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Author: Timothy Figueroa

Before You Get Into The Complexity of AI

Before you get into the complexity of AI you should dial in your approach to developing and prioritizing AI use cases. Align AI strategy to the corporate strategy and priority so AI use cases make sense to that organization.

The full value of AI implementations, for an organization, are not realized unless the manager responsible for AI in the organization aligns AI strategy and use cases to the corporate strategy and priority.

There are many examples today of AI implementations that bring value but are not linked to the Corporate Strategy and Priority of that organization. The result is difficulty understanding and measuring the value of the AI use case to that of the organizations goals and priorities.


This may come about where a leader hears about a cool AI technology like “Generative AI” and they want that implemented in some fashion in their line of business and then they accomplish this objective.

The lack of AI strategy alignment to corporate strategy and priority  misaligns  the AI use case resulting in AI implementations whose values are hard to assess in the context of the overall organizations KPI’s, its customers, lines of business and more.

Where AI use cases are carefully aligned and planned with the Corporate Strategy and Priority in mind it is easier to assess the value pre-implementation, in the short term and the in the long term. Aligned AI Use Cases may then, in time, become a jumping point for new products and services as the organization gains confidence in the AI space.




An alignment example might be, where the top corporate goal is to “better serve customers”. Drilling into this may mean, to the organization, where the customer interacts with the current corporate website and the portal does not support natural language queries for targeted information retrieval in a self-serve way and customers today bypass  the portal and phone customer service instead, asking for the information wanted.

The impact of this portal deficit to the organization is that they maintain a larger customer service staff, support training, and maintain infrastructure; who are tasked with processing customer information query requests manually.

The possible AI use case that proposes to solve this deficit may be to enhance the current website by implementing an AI powered information retrieval feature that is easy to use and is self-serve.  The AI solutions may be varied but the AI Use Case would be aligned to the corporate priority, would make sense to the organization garnering broad support and would solve a known problem. The AI user case would be measurable  in terms of the current KPI’s used to measure performance.


Where the AI Strategy is linked to the Corporate Strategy, management at all levels can assess value and priority prior to any AI use case approval. As well, management would be able to articulate AI use case deliverable expectations, and how these expectations may enhance the existing environment, talk to impacts to the customer or organization, the market, possible effects to product and service offering and, in some cases, impacts to their industry.

Before you get into the complexity of AI, consider your AI strategy and use case approach. Think about linking your AI strategy and use case development to the Corporate Strategy and Priorities of that organization, this will assure alignment, measurable value, and organizational support. I believe this to be the first step to AI success.

Strong Requirements Lead to Strong Solutions

Strong requirements lead to strong solutions. What are strong requirements? Strong requirements can be defined as the documented details of a customer request that provide readers a request from the customer point of view.

While weak requirements can be defined as documented details of a customer request that do not provide the reader a request from a customer point of view. Weak requirements slow the build factory, increase time to delivery, encourage misinformation, trigger unexpected adjustments to the build plan causing time and resource waste.

The quality of requirements impacts an entire chain of events and resources that are used to drive a customer request all the way to delivery. This chain, high level, starts with communicating your understanding of the customer request, planning and prioritization of the work entailed in the request, assuring development’s understanding of the request so they create the solution wanted, and then back to the customer to assess the solution created to confirm alignment.

Initial conversations with customers over a request are a start point in the elicitation process. Well thought out requirements is the goal and accomplishing this may take several iterations.




A recent example of an initial customer request was as follows:

–          “Delivery / vs internal transfers, Split delivery UI from transfer UI”


This start point told me, generally speaking:

  • The customer has a request,
  • The request is not well defined,
  • The initial request implies:
    • There is an existing system with a combined delivery and transfer feature,
    • A transfer feature is a sub-feature inside of the delivery feature,
    • The customer wants the Delivery and Transfer features separated,
    • This request will probably entail UI and workflow changes,
    • I have questions and clarifications…


Refining this request into a strong requirement entailed interviewing the customer to get their understanding of what was wanted.

A hidden risk to manage in the goal of refining the customer ask into a strong requirement is in the form of “Analysis Paralysis”. Only go as far as needed with details, and not beyond. Do this at speed so you deliver results in a timely manner. The line here is as much an art as it is a science.

The benefit of doing the upfront work that supports strong requirements may be in the form of reducing unexpected events on the way to delivery, assuring the same understanding and expectation of the request by the stakeholders, and supporting the notion that business and technical resources are aligned. In other words, no surprises.


Skilled business analysts will use the IIBA BABOK Elicitation and Collaboration techniques and method to refine requirements so that they are relevant and useful. Preparing, conducting, confirming, managing stakeholders and communication are key guidelines to apply.

In conclusion, Business Analysts with the skill to craft strong requirements will enable the build factory to deliver robust solutions at speed and without surprises.

Strong requirements lead to strong solutions.

A Picture Is Worth A Thousand Words

Many customer requests are complicated, involving complete end-to-end solutions. In many cases adding to the complexity is that these solutions must be integrated into an existing system. This article focuses on complex requests and the value of use of experience design tools to support the BA in meeting customer goals.

For the BA to demonstrate understanding of a request, work at speed, and gain consensus, the BA approach to elicitation matters.

The BA approach should support continuous communications, speedy responses, common understanding, risk mitigation and collaboration across distance and time. In the case of complex requests where the analyst writes detailed requirements up front, this may in fact result in miscommunications, reduced speed, and restrained collaboration. Most customers are business managers where an approach that generates upfront details may overwhelm the customer and obscure the view of the end-to-end solution putting the customer and BA in a “can’t see the forest from the tree’s” scenario.

Approach selection typically determines how efficiently and effectively the BA meets the customers’ need which in most cases is to demonstrate to the customer “you heard me”, “show me what I requested”, and “prove progress toward a solution, a road map”. IIBA’s BABOK defines knowledge areas containing strategies, guidelines, and techniques that provide an array of approaches to elicitation.

One efficient technique is the use of a prototype. It is said, “A picture is worth a thousand words”. Prototyping is a proven method for product design and helps a great deal in providing an early model of the final result.

Prototyping, in this case, will highlight the use of experience design tools for web and mobile apps in support of elicitation activities for complex requests. Complex requests are an indicator for the use of prototypes.




The use of an experience design tool is typically cloud-based supporting collaboration and communications. These types of tools as well support a big picture view giving the customer the ability to experience what the solution might be without the time and development expense.

Users do not experience database, integrations, or technical details. They are impacted by them, however, the user experiences UI’s, workflows, processes, features, and functions. Prototypes encourage common understanding by supporting the ability of the customer to walk through the solution model that incorporates UI’s, processes, workflows and features and functions.


There are several industry leading experience design tools that support solution prototyping. This article will not detail specific tools, rather it is to focus on the value of the use of these tools in satisfying complex business requests. Experience Design tools provide the ability for the business analyst to document requirements in the form of a prototype as described by the customer. The value of an experience design tool is that it supports the BA’s ability to create a simulated solution that,

  • The customers may walk through, make comments, suggest changes, discover the unexpected, use for demonstration purposes and more…
  • The technical team as well may walk through the proposed solution asking questions, making suggestions, prioritizing work, planning technical details, and coming to a common agreement using the additional documented requirements.
  • The BA can plan and write the requirements from the customer-approved prototype without leaving out any details. The prototype assets (i.e., UI’s, processes etc.) can be reused in the documented requirements to provide the needed details.

In conclusion, experience design tools can reduce misunderstanding, encourage communications and collaboration, support progress, and more. Consider adding experience design tool skills to your BA toolbox. A picture is undoubtedly worth a thousand words!

Distilling Data down to Actionable Details

It is not enough to implement systems and collect and store activity data. A key value proposition for the business is to justify the significant cost of Information Technology investments and one of those justifications one may be the ability to distill data down to actionable details that provide the business with timely insights management can use to make decisions that lead to profitable results.

The science of distilling data down to actionable details comes under the general topic of “Business Intelligence”. This article will discuss one of the key topics under Business Intelligence called Data Analytics and the need for Business Analyst to add this skill set to their toolbox.

Data Analytics is the means of distillation of data. IIBA’s CBDA, Certified Business Data Analytics, Certification is a pathway to learning the methods and standards used, by experts around the world, in applying the discipline of Data Analytics to producing actionable insights.

IIBA highlights four methods within Data Analytics that determine the type of insights one may generate. The decision to use one method or the other depends on identifying business research questions needing answers, the methods are:

  • Descriptive Analytics, this method focuses on “what happened”, an historical and current view of events.
  • Diagnostic Analytics, this method focuses on “why this happened”, what may have gone wrong suggesting the reason for success or failure in events.
  • Predictive Analytics, this method focuses on “what may happen”, the data here is based on historical and current data and the use of prognostic modeling to predict a future event.
  • Prescriptive Analytics, this method focuses on “what one can do to make a future event happen”, using predictive analysis to gain insights to results that provide estimates for different actions/result paths.




Data is a foundational aspect to Data Analytics. Data collection and storage present a couple of common considerations, additional data preparation considerations are:

  • Data Wrangling, which cover methods used to “clean and convert data” from one format to another so that analytics tools can use it.
  • Data Extraction, which involve “identifying and capturing the data needed” to answer the outstanding question(s).
  • Data Preparation, which involves “preparing that data for easy use”, highlights several points, they are:
    • Importing, the relevant data sets,
    • Cleaning the data, by removing out of range data, duplicate data, stray data,
    • Transforming the data, treating missing values etc…..
    • Processing the data, by preparing data for analysis by parsing, concatenation etc..,
    • Logging the data, by describing the data sets used, metadata details, data sources used, collection methods used, so others may discover reuse potential and as evidence against generated insights,
    • Backing up the data, so clean versions of the data are available for reuse,


Some hidden benefits of data preparation are that it raises the quality of data so analytics tools can consume that data. As well, prepared data is no longer siloed within a segment of the organization limiting access, rather is available to all authorized users in a reusable form going forward.


When the data is ready, understanding what business research questions are needing insights is key, this will lead to the choice of data analytics method to select that will support the insight model to use that will in turn generate the timely actionable insights the business needs to make decisions.

In conclusion, Business Analysts are increasingly encountering the need for skill in the topic of business intelligence in the form of implementing data analytics solutions in the normal course of work. IIBA’s CBDA certification is a strong first step to gaining the skill to apply data analytics tools and methods. This skill to design and implement processes that generate timely, evidence based, actionable insights management can use to make informed decisions is very valuable to organizations.