Generative AI: Navigating the Evolution, Adoption Challenges, and Business Impact
If you look at the AI journey from machine learning to Generative AI, you will find that 2000s was the decade when we were moving from analysis and prediction to deep learning. Going forward, with deep learning models, we got voice recognition, vision and AI-speech assistant. Before this era, machine learning was mostly known to automate tasks and identify patterns to generate insights. Fast forward to 2020s, Generative AI has appeared as an advanced form of deep learning – while the chatter around it is still hot, ChatGPT is the winner so far. In just two months, it touched 100 million users, and the euphoria is still not over.
The CEOs and business leaders across multiple industries are showing great interest in generative AI to solve their organisational challenges. That said, we continue to see more discussions on the future of gen AI but a lot of scepticism surrounding large language models have put to rest past 2022, thanks to the initial success of ChatGPT. So, the business leaders looking to gain the strategic advantage are happy with the proof-of-concept and want to translate this powerful technology into tangible business impact.
According to McKinsey's findings, generative AI applications have the potential to contribute as much as $4.4 trillion annually to the global economy.
BCG reports that Generative AI could boost efficiency and effectiveness by up to 50% , yet only 10% of enterprises have these models active in production.
India could increase its GDP by?US$359 billion?to US$438 billion by 2029-30 through Gen AI adoption.
84% of CEOs CEOs recognize the urgent need to invest in generative AI to avoid giving their competitors a strategic advantage.
Despite the apprehensions and discussions about risk factors executives see gen AI as a tool to drive innovation. No wonder investments are scaling across functions – the top three among them are software development, marketing and customer service.
According to one of Gartner experts, “Frances Karamouzis , “Organizations are not just talking about generative AI – they’re investing time, money and resources to move it forward and drive business outcomes”.
Looking at the trends and analysis, businesses ready with their implementation plan will have first-mover advantage- those who have already invested in it and have had early wins are in a stronger position as of now.
Gen AI Adoption: Going beyond the challenges
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Data Challenges
There are various data related and other concerns that organizations face with regard to Gen AI adoption – like creating solutions customized to business requirements, security protocols, pricing, etc.? The first and foremost is data requirements as you would need a huge amount of data to train on. Additionally, the data quality, relevance and structure are key factors besides security and privacy. Hiring a team that adheres to data governance policies, ensures compliance with data protection regulations, and follows secure and transparent data handling practices is important to mitigate the data related risks.
One may come across models performing differently in training and in the real world – this is a case of data shift, which can be prevented by continuously monitoring model's operational performance and further finetuning as per the requirement.
High Costs
Another major challenge is the cost of computational resources. Deploying large and sophisticated generative models at scale require significant infrastructure which is costlier than others AI solutions. Technical expertise along with financial evaluation is the solution to cut down on the risks and ensure effective implementation. Also, the foundation models can be cost-heavy but once you have successfully created one, you can customize them using small amount of data for new tasks without significant investment.
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Business Perspective
Start with a business-driven mindset and give time to developing a clear business case outlining important factors like technical complexity, integration, expected benefits and ROI - start with small-scale projects to demonstrate value. A phased approach including pilot projects will minimize the chances of failures, enable incremental learning and act as a value demonstration opportunity.
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To address ethical and bias issues, our team suggests businesses to have policies in place that give control over biased outputs. A well written policy outlines the ethical guidelines and directions to be followed for the optimal use of gen ai technology across your organization. Investing in responsible practices is equally important to mitigate bias and is crucial for both the end user and your business. A biased model is as good as a system performing at half of its capacity – no business would want this.
There are lot of other factors that may define or control the cost of GEN AI models- majorly you should be having clear answers to following questions:
What can you change with Generative AI?
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Typically, the foundation models are being used for three major activities - to automate, enhance (human performance), execute certain business processes. The topmost value enterprises can expect from generative AI include faster development, improved customer experience and higher efficiency- specifics depend on different use cases. For marketing and sales-focussed functions, integrating generative AI to the workflow adds speed and scale to content and customer-relationship management. Other businesses like healthcare, banking, insurance can also have these advantages, but they must handle the legal and compliance side more rigorously to take maximum advantage of the generative technology.
Generative AI is helping enterprises streamline their processes and upgrading the operations by handling routine tasks more efficiently. Acting as co-pilot for humans, these tools automate repetitive and managerial jobs like summarizing meeting notes, updating documents and spreadsheets, and prioritizing emails, which eventually makes room for the top executives and managers giving more time to strategic thinking. Producing initial draft, getting quick ideas and creative solutions like generating multiple social media captions in some clicks are the wonders we are witnessing today with the gen AI models.
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Analytics & Recommendations
Generative AI data analytics is different from regular data tools in the sense that you can analyze data in real-time. E-commerce and product teams are using these tools to brainstorm new solutions and attaining better customer engagement.
Visual Artifacts
Creating video and images for social media or sales and marketing purpose is fun and quick with gen AI.? Natural-language-driven content creation, AI-avatars and voice synthesis are few of the instances how image and video generation has become more accessible to teams of all sizes.
Coding
Creating new code through natural language was unthinkable five years ago but the current gen AI platforms have made it a cakewalk for developers today. For beginners it’s a great source to learn and deliver development projects more efficiently - for the advanced programmers gen AI is helping investigate and solve complex problems. Meeting deadlines and quality assurance are added returns for any software development business.
Have more questions about Generative AI adoption? Get this playbook to discover everything from getting started with Generative AI to a set of popular use cases across functions and criteria for successful implementations of Generative AI projects.