The Generative AI Revolution: How AI's Newest Wave Will Transform Business
Artificial Intelligence (AI) has revolutionized how businesses operate, and the latest advances in Generative AI and Large Language Models (LLMs) like ChatGPT promise to further transform organisations. As an AI researcher with over 20 years of experience and a PhD in computer science, I am often asked by executives about how these emerging technologies can benefit their companies. In my discussions with leadership teams across industries, I have seen a mix of excitement and apprehension around tapping into the power of AI.
This article provides an accessible overview of Generative AI and LLMs geared towards C-suite leaders exploring these technologies for their companies. My goal is to explain in straightforward business terms what these systems can do, where they excel, and how they may augment human capabilities across an enterprise. I will avoid technical jargon and include concrete examples for context.
The core opportunities lie in three areas: automating routine tasks, generating content and assets, and gleaning insights from data. By the end, you will have a better understanding of where generative AI and Large Language Models fit into a corporate strategy. The possibilities span departments company-wide, from legal to marketing to product development. But while adoption promises considerable upside, integrating these technologies requires meticulous planning around ethics and governance.
The bottom line is that Generative AI and LLMs should be on every executive's radar. Applied correctly, they can drive significant efficiency gains, free up employees' time, infuse creative work with data, and provide an edge over the competition. Let's explore the specifics of how.
Demystifying Generative AI and Large Language Models
If you are not an AI expert, terms like “Generative AI” and “Large Language Models (LLMs)” may sound complex. But the basic ideas behind how these technologies work are fairly straightforward. This section provides a plain English overview of what Generative AI and LLMs are all about.
Neural Networks That Learn Like Our Brains
Generative AI relies on artificial neural networks, which are computing systems inspired by the neurons and connections in our brains. Just like networks of real neurons enable us to interpret the world, artificial neural networks use inputs to make sense of data.
These systems “learn” by analysing tons of examples. For instance, to build a system that can recognise cats, you would feed it millions of cat photos. It would start to detect patterns in the images that define cat features like pointy ears and whiskers. With enough examples, it learns what makes a cat a cat!
Two Key Ingredients: Big Data and Attention
The more data used to train a neural network, the better it becomes at its given task. That is why Generative AI models require massive training data sets. For example, an LLM called GPT-3 was trained on 45 terabytes of text data – that is over a million feet of bookshelf space!
The other key ingredient is attention mechanisms. These help a neural network pay attention to the right parts of input to produce the desired output. It is how the network knows to associate “blue” with “mat” rather than “cat” in a sentence.
Generative AI: Creating Entirely New Content
Unlike traditional AI, generative AI creates completely new content like text, images, audio, and video. It does not just classify something or make a prediction. It generates original outputs by predicting the most probable next word, pixel, or sound based on patterns learned from training data. So, for example, an LLM trained on news articles could write a new article from just a headline prompt.
Realistic Outputs with the Right Training
With massive data and attention mechanisms, generative AI outputs can be surprisingly convincing. An LLM trained on legal documents could draft a believable contract. The possibilities are endless!
The key is providing the model enough relevant examples for the desired task. While not perfect, Generative AI continues to become more sophisticated and useful through advances in neural networks.
Automating Tasks and Processes
One of the biggest ways that Generative AI and Large Language Models can benefit businesses is by automating repetitive, rules-based tasks. This streamlines operations, reduces costs, and boosts productivity.
Legal Workflows Made More Efficient
Consider how Generative AI could transform legal teams. Lawyers spend countless hours manually reviewing contracts, summarising terms, flagging risks, and proposing revisions. This tedious process ties up staff that could be tackling more strategic initiatives.
With Generative AI, a lawyer simply inputs a new contract, and the system can rapidly analyse it. The model reviews clauses, identifies critical details, summarises the terms in plain language, notes any red flags, and suggests changes tailored to company policies.
This automates a traditionally manual workflow, generating in seconds what would take a person hours. Attorneys are freed up to focus on high-value tasks like litigation strategy, negotiations, and client counsel.
According to McKinsey, automation enabled by Generative AI could increase legal productivity by 25-35%. One major law firm using AI for contract review saw a 5X increase in the number of agreements processed annually. This accelerated turnover time from months to days.
Streamlined Customer Service
Generative AI is ideal for customer service, using natural language processing to resolve inquiries instantly. It can respond to FAQs, track orders, and handle common requests. This reduces call volume and frees up agents for complex issues.
At one company with 5,000 agents, an AI assistant increased issue resolution 14% hourly and reduced handling time 9%. Large banks using AI virtual assistants report 20-40% containment rates for common customer requests.
This automation also drives revenue. Salesforce found AI-powered chatbots increased conversion rates on websites by up to 15%. Customers get quick answers, so they make purchases rather than abandoning sites.
According to McKinsey, Generative AI could reduce human-serviced contacts by up to 50% for companies, depending on existing automation levels. The cost savings and productivity gains are enormous.
Streamlined Back-Office Operations
Finance, HR, and other back-office teams handle high volumes of repetitive data tasks. Generative AI can process invoices, screen candidates, populate reports, and more based on rules.
For example, an AI assistant can approve invoices under a certain amount or flag those missing standard information. Rather than have accounts payable clerks manually review each invoice, the AI handles routine approvals rapidly. Staff are then available to handle exceptions.
By automatically populating fields in financial reports using real-time data, Generative AI also reduces reporting time from weeks to days. The hours saved on repetitive tasks results in substantial cost reductions.
The common thread across functions is leveraging Generative AI’s language capabilities to automate repetitive workflows. The technology follows processes, accesses data, and communicates naturally. This enables huge efficiency gains and allows skilled staff to focus on higher-value strategic initiatives.
Creating Content and Assets
Generative AI truly shines when it comes to creating marketing copy, articles, designs, and other branded content. This content generation can drastically accelerate creative processes and campaigns.
Rapid Content Creation for Marketing
Marketing teams spend countless hours brainstorming ideas and drafting content. Generative AI speeds up this grunt work immensely.
For example, a consumer goods company could use an LLM to quickly generate 50 localised social media ads tailored to different geographies and languages. The AI reviews customer data in each region and creates relevant messaging and designs that resonate with local audiences.
What would take a human Marketing team weeks of intensive work happens nearly instantly with AI. This enables extremely personalised marketing at unlimited scale.
According to McKinsey, Generative AI could increase marketing team productivity by 5-15%. More impactfully, the hyper-personalised content can significantly boost campaign performance. One major retailer saw 400% higher click-through rates using AI-generated ads compared to their standard ads.
Streamlined Design Workflows
Generative AI is also revolutionising design work. Rather than manually creating storyboards or logo mock-ups, designers can describe what they want, and Generative AI will produce numerous options in seconds.
For example, an architect could say "Create three interior renderings showing modern, minimalist decor options for a 600 square foot living room with a view of the city skyline." The AI model would instantly generate photorealistic 3D designs meeting the criteria.
This kicks off the creative process much quicker. Designers review the AI drafts, provide feedback, and select the best option to refine rather than starting from scratch. For simple projects, the AI output may be immediately usable with minimal tweaking.
According to Adobe, Generative AI capabilities like these could reduce the amount of time spent on visual design work by up to 80%. Significant portions of design processes are automated by the technology.
The Possibilities with Generative Content
Whether for marketing, design, or other use cases, the core value of Generative AI is exponentially amplifying content creation abilities.
Brands utilise AI-generated drafts as starting points to create higher-quality assets faster. Output in one modality like text can also be translated into other formats like images, expanding options. Virtually, any content process can be enhanced through Generative AI's abilities.
This provides enormous time and cost efficiencies. But more critically, it enables businesses to engage customers in more personalised, targeted ways that were never before possible. Brands who leverage generative content creation will have a clear competitive edge. WPP is an excellent example of an advertising company leveraging Generative content. Read WPP Partners With NVIDIA to Build Generative AI-Enabled Content Engine for Digital Advertising.
Extracting Insights from Data
Generative AI offers game-changing capabilities when it comes to rapidly analysing large datasets and documents to extract key insights. This turbocharges business intelligence and data science work.
Traditionally, analysing enterprise data to answer business questions is a heavy lift. Data scientists spend long hours querying databases and wrangling data into reports. Generative AI upends this process.
Rather than writing code, users can simply ask questions in plain English like "What products had the biggest sales increase in North America last quarter?" The Generative AI will parse the request, extract relevant data, and serve up a comprehensive response.
For example, an auto manufacturer could get a generated report highlighting the car models that sold best in different regions along with possible reasons why. These insights can inform production plans and marketing strategies.
This ability to answer natural language questions makes Generative AI a powerful analytics assistant. It enables data-driven decisions in minutes rather than weeks.
Surfacing Hidden Connections and Patterns
Generative AI also uncovers non-obvious trends and patterns that humans easily miss when sifting through masses of data. This sheds light on unexpected connections.
A Generative AI model analysing customer support transcripts could highlight that a recent app update triggered a spike in crash reports from a specific phone model. Traditionally finding these types of correlations manually takes intense data exploration.
For financial services firms, Generative AI could pore through earnings statements, news articles, and transcripts to generate a market analysis report in seconds. This would accelerate trading decisions.
Synthesizing Disparate Data Sources
Often the most valuable business insights come from combining different data sets - say sales transaction data plus customer demographics. But disparate systems and file formats make this difficult.
Generative AI models excel at processing diverse data types and structures. An LLM can integrate text, images, audio, tables, and more to uncover cross-functional trends.
For instance, a hospitality company could combine reservation data, customer feedback, and revenue by department. The AI model could highlight an influx of complaints about room cleanliness that coincided with a personnel shortage, revealing the root cause issue.
Bringing an AI Analyst Onboard
Between its natural language, data integration, and insight discovery capabilities, Generative AI essentially acts as a superspeed data scientist. It amplifies the abilities of human analysts and allows enterprises to maximise the value of their data.
According to research from MIT, adding Generative AI has helped data teams improve productivity by up to 8X. Data-driven decision making happens faster than ever before. This provides a significant competitive advantage.
The key is combining generative AI’s strengths with human oversight to deliver robust, actionable analytics. Together, humans and AI make an unstoppable data analytics duo.
Conclusion: Realising the Generative AI Opportunity
As this article outlined, Generative AI and Large Language Models have demonstrated tremendous potential to transform enterprises by automating tasks, accelerating content creation, and extracting insights from data. But executing on this opportunity requires thoughtful planning and commitment from executives.
The use cases and benefits span departments across an organisation. Marketing teams can generate personalised messaging at scale to boost engagement. Designers can instantly create drafts to jumpstart projects. Data scientists can answer business questions in real-time. The list goes on.
According to McKinsey, properly harnessing generative AI could add $3-5 trillion annually across global economies. Companies leading adoption could see revenue gains of over 10% in some industries. The incentives for adoption are massive.
However, as with any disruptive technology, there are challenges to address. Models must be aligned with brand style and tone through careful fine-tuning. Rigorous oversight processes should be implemented to validate quality and accuracy. Workforce training and change management programs need to be in place.
For executives and business leaders, the prudent course is to take a phased approach. Start with contained pilots in departments like marketing and customer service to build expertise. Assess value and risks before determining expansion plans. Bring cross-functional teams together to share learnings.
The goal should be to incrementally scale Generative AI across the organisation, unlocking its capabilities while proactively managing change. With this pragmatic approach combined with sustained commitment from leadership, companies can fully capitalise on the Generative AI revolution.
The time is now for executives to ready their organisations. As stated in McKinsey’s report on economic potential, “The era of Generative AI is just beginning. Excitement over this technology is palpable, and early pilots are compelling. But a full realization of the technology’s benefits will take time, and leaders in business and society still have considerable challenges to address.”
Tackling these challenges is well worth the effort. Done right, Generative AI can drive efficiency, boost growth, empower employees, and deliver competitive advantage for years to come.
In closing, generative AI should be a priority on every executive's agenda given its disruptive potential. With a pragmatic roadmap tailored to their specific strategy and needs, companies can harness generative AI and LLMs to work smarter, faster, and better. The time to start is now.
References:
What every CEO should know about generative AI
The economic potential of generative AI: The next productivity frontier
AI-powered marketing and sales reach new heights with generative AI