2023 was the year of Generative AI (Gen AI). Businesses across industries dipped their toes into Gen AI waters – testing, learning, and seeing what could happen. As we step into this year, the initial awe of Gen AI’s capabilities has given way to a more pragmatic approach. This year, the focus is now squarely on implementation.
Going from the sandbox of experimentation to the reality of implementation is more than just lifting and shifting or scaling. Leaders must first pinpoint where Gen AI can really make a difference and understand the balance between what it costs and what it brings to the table. And perhaps the most important question of all is how we get our teams up to speed and ready for the changes Gen AI will bring. We also need to make sure we are deploying Gen AI responsibly and are always in the clear on privacy, bias, and accountability
Roadblocks to implementation
Most digital initiatives sputter before they take full effect. Even the most promising projects and technologies get stalled or even derailed during implementation. Gen AI implementation is no different, especially for customized solutions trained on proprietary data. Amidst many steps to ensure that the Gen AI transformation projects do not face the same fate, organizations can begin by implementing rigorous change management and internal- communication programs to clarify the Gen AI’s projected impact, which can help build alignment and commitment.
McKinsey suggests that every organization really needs to think about Gen AI’s impact—what it means for the industry, their business model, and their strategy. Digital leaders need to ask themselves: What are their most urgent use cases for AI right now, and how might Gen AI transform these in the next six to twelve months? Then, there’s the functional shift—what needs to change in the processes to pave the way for Gen AI? And let’s not overlook culture; it’s all about whether the workplace environment is ready to embrace and nurture the growth that Gen AI brings.
Scalability is also a challenge. An idea that works well in a controlled, small-scale environment might not perform as expected when scaled up. This could be due to technical limitations, unforeseen costs, or a lack of market demand at a larger scale.
Current state to the desired state
Every organization is at different levels of AI maturity, and what is special about Gen AI is that almost everyone can use it without any formal training. We see a gradation of AI’s role from passive to active, with increasing autonomy at each stage.At Autonomy Level 0, there’s no AI. Every action is human driven. Picture traditional methods like sketching designs by hand—a fully manual process.
Autonomy Level 1: Here, AI starts as a tool. The users are still in charge, but AI helps with the basics. For example, it’s like typing in a digital editor while AI checks the spelling on the fly. The users do the thinking; it handles the little slip-ups.
Autonomy Level 2: Now, AI steps up as a consultant. It’s got a voice but only speaks when spoken to. Take using a
language model to summarize text—that’s AI getting more involved, but users are still pulling the strings.
Autonomy Level 3: At this level, AI becomes a collaborator. It’s like a team player who chimes in with suggestions, such as chess-playing AI or entertaining users with social interactions. The AI is proactive but doesn’t take big steps without user discretion.
Autonomy Level 4: As an expert, AI now has serious input. The user gives it a goal, and it maps out the path, like using an AI system to advance scientific discovery. The users guide the direction; AI paves the way.
Autonomy Level 5: At this level, AI is in the driver’s seat, fully autonomous. It’s the future where AI might manage entire routines independently, making sure a day runs smoothly without users micromanaging.
The initial autonomy levels, like level 1 and level 2, are just the start of the Gen AI revolution and are comparatively easier to achieve. For example, software tools like Grammarly, which could help users check sentence formation or spell checks, are already in existence. The levels 3, 4, and 5 require companies to prepare extensively with a robust Gen AI-led transformation roadmap.
The control shift is clear: at lower levels, AI aids human decisions; at higher levels, it starts to make those decisions within defined parameters. But how do we get there?.
Rebuilding the momentum: Filling the implementation gap
In the event of Gen AI implementation facing roadblocks due to ineffective design of transformation, scalability issues, or unforeseen costs, Gen AI itself offers a solution to this implementation gap. A lot has been said about Gen AI’s capabilities in the past year, but McKinsey said it best – the 4Cs. Concision, content creation, customer engagement, and coding. It can process large volumes of unstructured data to derive needle-sharp insights. It can create structured documents and content tailored at scale. Out-of-the-box co-pilots improve customer engagement. Coding helps large-scale modernizations.
What does this mean for different functions of a business – any business? The 4Cs fundamentally enhance key business capabilities, which are also the top priorities for CEOs in 2024: decisionmaking, optimizing operations, hyper-personalizing customer interactions, and accelerating product development.
The strategic role of Gen AI in decision-making and operational efficiency
Gen AI amplifies human potential, allowing teams to achieve more by enhancing their natural capabilities with AI assistance. This means tasks are completed faster and with greater precision, from routine operations to complex problem-solving.
It unlocks value by tapping into data and insights that were previously inaccessible or too cumbersome to decode
A supply chain manager, traditionally reliant on AI for predictive analytics, would see their role transformed with the introduction of Gen AI. Where once they may have used AI to make educated guesses on inventory needs, Gen AI amplifies this potential, integrating deeper learning and a broader scope of data analysis. It might, for example, identify a pattern that suggests changing a shipping route can save time and reduce carbon emissions, offering a dual benefit of efficiency and sustainability.
The ultimate benefit comes down to impact. What once took weeks now takes days, with the added advantage that these smarter processes are consistently learning and improving, getting better at anticipating and capitalizing on them.
The future is hyper-personalized, and Gen AI is the driver
One of the most compelling narratives in the Gen AI story is its capacity for hyper-personalization – tailoring products, services, and experiences to individual preferences. Track the journey of an insurance customer, for instance. Alex contacts the service center with a query about policy coverage after experiencing property damage. Traditionally, Alex might endure long wait times or navigate complex menus for answers. Instead, a Gen AI-powered system instantly reviews Alex’s policy details, claim history, and the specifics of the inquiry, providing a personalized explanation and initiating the claim process without delay, significantly enhancing customer satisfaction by making the interaction smooth and straightforward.
Or consider another customer who prefers interacting with an agent over self-service. Sam wants her complex issue with a piece of industrial equipment resolved on a call. Let’s say, ordinarily, resolving such a query would require a customer service agent to comb through extensive technical documentation, a process spanning hours. However, with a Gen AI-enabled platform, the moment Bailey outlines her problem, the system dynamically analyzes the vast knowledge base, summarizes relevant information, and provides actionable solutions instantly to the agent. This drastically reduces resolution time and ensures that Bailey receives accurate, concise advice. It’s a perfect win-win-win for customers, employees, and companies.
The Gen AI playbook for accelerated product development
Gen AI significantly reduces the time it takes to bring new innovations to market.
Consider a digital product: a banking institution looking to develop a new, innovative financial product aimed at young entrepreneurs. Using Gen AI, the bank can analyze transaction data, customer feedback, and market trends to identify the preferences of young entrepreneurs. The banks can then measure the risk of their overall portfolio and come up with new products and solutions like flexible lending terms, interest in financial education tools, or integrated business management services. To strengthen the probability of success, Gen AI can simulate the market response to each of these product features and iteratively refine the product design based on simulated and real-time feedback.
Gen AI has another important application that could solve one of the biggest challenges in every industry– talent shortage. Imagine this. Instead of traditional drills, we deploy a Gen AI program designed to simulate customer interactions. This AI ‘customer’ can converse, pose problems, and react just like a real client, providing a wide range of scenarios for our team to respond to. It’s efficient— training happens on-the-fly with immediate feedback. The team members can quickly adapt to handling real-world situations, using complex simulations only Gen AI can provide.
New answers to old questions
While Gen AI implementation is one of the top 3 priorities of 89% of CEOs, 90% are still waiting for more answers to unfold. They are either taking it slow or just waiting to see if the hype is grounded in reality. Perhaps they know too well that Gen AI is not just a tactical or off-the-shelf implementation but a disruptive force that changes businesses as we know them today. While the role of Gen AI in the functional transformation story is inspiring, its real and sustainable value comes from collaboration, putting people in the loop, and, more importantly, from the responsible use of it.
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Disclaimer Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the respective institutions or funding agencies
- https://www.bcg.com/publications/2023/maximizing-the-potential-of-generative-ai
- https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/tech-forward/why-most-digital-bankingtransformations-fail-and-how-to-flip-the-odds
- https://www.mckinsey.com/capabilities/operations/our-insights/operations-blog/generative-ai-will-first-be-successfullyscaled-in-business-operations
- https://www.forbes.com/sites/lauraclaytonmcdonnell/2024/01/22/ceos-are-ready-to-embrace-genai-in-2024-can-theymake-it-work/?sh=bae52f94bf7e
- https://www.mckinsey.com/capabilities/growth-marketing-and-sales/our-insights/how-generative-ai-can-boost-consumermarketing
- https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/the-economic-potential-of-generative-ai-the-nextproductivity-frontier#business-value
- https://www.bcg.com/publications/2024/from-potential-to-profit-with-genai
