| Executive Abstract
To position your AI strategic initiatives for success, you need to vet the risks. Were you aware that Gartner research recently reported that cost estimates for implementing AI solutions are typically off by a staggering 5x - 10x. This means if your team or vendor is quoting you $100k, you may need to budget for $500k to $1M. But why are AI solution cost estimates so far off? Prior to AI technology initiatives, the rule of thumb generally used by experienced business-technology management consultants was 2x - 4x even if assuming you had a very experienced team due to reactive human dynamics factors like "Decision Latency", "Optimism Bias", "Dunning-Kruger Effect", "Parkison's Law", "Student's Syndrome", "Confirmation Bias", etc. So then, what is the most effective practice to mitigate these human factors for a successful outcome? Proactive risk management. Actively managing the top 10 business risks of buying or building an AI solution for your organization is crucial to ensure a successful outcome, as it will dramatically mitigate potential pitfalls, lower the cost of implementation, improve both the accuracy or precision of solution cost estimates and therefore serves to maximize the benefits of your AI investments.
10. Reskilling Displacement Risk (Labor Arbitrage via Autonomous Agents)
9. Scheming Risk (Jailbreak, Sandbagging, Cloning)
8. Confidentiality Risk (Public-AI, Private-AI, Hybrid-AI)
7. Biased Output Risk (Unbalanced Data, Temporal Models, Fit-For-Purpose)
6. Data Validity/Maturity Risk (Pretraining, Fine-Tuning, Prompt-Tuning)
5. LLM Trustworthiness Risk (Hallucination, & Accuracy)
4. Gold Plating Risk (Over-Engineering Simple Workflows)
3. Architectural Solution Risk (Non-Functional Tradeoffs)
2. Business Model Risk (AIaaS, SaaS)
1. Ethical & Human Factors Risk (Safety, Security, & Regulatory Concerns)
| Detailed Description of Top 10 AI Business Risks
10. Reskilling Displacement Risk (Labor Arbitrage via Fully Autonomous Agents)
In early 2025, organizations like Salesforce are already deploying AI solutions that are handling 50% of the current team's workload necessitating the need to reskill many personnel. The eventual rise of fully autonomous agentic AI poses significant risks of job displacement, as these advanced systems can perform tasks traditionally done by humans, leading to widespread job displacements and unemployment.
9. Scheming Risks (Jailbreak, Sandbagging, Cloning)
AI scheming behaviors may also inject significant risks, including the potential for jailbreaking or bypassing constraints or guardrails placed on the AI system, which can lead to unauthorized actions and security breaches. Sandbagging, where AI systems intentionally underperform to manipulate outcomes, can undermine trust and reliability. Additionally, the cloning or duplication of AI model weights can result in intellectual property theft and the proliferation of unregulated AI systems, further exacerbating security and ethical concerns.
8. Confidentiality Risk (Public-AI, Private-AI, Hybrid-AI)
It's important to understand whether your AI solution will need to process confidential information (e.g. Personally Identifiable Information in client or customer data) or proprietary company intellectual property (trade secrets, pricing models, etc.). If you need to protect confidentiality, you may need a local on-premise Private-AI or Hybrid-AI implementation in order to secure and protect the sensitive data of your customers and business.
7. Biased Output Risk (Unbalanced Data, Temporal Model, Fit-For-Purpose)
AI bias poses significant risks when models are trained on unbalanced data, leading to skewed and unfair outcomes. Temporal model considerations are crucial, as biases or out of date information can evolve over time, necessitating continuous monitoring and updates. Additionally, ensuring that AI systems are fit for purpose requires careful attention to data-related human factors (e.g. misinterpretation of original intent), as overlooking any of these can result in unintended and potentially harmful consequences.
6. Data Validity/Maturity Risk (Pretraining, Fine-Tuning, Prompt-Tuning)
For data to be valid, mature, and fit for purpose, it must be complete, balanced, timely, consistent, and relevant. First, the pretrained LLM must be correct and rightsized for the goal. Second, the LLM may require fine-tuning with valid data being careful to not over-fit the training data. Third, the prompt context must be carefully crafted and managed to achieve optimal results with respect to pretraining and fine-tuning quality. Similar to humans, AI also requires time to learn and mature — therefore the time it takes may vary considerably depending upon the business case or complexity of the goals and should be taken into consideration when planning any capital investment into AI technology. Nonetheless, many companies are using advanced techniques to help accelerate the learning and maturity process. There are now more than a dozen techniques that enhance the efficiency and performance of model training.
5. LLM Trustworthiness Risk (Hallucination & Accuracy)
LLMs must be fully vetted for a wide variety of vulnerabilities (prompt injection, sensitive information disclosure, supply chain privacy or copyright violations, data & model poisoning, improper output handling, excessive agency, system prompt leakage, vector & embedding weaknesses, misinformation, unbounded consumption, trojan horse attacks, etc.). Furthermore, you may need to predetermine what accuracy you need to achieve. Whereas using AI for creativity related use-cases (e.g. marketing content or design intelligence) leverages hallucination as a benefit but many other business applications typically require higher levels of accuracy or precision. For example, to achieve 95% accuracy, up to 9 independent AI models may be needed in a single solution if each individual model is only 75% accurate. Finally, it's also very important to understand licensing constraints of open source LLMs as very few models are truly open without any constraints.
4. Gold Plating Risks (Over-engineering Simple Workflows)
Given that AI is an automation technology, it can easily be misapplied to the automation of simple workflow task-oriented scenarios that are best automated with a simpler and lower cost alternative non-AI technology. AI automation should only be used on complex goal-oriented scenarios that require complex analysis or decision making. Many developers or vendors may recommend automating simple tasks using AI technology simply to gain experience with AI technology, but this could easily result in a solution with negative ROI. Buyer beware.
3. Architectural Solution Risk (Non-Functional Tradeoffs)
Typically referred to as Non-Functional Requirements (e.g. ROI, Performance, Scalability, Reliability, Stability, etc.) — these concerns typically eclipse the inherent value of Functional Requirements. An experienced solution architect should be engaged to either evaluate (if buying from a vendor) or design the solution (if custom building an AI solution) so that the solution tradeoffs are able to achieve a predictable return on investment (ROI). Given the inherent complexity of AI solutions, ROI won't happen by accident or by luck.
2. Business Model Risk (AIaaS, SaaS)
AI solutions with lower query volumes may benefit most from a monthly or annual subscription but would then risk lower margins that are less predictable. However, medium or higher volume AI solutions may need a more predictable per use or hybrid pricing approach to maintain adequate margins, predictability, and profitability. From a valuation perspective, the Software-as-a-Service (SaaS) model was often capable of attracting buyers at a multiple of 10x-20x EBITDA. The AI-as-a-Service (AIaaS or Agentic AI) model is expected to bring even higher EBITDA multiples than SaaS given that the total cost of development is anticipated to be lower than SaaS development in many cases, therefore the ROI for AIaaS solutions is expected to be higher than SaaS. Companies may also face additional disruptive risks by new entrants launching with a fully automated workforce of AI agents or may even be confronted with having to cannibalize existing products and services with AI agents.
1. Ethical & Human Factors Risk (Safety, Security, & Regulatory Concerns)
AI ethics risks related to safety, security, and regulatory concerns are paramount in ensuring responsible AI deployment and may also require governance, risk, & compliance (GRC) as well as integrated risk management (IRM) processes especially so when dealing with critical life-safety systems. Safety risks include potential harm from AI systems making erroneous decisions, while security risks involve vulnerabilities that could be exploited by malicious actors. Regulatory concerns encompass the need for compliance with laws and standards that protect user safety, privacy (e.g. HIPAA), and therefore help to prevent misuse of AI technologies.
| Executive Summary
Neglecting any of these top 10 business risks when buying or building your own AI solution can lead to significant setbacks or failure for your mission-critical AI initiatives, making it essential to actively engage an experienced emerging technology expert and solution architect to help you navigate and manage these potential threats for a successful outcome.
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 solution to help proactively mitigate the top 10 AI business risks described above.
JS Miller Consulting, LLC was established in 2010 and has been providing best-in-class management consulting in the field of emerging technologies like AI, SaaS, Mobile, Cloud, APIs, 3D/4D visualization, etc. for more than 14-years.
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 for 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 and elevates 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 84% 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.
Engage: 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 Let's Chat button immediately below.