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The Age of Enterprise AI

Jim Snabe, chairman of Siemens, former co-CEO of SAP, and C3 AI board member, explains how accelerating the pace of artificial intelligence adoption can help improve decision-making and drive business value.

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 its “Innovator’s Dilemma” by transitioning entirely to a streaming service, while Kodak is an example of one that did not.

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Reimagining the enterprise with AI

Most companies today are facing a similar dilemma as they grapple with the adoption of artificial intelligence (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 Those who ignore it risk falling behind their peers as they rely on increasingly outdated and backward-looking systems, leaving them reacting to market disruptions. However, companies that adopt enterprise AI and its new companion, generative AI, can enable anticipatory decision-making for every employee, from those on the frontline to those in the boardroom, driving responsiveness, efficiency, and agility across the business. The pace and alacrity of AI adoption will determine businesses’ 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 or the percentage of deliveries made on-schedule and in the full quantity ordered. 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. 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 also reliable predictions about the future that they can trust and reference. Leaders need the predictive capabilities of AI that can help them anticipate and shape the future.

While AI has been riding the peaks of the hype cycle for a few years, businesses have struggled to adapt and implement the technology 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 businesses with the perfect opportunity to harness the predictive powers of AI, something even ChatGPT waxes poetic about.

The advent of AI

But generative AI can do a lot more than produce cutesy poems. The new generative AI capabilities of large language models, which can synthesize large data sets and generate new content from them, are now being trained on the corpus of a company’s data infrastructure, enterprise systems, machine learning (ML) 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:

AI and anticipatory decision-making

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

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 ML 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.

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.

How to implement AI in your business

Step 1. Identify the most relevant business opportunities Not all AI use cases are equally valuable. Selecting the right opportunity within 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. Pilot the first AI application The next step is to pilot the first generative enterprise AI application to address the priority opportunity in your business. It is important to pick a part of your business that has the data to support the use case, leadership committed to make the change, and resources to implement the pilot 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 the AI platform to build your own applications While you can purchase off-the-shelf proven AI applications to begin with, you also 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 all a company’s 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.

Note: This article was written by Jim Snabe and published by C3 AI and originally appeared on C3 AI.

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We often assess risks associated with action but forget the risk associated with nonaction. In the case of AI, the risk of nonaction 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 therefore have an opportunity to overcome the “Innovator’s Dilemma” by reinventing themselves again and again.

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—A poem about AI in the enterprise by ChatGPT

The Age of Enterprise AI