Three reasons to start intelligent AI adoption now
Karthik Ramakrishnan Karthik Ramakrishnan
May 22 5 min

Three reasons to start intelligent AI adoption now

The AI adoption journey will be unique for every business, yet every business’s journey shares a strong imperative to start now. Distinct challenges accompany the stages of preparing, executing, and scaling AI.

The first step, getting ready for AI, takes time, and most organizations are less ready than leaders think. Once a business is ready, putting AI to use involves iterative work that must unfold over time. Finally, there’s limited time in most industries to scale to your full AI ambition.

You need to start intelligent AI adoption early, because it’s easier than ever to run out of time.

Your business needs time to prepare

No one wants to be caught unprepared, yet most businesses are still not clear about what’s required to be ready for AI. Many are not as ready as they think.

The biggest area that businesses underestimate when starting with AI is data. Having big data is not enough. The right data must exist for each application in mind, and it must be available in the correct time, place, and state, with the right labels. In our experience helping businesses prepare for AI, we’ve seen that few of them have properly labelled data. Often, existing data has gaps or blind spots, leading to needs for further data capture, infrastructure, partnerships, and more.

Establishing your data requirements requires a clear vision for what AI applications to build or train. An AI application pairs AI capabilities, such as machine vision, with a specific business problem, such as insurance claim processing. It takes time to find desirable, feasible, and viable opportunities for AI applications: first to learn how to recognize those opportunities, then to decide which should take priority.

Preparations in data, people, strategy, and governance are all necessary to adopt AI. By gaining insight into your level of AI readiness early on, you can increase control over how long these necessary steps will take.

AI solutions require fine-tuning

Once you know what you want to do with AI, actually doing it requires iterative work that unfolds over time. You cannot shorten this work with extra planning, analysis, or technology at the start.

The reason is simple: compared to the old approach of using predictable rules to build software, AI systems learn to function using examples from data. This makes AI application performance unpredictable at first. You must experiment with the learning process, selecting and tuning the algorithms to find the right combination of choices that will achieve your goals. If the predictive performance doesn't match your expectations, you must decide whether to change the model, the data, or the task itself to improve results.

You must also consider what will happen after an AI system goes online. How will stakeholders work together to build and maintain trust in the system? How will they maintain and improve performance of the models over time? And how will you take advantage of any new opportunities that emerge?

There isn’t one right answer to these questions. Instead, you must reach the point of seeing what’s possible for you to achieve, and under what conditions. By starting this work early, you gain the benefit of time to explore these options on your own terms rather than under pressure from external forces.

There’s limited time to win

Even if you’re ready, willing, and able to achieve with AI, there's limited time on the clock for winning in most industries.

Businesses that scale first can gain benefits that allow them to scale even further, faster. With AI, increasing scale improves resources available for learning. Over time, this makes AI capabilities more powerful and more exclusive.

Businesses that adopt AI early can achieve this superior efficiency and scale before others in their industry. In contrast, those who start adopting AI too late may be unable to keep pace. With 60% of businesses committing IT budget to AI in 2019, and with AI talent still scarce, this challenge may get worse before it gets better.

In the longer term, the most successful businesses will be those that not only scaled with AI, but can scale their AI to match their ambitions. These businesses will specialize and deepen AI capabilities with proprietary learning resources, locking in access to the markets, partnerships, and trust required to apply and improve AI over time.

By starting now, you increase the chances that your business can set the pace for others rather than be left behind.

The long and short of it

This temporal imperative is unique to AI. The best case scenario is that you gain nothing by waiting. In the worst case scenario, you lose too much ground and can’t catch up.

While there are no shortcuts to AI success, you can still speed up intelligent AI adoption responsibly in other ways. In our next two posts, we explore our strategic roadmap approach that adapts your business to AI across four key areas of practice.