learn from them.

Go-to Strategies of Successful Early Adopters

Early adopters of generative AI have developed effective implementation strategies that you can use to embrace AI.

Identify the use cases

A staggering 80% of companies believe that finding the right use cases—for your customers and internal teams—is crucial for successful AI adoption.
Consider your customers first. Most users fall into six groups based on their AI priorities:

1. Trailblazers adopt the newest AI tools for their work and daily life.
2. Creators rely on AI to boost their creativity and speed up content generation.
3. Investigators search for information, brainstorm, and fact-check with AI.
4. Protectors focus on AI data privacy and transparency.
5. Optimizers view AI as a productivity tool for everyday tasks.
6. Enjoyers seek new ways to stay entertained with AI.
   

Knowing which customers belong to which group, you can identify their specific needs and preferences. As a result, it becomes easier to market to the right audiences and provide more value.

With business users, you must clearly understand how each department can use generative AI at scale. Determine where AI can add the most value: in task automation, customer interactions, or decision-making. Then, evaluate where each department stands on the AI adoption curve, considering skill levels, potential impact on workflows, and existing technology infrastructure.

Start small and scale gradually

Begin by integrating generative AI for smaller, in-house use cases. Try optimizing processes first to ease the burden on your employees.

Same with custom software development projects. It’s best to create your gen AI app for internal use first. Then, you’ll get a controlled environment to test and refine the models while building skills with the technology. The insights you’ll gain will guide you when developing models for customers.

Test, monitor, and evaluate continuously

Design a comprehensive evaluation strategy for data selection, model training, and output monitoring to maintain your AI systems’ quality and reliability. Among other things, you should:

• Implement strict data governance rules to train your AI with high-quality information.
• Develop automated and manual testing processes that cover all scenarios your AI system may encounter.
• Assign groups of testers to test different scenarios to uncover hidden issues.
• Have your data scientists continuously monitor, evaluate, and document the results to refine the model and prevent biases, mode collapse, and hallucinations.

Remember that your model will keep improving throughout testing, and eventually, you’ll gain greater control over outcomes.

Streamline integration with IT infrastructure

Optimizing your infrastructure is a challenge, but these tips can facilitate the process:

• Select the right tech stack. For smooth implementation, you should choose the right stack of generative models, programming languages, data processing tools, and cloud services. ChatGPT, BARD, and GitHub Copilot provide pre-built models for generating textual and visual content.
• Connect with existing apps via APIs. Use APIs to link your in-house apps with off-the-shelf generative models. Open-source generative AI frameworks or libraries provided by third-party services, such as Azure OpenAI, help develop and train gen AI with your proprietary data for specific use cases.
• Use middleware for legacy systems. Create a compatibility layer between old and new technologies with middleware solutions. This will allow older systems to use AI capabilities without major rewrites or disruptions.
• Leverage third-party cloud platforms. Save time and money by using platforms that provide managed infrastructure and tools. Amazon SageMaker JumpStart is one ML model hub that offers frameworks, built-in algorithms, and pre-trained models to build and scale AI apps.

Enable cross-functional collaboration

Successful AI projects involve IT, data science, and business teams. By working together, they ensure that AI initiatives are technically feasible, strategically relevant, and aligned with business objectives.

Encourage using gen AI tools for both creative and routine processes, providing your teams with intuitive tools suitable for all skill levels. Support experiments with various use cases and refine them based on feedback.

Invest in AI talent and training

With a skilled team and proper expertise, you can effectively develop, deploy, and manage generative AI technologies. Here are a few strategies to consider:

• Look for candidates with a proven track record in developing and deploying AI models. Seek out data scientists and prompt engineers.
• Upskill your workforce through programs, workshops, and guidelines covering essential AI concepts and practical applications. Encourage continuous learning and give your employees access to updated knowledge bases.
• Partner with AI consulting firms or universities whose experts can fill knowledge gaps or provide fresh perspectives.
• Establish roles focused on AI, such as researchers, prompt engineers, data scientists, and machine learning specialists.

Ensure data security and privacy

Every company should enforce policies to ensure compliance with privacy regulations and guidelines for ethical AI use.

• Involve your security teams early in the AI adoption process. You’ll minimize potential vulnerabilities if security is prioritized from the start.
• Establish strict guardrails to prevent unauthorized access. Use role-based access control to enable multi-factor authentication for corporate accounts.
• Encrypt data exchanges between systems and gen AI tools. This protects data in transit from interception and unauthorized access, preventing attacks.
• Make sure users are aware when they interact with AI. You should also clearly communicate all artificially generated responses.
• Gather consent to use information in AI learning. Users must be able to opt out of data collection at any time.
• Utilize anonymization and synthetic data generation tools to mask and remove personally identifiable information from training datasets. Doing so helps comply with regulations and reduces the risk of exposing sensitive data.
• Minimize collected data. Collecting only the data necessary for AI training reduces the risk of data breaches and potential damage caused by leaks.

Learn From Others to Simplify AI Adoption

Success in AI adoption hinges on understanding. Identify which tasks to delegate to generative AI, align these initiatives with your business goals, and invest in experts. Selecting the right generative model, programming languages, and data processing tools while securing data and optimizing infrastructure is just as crucial.
These aspects do need specialized knowledge. With the right software partner, they become more manageable.

Consider partnering with experts if you feel unsure or lack experience. Techstack offers comprehensive AI software development services and consultancy to guide you. Explore our blog to learn more, or contact us directly for tailored solutions.