ai strategists are experts at creating an ai roadmap and vision for businesses. However, aligning the roadmap with expected business outcomes becomes challenging, given the changing scope of ai initiatives.
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Therefore, it is crucial to continue adapting and refining the ai strategy to ensure it remains aligned with evolving business objectives and the technology landscape.
But before we start with the strategy itself, let's discuss the role of an ai strategist.
ai strategists master ai workflows and assign business prerogatives to technical solutions that leverage ai. They understand the complexities associated with estimating opportunities and do not necessarily need to know the complexities of the algorithms.
Let's expand on these three pillars of opportunity estimation. Firstly, it is important to note that there are many innovative ways to solve a business problem, and not all of them require the use of sophisticated and advanced technology such as ai.
Some can be easily solved by rules, while others can simply be automated and solve the problem to a reasonable extent.
Carrying out such an evaluation is key to analyzing the baseline, which involves taking an inventory of what part of the problem is solved with the existing solution. If the current solution is not acceptable, then strategists make a concession by explaining the potential increase in effectiveness of the proposed ai-powered solution and the risks it entails.
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Primarily, strategists ensure that the team is aware of ai and opts for an advanced solution, fully aware of the time, effort, cost, complexity and risks associated. It would be fair to say that an ai strategist is the key to successful ai transformation.
Equipped with great business acumen, an ai strategist typically follows three factors to build a successful roadmap:
The first is to ensure that the proposed solution is technically feasible. They identify data requirements and evaluate whether the problem at hand justifies the use of ai. What if the data is not available, not authorized for model training, or does not have accurate labels? All of this falls within the purview of an ai strategist.
In addition to a feasible solution, the second aspect is feasibility. Even if the solution can be scaled up, an ai strategist uses the techno-business lens to evaluate whether the proposed model development is financially viable for business objectives. If the cost-benefit analysis suggests that the estimated benefit of developing the new ai/ML model does not justify the non-trivial cost of building it, then it is best to abandon the idea.
Any solution is good only if it provides value, which is often a challenge. The value could be expressed in terms of a new revenue stream, a business differentiator, improved processes in the form of automation that brings efficiency, and more. An ai strategist takes a detailed, methodical approach to defining the value proposition behind ai initiatives.
Phrases like digital transformation or ai transformation may seem irrelevant in today's rapidly evolving technology landscape.
It is worth asking that companies must continually innovate, take advantage of emerging technologies and adapt to market changes. So how do we define transformation when innovation is continuous?
Let's simplify and understand the basic principles behind incorporating such a multi-year business evolution.
Transformation is often a turning point when an organization realizes the need to review the legacy way of doing business. They understand that the status quo way of operating a company is not sustainable, causing them to lose their competitive advantage and therefore affecting their growth.
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Therefore, the pace at which they test the number of ideas quickly accelerates and flows into the funnel, making experiments work at scale. That's where the organization benefits from the combined knowledge of an ai strategist over time, who has led multiple ai transformations at scale. They are equipped with a toolkit consisting of adaptive frameworks, systems and processes, which can be abstracted as strategies that lead to a successful ai transformation.
A few years ago, when the concept of ai strategy began to become the focus of boardroom discussions, it caught everyone's attention. Specifically, by having too many strategies: business, artificial intelligence and data.
It's easy to get confused between multiple strategies, such as business strategy, data strategy, and now ai strategy. This is how the three strategies work together in coherence.
Strategy and business vision are always at the top. It is essential to have a clear business vision, critical growth drivers, and a roadmap that aligns with business objectives. Once business leaders decide the “why and what,” the “how” follows.
An ai strategist along with technologists focuses on the how part, to achieve the business vision through technology. It is important to note that technology is only an enabler. Therefore, ai strategy is derived from business strategy., which means it takes a lot of time to understand the business – the moat and the competitive point of view.
However, ai does not work on its own and needs data at its core to model the phenomenon. Therefore, it works in conjunction with the data strategy.
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The next important aspect of designing a successful ai strategy is ensuring that ai teams are not committed to reaching for the moon.. This is in line with the role of an ai strategist in evaluating the viability of the proposed idea. ai projects come with many “unknowns,” so it is essential to take into account anticipated and unforeseen risks.
The model is ready, but it is of no use if it is not aligned with the responsible and ethical principles of building ai. Imagine having spent a lot of money creating ai pipelines and workflows, the data is in place and the predictions are working well.
But only to realize that the data has a bias, includes PII information or something as basic but crucial as transparency and explainability.
It is important to note that predictions are useless until someone acts on them, and no one can act on predictions until they trust how and where they come from.
Therefore, ai governance, which includes extensive documentation on roles and responsibilities (ownership if it goes rogue), and the process of data collection, transformation, and training set, is the key factor for a successful implementation.
Understanding the trifecta of business strategy, data and ai along with the key pillars of ai strategy is crucial to leading organizations through a successful ai transformation.
Vidhi Chugh is an ai strategist and digital transformation leader working at the intersection of product, science, and engineering to build scalable machine learning systems. She is an award-winning innovation leader, author and international speaker. Her mission is to democratize machine learning and break down the jargon so everyone can be a part of this transformation.