| Executive Abstract
AI technology advancements are accelerating at breakneck pace; therefore, launching an effective AI solution may be imperative for proactively sidestepping disruption from new entrants or keeping pace with business innovation to maintain a competitive edge. To navigate this complex terrain, it is crucial to understand how to effectively get started and how to identify the key factors that impact both business value and risk (see CEO Alert #1 on Top-10 AI Business Risks Every CEO Should Know). Initiating your AI journey involves assessing your customer challenges, your organization's specific needs, setting clear goals, verifying data quality, and identifying high-impact use cases that align with your strategic goals. The most important considerations include evaluating the potential return on investment (ROI), understanding the implications of data privacy and security, ensuring ethical and responsible AI deployment, and validating product-market-fit (PMF). By carefully balancing these elements, organizations can harness the transformative power of AI to capture more value while mitigating potential risks, ultimately paving the way for sustainable growth and success. Exploiting the potential benefits of AI technology requires following a proven process purpose-designed to maximize the benefits while minimizing the risks — the 5 step process below is based on over 30 years of R&D experience to achieve a success rate exceeding 90% in contrast with other processes such as Waterfall or Agile which collectively rarely exceed 30% success when measured across projects of all sizes and all industries.
1. Identify Rightsized Problem (Validate Goal, Scale, Data, Risks, & PMF)
2. Develop Business Case (Validate Use-Cases, ROI, Durability, & PMF)
3. Deploy Proof-Of-Concept Prototype (Validate Team, Architecture, Sales, & PMF)
4. Iterate First 3-Steps (Mitigate Top-10 Risks)
5. Go-To-Market with MRP (Minimum Remarkable Product)
| The Detailed 5 Step Process that Kickstarts an Effective AI Solution
1. Identify Rightsized Problem (Validate Goal, Scale, Data, Risks, & PMF)
Effective AI Solutioning best practice is to start by identifying a complex goal or a problem at scale, ensure you have the right kind of data for the AI solution, identify key risks, and to then validate product-market-fit (PMF). First, it's important to identify a complex goal (one that is dynamic, uncertain, or non-predictive in nature) in contrast with a simple goal (one that is static, stable, or predictable). The former is optimal for an AI solution whereas the latter is best suited for simple automation technology less costly than using an AI solution. Second, it's important to verify that the goal is a complex problem of scale capable of supporting the investment of an AI solution. Third, it's important to verify you have the right kind of data that the AI solution can learn from and it is advantageous if the data is proprietary and not widely available (see Durability in Step 2 below for additional information). Fourth, it's important to identify key risks for mitigation to help minimize the cost of the AI solution. Finally, it's important to validate product-market-fit (PMF) by conducting research with your existing customers, partners, or employees to verify that the problem is one they recognize and support. To do this effectively it's important to ask these stakeholders questions about their challenges specific to a certain area without actually describing the problem you have in mind in order to avoid "framing bias" and "confirmation bias".
2. Develop Business Case (Validate Use-Cases, ROI, Durability, Risks, & PMF)
The next step of the process is to decompose the complex goal or problem from Step 1 into one or more high-level use cases that define the specific jobs, tasks, and objectives. The tangible business benefits for each use-case should then be quantified when possible. If the benefits are intangible, the expert judgement of an executive-level sponsor can be relied upon after consulting with an experienced AI Solution Architect to more accurately verify the cost of development being careful to account for the expected monetary value (EMV) of relevant risks. Afterwards, the sponsor and architect can calculate the ROI, Breakeven Period, IRR, NPV, etc. as needed. At this stage of the process, it is often times very important to verify that the solution would have long-term durable advantage (not easily copied by competitors). Again, same as in Step 1, the final step is to validate product-market-fit (PMF) by verifying directly with end customers and users whether the use-cases solve the problems in a way that meets their expectation and satisfaction. If the solution is successfully validated, it's now time to build a team (internal or external) to either buy or build a solution that aligns with the constraints of the business case so that ROI benefits may be fully realized.
3. Deploy Proof-Of-Concept Prototype (Validate Team, Architecture, Risks, Sales, & PMF)
Now that a solution has been identified, it's important to validate that the team assembled in Step 2 is the right team with the requisite rapport, skills, and experience to get the solution across the finish line successfully. The best way to approach this is to deploy a quick and low-cost proof-of-concept (POC) prototype to help evaluate the team's ability to execute and also as preparation for further market validation. After the POC has been deployed, the architecture can be vetted for performance and cost, the business case sales assumptions can be verified, and early adopters can be surveyed to validate product use and adoption as well as product-market-fit (PMF).
4. Iterate First 3-Steps (Mitigate Top-10 Risks)
By this stage of the process you should have a well established and prioritized risk register that clearly identifies the top-10 solution risks (the biggest risks are typically things like the skillset and experience of the team, communication, management support, AI technology related risks, and product-market-fit). The first 3 steps of the process should now be repeated iteratively until all of the key risks have been sufficiently mitigated to the satisfaction of the sponsor and architect. Once completed, you should now have a minimum remarkable product (MRP), a product with strong Product-Market-Fit (PMF) characteristics. This is in contrast to a minimum viable product (MVP) which traditionally emphasized the smallest functioning product to test product-market-fit (PMF) but without validating product-market-fit (PMF) along the way.
5. Go-To-Market with MRP (Minimum Remarkable Product)
The last step is to develop your Go-To-Market (GTM) strategy and plan for a full release of your new AI Solution. It's important to note that the project should not be closed or ended yet but rather extended for a short protection period after deployment to measure and verify that the benefits and costs projected in the business case of Step 3 are materializing as expected. This final stage of the process typically includes both Organizational Change Managment (OCM) and Benefits Realization Management (BRM). OCM may also be deployed during the project to support better internal communications, act as champions to address employee concerns, develop standard operating procedures (SOP), and provide employee training whereas at the end of the project OCM typically focuses on remediating issues blocking end user adoption. Like OCM, BRM may also be deployed during the project to help identify, plan, and define KPIs useful for measuring the benefits whereas at the end of the project the focus is on ensuring the solution achieves the pro-forma ROI forecasts.
| Executive Summary
The 5 step process described herein to kickstart an effective AI solution is just an initial step to quickly get started; however, depending on the size of your organization you may need a more comprehensive AI-centric strategic plan that addresses multiple areas within your organization.
This 5 step kickstart solutioning process is different from traditional processes and is based on over 30 years of R&D experience to achieve a success rate exceeding 90% in contrast with other processes such as Waterfall or Agile which collectively rarely exceed 30% success across projects of all sizes and all industries. This represents an improvement of well over 200% when compared to Waterfall and Agile.
Whether you have an existing AI initiative that is already in-flight or are contemplating launching a new AI solution, you may benefit from a free AI strategy review that strategically audits your AI strategy, approach, or solution to help proactively manage the process and risks described herein.
JS Miller Consulting, LLC was established in 2010 and has been providing best-in-class strategic management consulting in the field of emerging technologies like AI, SaaS, Mobile, Cloud, APIs, 3D/4D visualization, etc.
Jonathan S. Miller is the founder of JS Miller Consulting, LLC and has more than 30 years of experience in the business-technology space working in a wide variety of industries including startups, non-profits, and large global companies with up to $16B in annual revenue.
CAPstone Strategy™ is Jonathan's proprietary framework that comprises more than 24+ best practice capabilities synthesized, developed and refined over a 30-year period. It was created and brought to market for the purpose of mitigating the problem of corporate annual worldwide losses well in excess of $1.2T annually worldwide (about 5% of US GDP) that result from failing projects. It represents Jonathan's passion, life work, value proposition, and industry contribution for elevating the success rate of complex mission-critical technology projects from the dismal worldwide average 28% to over 90% success. CAPstone Strategy represents an improvement of over 200% that has helped many companies save years and millions of dollars as well as facilitating as much in new revenue.
Success Stories: Some of Jonathan's most notable success stories that leveraged his CAPstone Strategy™ framework include saving a client $1M on the first day of a consulting engagement by spotting an issue in the system architecture. In 2020, Jonathan was able to help one client reduce their project portfolio by 87% saving years and millions of dollars. In 2008, Jonathan was the lead solution architect on a bankruptcy clearing project that helped a $1B airline emerge from Chapter 11 bankruptcy with a technology solution that generated an extra $70M annually, making the airline an acquisition target by another airline, thereby saving over 5,000 jobs.
Let's Chat: If you would like to meet with Jonathan to explore strategic options relating to your mission-critical emerging technology initiatives (AI, SaaS, IT related issues) you can book an appointment by clicking the button below.
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