At Being Guided we believe that digital innovation in pursuit of delivering digital innovation with AI-powered No-Code technology is best done when engaging on a foundation of thinking and engineering, specifically: Design Thinking¹ and Value Engineering².

Start with Design Thinking

As the name implies: Design Thinking 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. In order to generate receptivity and rapport, empathy is the key to success.

Inspired by the Stanford d.school¹ our Design Thinking is delivered as six iterative stages: Empathize; Define; Ideate; Prototype; Test; and, Implement. This offers a structured yet flexible framework to better understand and challenge assumptions and redefine problems.

Design Thinking is strengthened by Value Engineering²: mapping out a solid use case, financial justification and technology preferences for high-value products and services.

01. Empathize
The first and most important stage in Design Thinking is Empathize. This where creating receptivity and rapport among a broad set of decision-makers and influencers as stakeholders in innovation, leads to trust. In turn, this generate the truth required to move to the Define stage.

02. Define
Clearly articulating the problem to be solved. After gathering insights, define the core problem in a human-centered manner. This stage is about synthesising observations and articulating the problem in a way that guides the creation of a compelling argument for a solution.

03. Ideate
Generating a range of creative ideas to solve the defined problem. This phase involves brainstorming and exploring potential solutions, encouraging out-of-the-box thinking. It's essential for innovation, as it embraces creativity in the discovery of effective outcomes.

04. Prototype
Turning ideas into tangible products. Prototyping means a hands-on approach to the rapid transformation of Current State, generating a simpler, more effective Future State with the right solution. Prototyping is crucial for visualising how the solution will work.

05. Test
Gathering feedback and refining the Prototype. Testing includes feedback collection on reactions to the solution offered. This helps in understanding the prospect's experience, identifying issues, and validating the effectiveness of what has been proposed.

06. Implement
Finalising the solution and closing the deal. The final stage involves finalising the solution design based on feedback, completing the development, and launching the product or service in question. This ensures that the solution is fully understood and ready for everyday use.

Reinforce with Value Engineering

Design Thinking is strengthened by Value Engineering²: mapping out a solid use case, financial justification and technology preferences for high-value products and services.

Value Engineering was originally conceived by Lawrence D. Miles², a General Electric engineer. Miles' techniques have saved design engineers, manufacturing engineers, purchasing agents and service providers millions of dollars.

To quote Miles, it was neccessary to show "why so much unnecessary costs exists in everything we do and how to identify, clarify, and separate costs which bear no relationship to customers' needs or desires."

Value Engineering eliminates waste and determines value over price. This can apply to calculating the cost of purchasing (or crucially, not purchasing) any high-vale product or service in timely manner. It is quantifying time-based value versus the cost of delay or doing nothing.

As Value Engineers, we set the scene mapping your ideal customer's needs with your offering. This is where we apply Design Thinking to enable you to build receptivity, rapport, trust and truth with buyers - early and often.

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

What is the cost of NOT buying the product or service?

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

Summary

One day, we may see AI Coworkers replacing AI Copilots and humans-in-the-loop⁴. However, we do not believe that Google AppSheet Software-as-a-Service (SaaS) apps should be ripped up and replaced by AI-generated Web apps. The core architecture of Google AppSheet is best when enhanced - not replaced - by AI.

Furthermore, it might make sense to keep existing first generation SaaS apps, such as Salesforce, as a data source for new No-Code apps built with Google AppSheet and Gemini AI.

Today, it is AI Augmentation (Copilots) aiding humans-in-loop, where commodity tools, such as Google Gemini AI and ChatLLM provide highly-accessible, reliable and low-cost tools that genuinely improve the productivity of both Citizen Developers and Software Developers alike.

To repeat the mantra: before you apply AI, simplify. In this context the order of work, starting with Design Thinking and progressing through Value Engineering also means taking on board a mindset for simplifying everything business and tech: Fierce Reduction.

With Fierce Reduction I am greatly influenced by the timeless thinking of Apple cofounder Steve Jobs⁵ and also John Maeda⁶ in relation to the need for simplicity in designing digital solutions. It sets the foundation for No-Code First approach to digital innovation.

References

1. The Hasso Plattner Institute of Design. (2004) Stanford d.school. https://dschool.stanford.edu/about
2. Miles, L.D. (1947). The Lawrence D. Miles Value Engineering Reference Center Collection.
   https://minds.wiscon.edu/handle/1793/301
3. Covey, S. R. (1989). The 7 habits of highly effective people. Free Press.
   https://www.simonandschuster.com/books/The-7-Habits-of-Highly-Effective-People/Stephen-R-Covey/9781982137274
4. Davenport, T. H., & Bean, R. (2025, February 15). Agentic AI in 2025: Autonomous systems outpacing human programmers. MIT Sloan Management Review, 66(3), 22–29.
   https://sloanreview.mit.edu/article/five-trends-in-ai-and-data-science-for-2025/
5. Segall, K. (2013). Insanely Simple: The Obsession That Drives Apple’s Success, New York, United States: Penguin Group.
   https://kensegall.com/books/
6. Maeda, J. (2006). The Laws of Simplicity. Design, Technology, Business, Life. Cambridge, Great Britain: MIT Press.
   https://mitpress.mit.edu/9780262539470/the-laws-of-simplicity/