Why is Speed of Implementation So Important to Success with AI?

One of the most common questions I’m asked about AI is why speed-to-market seems so much more important for AI than it did for other transformative technologies. It will come as no surprise that there are nuances to the answer.?

To tackle the implicit question first, yes, speed of implementation is extremely important when it comes to AI, and especially generative AI. With the success of Nvidia and OpenAI, you can already see that the stakes of this race are tied to billions, if not trillions, of dollars, and that the first-mover advantages are already significant.?

At a surface level, one primary reason that AI is seeing such a high speed of adoption is the low barrier to entry compared to say, a smartphone. For most generative AI applications, there is no hardware involved, which cuts a massive amount of time off implementation. And because much of AI is open source, the foundations are essentially available to everyone.??

The nuance comes in the “how” and the strategy behind the speed of implementation. What an organization is implementing, why it’s implementing that particular application, and importantly, the quality of the data and security of the application itself are all factors that will decide whether a first mover falls back into the pack after a fast start or actually maintains a sustainable advantage.?

In essence, this means IT departments must work hand-in-hand with business decision-makers to ensure rapid integration of AI into their organizations’ current tech stacks... but not at the expense of data quality.??

Without the right infrastructure and resources in place—particularly when it comes to data—businesses run the risk of their major AI investments ending in failure. The keystone to any successful AI implementation strategy rests on your organization's capabilities to gather, store, clean and crunch the data needed to make those AI algorithms hum.??

Unlike traditional software, which functions based on predefined rules, AI models thrive on real-world data. The more data, the more these AI tools will learn, adapt, and ultimately improve in their accuracy and effectiveness. The earlier you implement AI, the more time you're giving the algorithms to gather and analyze vast amounts of data.?

Data Strategy Before the Starting Gun??

Given how effective AI implementation requires vast amounts of data, it’s critical to invest in advanced data collection systems to gather information from various sources—everything from customer interactions and transaction histories to operational processes, depending on your broader strategy and needs.??

But equally important is the storage infrastructure: cloud-based solutions are often preferred due to their scalability and flexibility to store and manage large datasets efficiently.

Once data is collected, it must be integrated and managed effectively. Data integration tools and platforms can aggregate data from different sources, making it clean, accurate, and ready for analysis. Granular data management practices, such as regular data audits and validation processes, are also essential to maintain data quality.?

For AI systems to learn and adapt, data must flow freely across departments. This requires a cultural shift towards greater collaboration and the integration of data management systems that facilitate seamless data sharing. Otherwise, you run the risk that data silos—isolated pockets of data within an organization—will bottleneck effective AI implementation.?

Another corporate cultural shift may require the hiring of a chief data officer (CDO), tasked with managing and overseeing data strategy and ensuring data practices align with business goals. The CDO will also enforce data governance policies, and drive an organizational culture focused on data-driven decision-making.??

Adoption at the Speed of Thought?

Now that we've laid the data foundation, let's look at the five segments of technology adoption—innovators, early adopters, early majority, late majority, and laggards—that clarify the criticality of speed when it comes to AI implementation.??

Innovators?

Innovators are the trailblazers, the earliest individuals or companies to adopt new technologies, willing to take risks and venture into uncharted territory. In the AI landscape, innovators embrace new AI models and tools as soon as they emerge, experimenting and iterating rapidly. Their risk tolerance and financial resources allow them to absorb failures... but when they succeed, they set the pace for the entire industry.??

By being first movers, innovators can capture significant market share and position themselves as leaders. Their willingness to experiment with unproven technologies gives them early insights and the ability to refine their AI strategies before the starting pistol is even fired. One recent study found that 85 percent of CIOs are already examining their existing infrastructure to ensure it can withstand the demand from cloud and AI applications over the next several years, efforts that will yield dividends as more companies shift to an “A- first strategy.”??

In a bid to accelerate their AI adoption, innovators are also leaning on consultants. For example, IBM has won more than $1 billion in AI-related consulting commitments, while Accenture pulled in more than $300 million in generative AI-related sales in fiscal year 2023. Those are huge numbers, and underscore trailblazers’ commitment to rapidly moving into this new territory.?

Early Adopters?

Early adopters closely follow innovators and can also play a pivotal role, thanks to their robust culture of opinion leadership—these are the trendsetters within their networks and set benchmarks for others to follow. As AI adoption gathers steam, early adopters will leverage AI to optimize processes, personalize customer experiences, and gain valuable insights from data. Think of them as the bridge between the daring innovators and the more cautious majority, with a laser focus on practical benefits of successful AI implementation.?

Early Majority?

The early majority adopts technology significantly after innovators and early adopters, taking a more pragmatic approach cemented in proven benefits and clear use cases. This crowd will wait for the early AI kinks to be ironed out and have time to study successful cases. By the time they adopt AI, however, the competitive landscape may well have already shifted. Sure, they benefit from the experiences of the early adopters, but their slower pace means they often play catch-up, missing out on the first-mover advantages and potentially being forced to invest more money into applications due to more solidified industry players and established pricing models.??

Late Majority?

This brings us to the skeptical, risk-averse late majority adopters, who wait to adopt new technology until it is widely accepted and established. Members of the late majority need to be able to point to proven ROI before committing. As AI becomes more integrated, the late majority will be reacting to changes rather than driving them. This also translates into higher costs and lower gains from AI implementation, with market leaders having already reaped the most significant benefits.?

Laggards?

Bringing up the rear are the laggards—resistant to change, rooted in tradition, the last to adopt new technologies. By the time they get around to adopting AI—often out of necessity—they face severe competitive disadvantages: their processes and strategies are outdated, marked by a scramble to stay relevant in a market that has long since moved forward. Unlike innovators and early adopters, their insistence on traditional methods has cost them an advantage in efficiency and advancements.??

A Note on Responsible Development?

While I'm convinced speed in AI adoption is critical for success, rapid implementation should not come at the expense of thorough testing.?

Not only are there ethical considerations—take healthcare, for example, where data leakage from improperly structured AI features could prove disastrous, even illegal—but a rushed deployment without sufficient testing can lead to flawed systems that might not only fail to deliver the expected benefits but also introduce new risks and challenges.?

When AI systems are thoughtfully and ethically implemented, they can deliver substantial benefits such as increased efficiency, better decision-making, and enhanced user experiences, while fostering trust among users and stakeholders, which is essential for the long-term success and acceptance of AI technologies.?

Make no mistake: the bold leaders who focus on speed of AI adoption are bound to have a greater initial advance and the benefit of more lessons learned at a time when the technology and its use cases are still evolving with astounding speed.?

While the finish line may still lie far ahead of you, maintaining a trend-setting pace will keep you ready to clear whatever hurdles are coming down the track.

Conclusion?

Here are some key takeaways as you plot your own AI journey:?

• Speed is crucial for AI adoption due to first-mover advantages and lower barriers to entry compared to other technologies. Generative AI, for instance, doesn't require hardware and leverages open-source foundations.? ?

• Successful AI implementation hinges on data quality and strategy. Large, clean datasets are essential to train AI models and improve their accuracy. Businesses need to invest in data collection, storage (often cloud-based), and management systems to ensure data quality and accessibility across departments.? ?

• Different adopter categories exist with varying risk tolerance and speed of adoption. Innovators are the first movers who experiment rapidly, while the Early Majority wait for proven benefits and ironed-out kinks. Late Majority and Laggards are more risk-averse and adopt later, potentially missing out on first-mover advantages.? ?

• Speed shouldn't compromise responsible development. While rapid implementation is important, thorough testing and ethical considerations are crucial. Flawed systems due to rushed deployment can lead to worse outcomes and erode trust in AI.?

This is Part 2 of my LinkedIn series: From Calculated Risks to Quantum Leaps: Charting the Course for Tech Talent in Flux.??

You can read the previous article here?and the next one on July 22nd.??

Most importantly, please join the conversation and share a comment below.?