AI readiness: Where are you?

Brian Kempf, Vice President, Applied AI

Brian Kempf

Vice President, Applied AI

Illustration of surfer on a large wave to indicate the pace of AI advancement

The first step on your AI roadmap is to ask the right questions.

For many of us, the topic of artificial intelligence (AI) has taken over just about every conversation about the future of business. Organizations of all sizes are exploring how the technology can be a force multiplier for operations, elevate customer engagement, transform product experiences, and automate routine tasks. A variety of low-risk, high-value use cases for AI are already emerging in the digital workplace.

As the capabilities of AI tools continue to advance at a feverish pace, companies are forced to take a hard look at how prepared they are — or not — for AI adoption. To make it easier for organizations to get a holistic view of their maturity, Modus has launched a free AI-readiness assessment that asks 15 questions across five key categories — strategy, data, technology, people, and governance. It only takes about five minutes, and we’ll follow up with a personalized analysis of how prepared your organization is to explore, implement, and scale AI innovation.

Why should you evaluate your organization’s state of AI readiness? A baseline understanding is crucial to building a comprehensive and sustainable AI roadmap, rather than tacking on tools ad hoc. This initial research will also help you build a business case for bridging gaps, moving past initial challenges, and establishing a compliant framework before exploring more transformative applications of AI.

And when should your business conduct this analysis? This latest wave of AI is already at an inflection point — the time to get on board is now, you risk getting left behind in its wake or worse, trying to catch up.

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The pace of AI innovation isn’t waiting for ubiquity. It improves and spawns new innovations before the discontinuity of the prior curve.

Chart showing how the performance of AI is outpacing the traditional adoption curve.

Image: The performance of AI is approaching an inflection point along the S curve.

The "S-curve" phenomenon in AI advancement

For organizations holding back on AI experimentation — perhaps hoping to learn from more AI-advanced organizations’ mistakes and successes — a “wait-and-see” approach comes with financial risk. That’s because AI doesn't conform to the traditional bell curve associated with innovation but instead follows an "S-curve" pattern, characterized by periods of rapid innovation followed by periods of relative stability. This means late adopters are more likely to miss productivity gains and increase opportunity costs, lagging behind those already integrating AI into workflows.

AI is not a static entity but a continuously evolving field. Research breakthroughs, algorithmic improvements, and the availability of vast amounts of data drive this. As a result, AI applications often undergo multiple iterations and upgrades over a relatively short time, creating an ongoing cycle of innovation.

The initial stages of AI adoption exhibit exponential growth as innovators and early adopters seize upon new capabilities. This growth is often driven by the promise of AI's transformative potential across various industries, from healthcare and finance to transportation and entertainment.

After an initial surge of innovation, AI applications tend to reach plateaus where progress appears to slow down. During these periods, the technology consolidates its gains, refines existing solutions, and focuses on addressing challenges and limitations. This consolidation phase may give the impression of a stagnant adoption curve.

Just as AI reaches a plateau, a breakthrough or paradigm shift occurs, sparking a new "S-curve" of innovation. These disruptions can be fueled by advances in machine learning techniques, hardware capabilities, or entirely new AI subfields. Late adopters, who may have been waiting for the technology to stabilize, suddenly find themselves faced with a new wave of innovation and a widening gap in skills, resources, and competitive edge.

The problem with being late to the game

Late adopters who were waiting for stability in AI applications may find themselves further behind when the next wave of innovation begins. This larger gap can be a significant barrier to entry and competitiveness.

Delaying AI adoption for too long can result in a company or organization becoming obsolete in its industry. Early adopters and fast followers may have already gained a substantial competitive advantage.

Late adopters must rapidly catch up with the latest advancements and may face a steep learning curve. Staying current with AI trends and best practices becomes imperative.

Get a clear roadmap and next steps

Organizations just getting started with AI may benefit from forming partnerships or collaborations with early adopters and AI experts to navigate the complexities of AI adoption more effectively.

Wherever your organization falls on the AI maturity curve, Modus’ digital innovation solutions can help you put emerging technology to work. Our AI researchers, technologists, designers, strategists, and marketers help leading businesses stay ready for what’s now and what’s next.

AI-readiness assessment: See how you stack up

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Brian Kempf, Vice President, Applied AI
Written by

Brian Kempf

Vice President, Applied AI

Brian leads the Applied AI team at Modus. With a background in design, operations, consulting, product, strategy, front-end development, information architecture, and marketing, he is passionate about developing elegant, simple solutions to complex technical and experiential problems.

Brian leads the Applied AI team at Modus. With a background in design, operations, consulting, product, strategy, front-end development, information architecture, and marketing, he is passionate about developing elegant, simple solutions to complex technical and experiential problems.

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