At Being Guided I have been working on how our customers can clearly map out a solid use case, financial justification and technology preferences for solutions provided by AI startups.

With Artificial Intelligence (AI) emerging as a dominant feature in Digital Innovation, I see the role of AI as a choice of Augmenting or Automating human tasks and IT systems in an AI Enabling phase. Design Thinking and Value Engineering enables clarity to be applied on selecting the right approach in Future State Models.

Simply put, Value Engineering determines value over price. This is means calculating the cost of purchasing (or not purchasing) an AI-powered Digital Innovation in timely manner. It is quantifying time-based value versus the cost and inaction of remaining with business-as-usual.

As Value Engineers, we set the scene with prospective early adopter customers and AI startups. This is where we apply Design Thinking to build receptivity, rapport, trust and truth. This leads us to explore the deeper implications of what measrable impact the AI technology in question could have.

Design Thinking

At Being Guided we believe that all digital landscapes require a broad set of motivated, empowered stakeholders to engage in Design Thinking. As the name implies: this is thinking (and acting) like a designer. Being curious, restless and constantly challenging business-as-usual. It is all about solving problems in a human-oriented way.

The Stanford d.school¹ Design Thinking method provides six (6) iterative stages: Empathize; Define; Ideate; Prototype; Test; and, Implement. This offers a structured yet flexible framework to better understand users, challenge assumptions and redefine problems.

I have expanded elsewhere on Design Thinking, as it applies to the particular breed I studied for my Master's degree. This is all about applying the Stanford d.school method to a high-value, high-touch sell. You can read more about this in my Sales Thinkers blog post.

Here are the six stages of Stanford d.school Design Thinking:

• Empathize. Purpose: Understanding the users and their needs.
• Define. Purpose: Clearly articulating the problem to be solved.
• Ideate. Purpose: Generating a range of creative ideas to solve the defined problem.
• Prototype. Purpose: Turning ideas into tangible products.
• Test. Purpose: Gathering feedback and refining the prototype.
• Implement. Purpose: Finalising the solution and launching it.

Design Thinking enables all stakeholders exploring any business process as a candidate for AI Augmentation or Automation to achieve a positive, open conversation. As I set-out in my blog post Fierce Reduction, this is also a time to challenge business-as-usual and apply a simplification of processes wherever possible.

In the current startup phase that is all things AI it is a good time to consider not only what we are automating, but how and why we are doing it.

AI Augmentation or Automation

Of course, transformation of Current State processes, practices and IT systems is hard. But in addition to Design Thinking enabling a positive, open and trustful approach to change, it is further reinforced by the hard-nosed financial arguments that Value Engineering generates.

What follows is the creation of a Return On Investment (ROI) Model for your AI-powered Digital Innovation. The ROI Model leads creation of a Return On Investment (ROI) Model for your specific use cases.

Value Engineering focuses on dividing AI-powered Digital Innovation into two categories:
1. Augmentation of human tasks. Introducing the role of the AI Copilots.
2. Automation of human tasks. Replacing human labour with AI Coworkers.

This starts with analysing Current State processes, broken-down into individual tasks, sub-tasks and dependent tasks. It includes documenting the inputs, outputs and actors invloved in each task. Other activities include gathering data related to each task, such as time taken, resources used and frequency of execution.

Before moving on to Return On Inverstment (ROI) Modelling, Key Performance Indicators (KPIs) should be established. Examples could include:

• Time-to-Completion.
• Cost per Transaction.
• Error Rate.
• Customer Satisfaction Score.

We are now ready to divide AI Opportunity Identification into its two categories:

AI Augmentation (Copilot):
Design AI tools to assist employees with tasks, providing suggestions, insights, and automations.
Integrate AI into existing systems like Salesforce, Slack, or Jira to provide real-time support.

AI Automation (Coworker):
Design AI agents to take over complete tasks, potentially replacing human labour.
Define clear rules and decision-making criteria for AI agents.
Implement human oversight and exception handling mechanisms.

This could also include considerations for Data Integration:
Identify the data sources required for AI training and operation.
Integrate AI with existing SaaS apps and other IT to access structured and unstructured data.
Extract information from documents and emails using natural language processing (NLP).

ROI Modelling

From a financial perspective, we start with a simple question for the buyer:

What is the cost of NOT buying the AI-powered Digital Innovation?

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Firstly, let's look at the Return On Investment (ROI) Model - a general formula:

ROI = (Cost of Investment / Net Profit​)×100%

To adapt this formula for an As-Is vs. To-Be comparison, consider:

Net Profit: This will be the difference in profits between the Future State (To-Be) and the Current State (As-Is).

Cost of Investment: This is the cost incurred to move from the Current State (As-Is) to the Future State (To-Be).

Given the above considerations, the formula becomes:

ROI = (ProfitTo−Be​ − ProfitAs−Is​​ / Cost of Transition) × 100%

Where:

Profit To-Be = Profit or (benefit) in Future StateProfit As-Is = Profit (or benefit) in Current StateCost of Transition = Cost to move from As-Is to To-Be

Note: If you're measuring benefits other than strict monetary profits, such as time saved or other intangible benefits, ensure you can convert these benefits into a monetary value for this to be valid.

To calculate the Return On Investment (ROI) from AI-Powered Digital Innovation with the specified inputs, we can formulate several equations. Let's define the variables first:

BVAs-Is = Current State (As-Is) Business Value generated per annum without Solution.BVTo-Be = Future State (To-Be) Business Value generated per annum after investing in Solution.

COS = Cost of Solution.ROI = Return on Investment as a ratio relative to the Cost of Solution.

CoD = Cost of Delay per day when not investing in Solution.CoDN = Cost of Doing Nothing per day when not investing in Solution.

CoDday = Cost of Delay per day when not investing in Solution.

CoDNday = Cost of Doing Nothing per day when not investing in Solution.

Calculating ROI from Solution:Net_Gain - BVTo-Be - BVAs-Is

Calculating ROI:ROI - Net_Gain - CDI / CDIThe ROI is expressed as a ratio. Multiply by 100 to get a percentage.

Cost of Delay (CoD):This represents the loss per day by delaying the Solution purchase. Assume the delay starts from the beginning of the year and goes on for d days:CoD = BVTo-Be - BV As-Is (d x CoDday) - CDI / CDI

Cost of Doing Nothing (CoDN):This is the loss per day for not implementing the Solution. Similarly, for d days:CoDN = BVAs-Is - (d x CoDNday) - CDI / CDI

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This Return On Investment (ROI) Modelling must also be set in the context Current State ('As Is') versus Future State ('To Be'). This is where the quantification of the value of AI and a simplified No-Code technology underpinning it leads to creating a compelling argument for timely purchase.

AI Enabling

As the infographic at the top of this blog post illustrates, the Design Thinking and Value Engineering activities focus on where AI best fits a particular use case: Augmentation (AI Copilot to support human tasks); or, Automation (AI Coworker to replace human labour).

As I explain in my AI Innovators blog post, I introduce the AI work that I am doing with Supercog, a West Coast, US-based AI startup. This includes early adopters and use cases, such as the integration of Salesforce CRM and Electronic Health Records (EHR) for the UK National Health Service (NHS) and automation of Scheduling and Payments for Arts Care.

Summary

By combining Design Thinking with Value Engineering I help organisations of all shapes and sizes to work out the role and justify AI as a choice of Augmenting or Automating human tasks and IT systems in an AI Enabling phase with a startup offerings.

Reference

1. Stanford d.school. https://dschool.stanford.edu/about 

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