Clayton Christensen’s celebrated book, The Innovator’s Dilemma, describes how large, established enterprises struggle in adopting disruptive technologies. The oft-proven premise is that as companies grow, scale, and optimize existing products they must face a dilemma to either continue down the same beaten path of incremental improvements delivering steady returns, inexorably leading to a declining market position, or reinvent by preemptively replacing their existing product(s) with disruptive technologies. Netflix is a good example of a company that successfully resolved their Innovator’s Dilemma by transitioning entirely to a streaming service, while Kodak is an example of one that did not.
Most companies today are facing a similar dilemma as they grapple with the adoption of AI. Those who ignore it risk falling behind their peers as they rely on increasingly outdated and backward-looking systems, leaving them reacting to market disruptions. Those that adopt enterprise AI and its new companion, generative AI, can enable anticipatory decision-making for every employee from the frontline to the boardroom, driving responsiveness, efficiency, and agility across the business. The pace and alacrity with which companies adopt AI will determine their long-term competitiveness and survival.
Decision-Making Today: A Rear-Gazing High-Speed Chase
Today, leaders in most companies use data and metrics to assess performance, identify gaps, make course-correction decisions, and update plans. These could be financial metrics such as profit per customer or supply chain metrics such as On-Time-In-Full. In most companies, these metrics are assembled from data provided by enterprise applications such as ERP, CRM, SCM, and HRM.
This data is usually current for the most recent planning period. The more progressive companies may have data that is more current or even near real-time. But in almost all companies, this is historical data about what has already happened, the figurative equivalent of driving forward with your eyes fixed on the rearview mirror.
Leaders can get away with making decisions based on historical data when the world is stable, business conditions are certain, and the road ahead is straight, flat, and deserted. But today’s tumultuous post-pandemic world riven with macroeconomic, geopolitical, and technology disruptions, is anything but stable and certain.
What leaders and decision makers need today is not only data about the past, but reliable predictions about the future that they can trust and reference. Leaders today need the predictive capabilities of AI that can help them anticipate and shape the future.
The Advent of AI
While AI has been riding the peaks of the hype cycle for a few years, businesses have struggled to adapt and implement AI at scale. Recent advancements, such as the launch of the hugely popular service, ChatGPT, has made AI instantly relatable and captured the imagination of millions the world over. This presents the businesses with the perfect opportunity to harness the predictive powers of AI, something even ChatGPT waxes poetic about.
“With AI at their side, the enterprise grew,
Their profits soared, because now they knew,
With technology they could predict and see,
What’s to come and how it will be.”
-A poem about AI in the Enterprise by ChatGPT.
AI and Anticipatory Decision-Making
But generative AI can do a lot more than produce cutesy poems. The new generative AI capabilities of Large Language Models are now being trained on the corpus of a company’s data infrastructure, enterprise systems, machine learning models, and AI applications to help decision-makers anticipate the future and improve the quality of their decisions.
For instance, a business leader could ask, “What’s the most likely sales forecast for this quarter and what can we do to improve it?” The generative enterprise AI application would:
- Understand the intent of this search query,
- Acquire data from internal systems such as CRM and ERP as well as external sources such as econometric data and equity prices,
- Use machine learning models to assess the likelihood of each deal closing and accordingly estimate the likely gap to forecast plan,
- Explain the largest drivers of the gap-to-plan,
- And make recommendations on best actions to close the gap-to-plan.
Similar generative enterprise AI applications will be able to address key leadership questions about expected supply chain risks, asset performance, or carbon emissions by pulling the required data and using relevant AI models for generating forward-looking answers, complete with explanations and action recommendations.
How to Implement AI in Your Business
While the vision of AI-based decision making is compelling, making it a reality will require disciplined execution and a systematic approach with an eye on producing short-term business value while building a reusable and scalable architecture.
Step 1 – Identify the most relevant business opportunities
Not all AI use cases are equally valuable. Selecting the right opportunity with the right parts of your business can have a significant impact on the trajectory of your transformation program. The first critical step in this journey is to assess AI opportunities based on the economic value they can generate and the level of complexity in implementing the AI application.
Step 2 – Deploy the initial AI application
The next step is to deploy the initial generative Enterprise AI application to address the priority opportunity in your business. It is important to pick a part of your business to do the initial production deployment in that has the data to support the use case, leadership committed to make the change, and resources to implement the deployment and drive adoption.
Step 3 – Leverage the AI Platform in other areas
After the successful implementation and rollout of the first AI application, it is important to reuse the underlying AI platform to quickly follow up with other applications. This will ensure scalability and efficiency in the transformation program and significantly accelerate the rate at which AI applications can be implemented.
Step 4 – Master AI Platform to Build Own Applications
While I recommend that you purchase off-the-shelf proven AI applications to begin with, you want to master the underlying platform so that you can build your own AI applications that capture your unique IP and business practices.
Step 5 – Democratize AI for all
The power of Generative AI can make the data, AI models, and AI applications available to everyone in the organization through an easy search bar interface. This can significantly multiply the number of users who can benefit from the AI infrastructure and improve decision-making at multiple levels throughout the organization.
We often assess risks associated with action, but often forget the risk associated with non-action. In the case of AI, the risk of non-action is quite significant. Companies that choose to ignore AI do so at the risk of being reactive and thus too slow to stay relevant. Companies that accelerate AI adoption will become proactive and agile in their decision making and thus have an opportunity to overcome the “Innovator’s Dilemma” by reinventing themselves again and again.
Ready to see what enterprise generative AI can do for your business? Learn more about C3 Generative AI or request a demo.