The Road to Credible and Value-Driven AI: Start with Asking the Right Questions.

Author(s): Hajar Khizou

Originally published on Towards AI.

The recent advancements in generative AI have caught the attention of businesses, irrespective of their size, in implementing this technology to achieve tangible business benefits. However, many of these organizations have rapidly adopted existing AI models or embarked on developing their own without a strategic assessment of the challenges they aim to address or their readiness for AI integration.

While AI undeniably offers the potential for accelerated growth, revenue enhancement, and improved customer satisfaction, extracting its true value necessitates establishing a conducive environment for the technology to thrive.

As a CxO, it’s likely that various stakeholders, be it your board, competitors already capitalizing on AI, or third-party entities, have advocated for AI adoption in your organization. The current industry landscape doesn’t merely require an influx of AI solutions but demands credible and value-driven solutions.

Over the years of working and consulting for many organizations, I had the privilege of helping my clients design and build AI capabilities that align and drive their missions. Today, I’m sharing ‘The Road to Credible and Value-Driven AI’ with the intention of guiding the readers in their journey, bringing them closer to creating value-driven solutions and steering them away from the prevailing hype surrounding AI.

Where do we start?

First, Always Start with ‘Why?’ While it may seem intuitive, many organizations focus on the ‘What.’ Beyond merely identifying the right solution or answer, it’s imperative to ask the right questions — meaningful questions that can truly unlock value, even if they are challenging. As Simon Sinek articulates in his book:

‘It all starts with Why’.

Identifying the most critical issue that is addressable and offers significant potential for progress is essential. One should proceed with targeted actions only after this understanding, especially in AI implementation. Thoroughly examining the underlying cause of the issue necessitates a careful and deliberate approach. Frequently, the undertaking might be a complex and laborious endeavor, especially when confronted with intricate difficulties. Ambiguities in your information might introduce additional complexities in defining a problem. Firms must exhibit curiosity and courage, consistently asking the right questions within the right context.

In cognitive sciences, it is commonly known that there are two distinct cognitive systems underlying reasoning. System 1 is fast, focusing on readily available information, often summarized by the principle ‘What You See Is All There Is’ (WYSIATI). It excels in rapidly detecting associations, connecting the dots, and quickly developing a coherent story. As Adam Grant notes in his book ‘Originals’:

“People can’t help seeing signals, even in the noise.”

On the other hand, System 2 is believed to have evolved much more recently and is thought by most theorists to be uniquely human. System 2 thinking is slow and sequential. Despite its methodical nature, System 2 enables abstract and hypothetical thinking, which is beyond the capabilities of System 1.

The dynamics of these cognitive systems are prominently evident in the business landscape. Frequently, organizations oscillate between two extremes. At one end of the spectrum, there is a tendency to make rapid choices motivated by the appeal of quick action. This can result in decisions made without adequately examining the sufficiency or relevance of the available information. Such urgency to act can lead to misaligned strategies or overlooked nuances. On the contrary end of the spectrum, some organizations become excessively cautious and engage in exhaustive deliberation. They may pore through countless PowerPoint presentations, reports, and meetings to evaluate every available information. While thoroughness is commendable, this approach can sometimes result in “analysis paralysis,” the overwhelming volume of data and perspectives hinders rather than facilitates decision-making. Businesses must balance these two extremities to make informed, timely, and effective decisions.

In the corporate landscape, terms such as ‘quick win’ and ‘low hanging fruit’ frequently resonate. However, it’s not uncommon for organizations to invest significant time, often weeks or months, and substantial financial resources into tools, cloud infrastructure, and other associated costs for a ‘Proof of Concept’ (POC).

Ironically, these POCs sometimes aim to automate processes that, when assessed, would take mere minutes to execute manually. For organizations to effectively leverage the capabilities of artificial intelligence and attain a measurable return on investment (ROI), it is imperative to allocate resources prudently. Strategic discernment in identifying genuine opportunities for AI application, rather than pursuing automation for its own sake, is vital to ensure that investments yield meaningful and sustainable outcomes.

Second, Keep your focus and avoid shiny object syndrome. It’s easy to be swayed by the latest innovations and trends. The allure of the ‘shiny object syndrome’ — the tendency to chase the newest technologies or trends without fully realizing their implications or alignment with business goals — can be detrimental. Businesses often invest heavily in the latest buzzwords: one moment, it’s machine learning; the next, it’s deep learning, and then the spotlight shifts to generative AI. Such frequent changes in focus can lead to significant expenditures without yielding a proportionate return on investment.

However, It is crucial to remember the identified problem and remain committed to solving it. This focus guarantees a more efficient use of resources and maximizes the possibility of releasing genuine value. While staying abreast of technological advancements is essential, ensuring that these innovations serve the organization’s core objectives and do not divert attention and resources is vital.

In addition, they will undoubtedly face uncertainties and “unknown unknowns.” These are unanticipated challenges or variables that were not initially considered but can substantially impact problem-solving. In such circumstances, organizations must allocate a buffer for experimentation, allowing adaptability and flexibility. This allows learning from unanticipated outcomes and refining strategies based on real-world feedback.

Moreover, it is essential to bring in subject matter experts in the early stages of the process. With their extensive knowledge and experience, these experts can provide invaluable insights, guide the experimentation process, and aid in navigating the complexities, ensuring the organization stays on the path to impactful solutions and value realization.

Last, TAKE ACTION. Once an organization thoroughly understands the problem and has accounted for potential uncertainties, the next crucial step is to take coherent action. Coherent action signifies that every step taken is aligned with the overarching objective and consistent with understanding the problem and the experimentation phase. It’s not merely about implementing solutions but ensuring they are synergistic, building upon one another to create a holistic approach.

This requires a combination of strategic foresight, tactical execution, and continuous feedback cycles to adjust course as needed. It is also essential to create a culture of collaboration in which cross-functional teams work in partnership to drive the solution forward by leveraging their unique expertise. Organizations can optimize their efforts, reduce redundancies, and speed up the path from problem identification to solution implementation by ensuring actions are coherent and aligned.

Finally, the journey of AI implementation is as much about the question or challenge to be solved as it is about the solutions developed. The appeal of AI’s transformational promise can sometimes outweigh its practical use. However, a technology-first approach — choosing AI and then looking for challenges to solve — can result in misaligned tactics and unsatisfactory outcomes.

By asking the right questions, organizations can ensure that they are not just leveraging AI for the hype surrounding it but for its genuine capability to address specific challenges. This problem-first approach guarantees that AI acts as a tool adapted to the company’s particular needs rather than the organization bending to meet the limits of the technology. In essence, the success of AI implementation is dependent on clarity of purpose, which is obtained through asking the right questions from the start.

About the Author:

Hajar is an Artificial Intelligence advocate, speaker, and consultant who values technology, innovation, and excellence.

She helps mission-driven organizations leverage the power of data and AI to maximize their impact. She takes pride in listening to customers’ needs and crafting well-architected, innovative, and value-driven solutions that help her customers achieve their goals.

She can be found on LinkedIn & Twitter.

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Published via Towards AI