20 Critical Concepts in Checklist of Chokepoints or “Missing Parts” Before Buying, Building or Doing Agentic AI Agents
If Your AI Project Fails, You Will Certainly Lose Your Job, Your Business and Customers and even Face Litigation – See You in Court.
Editor Note: This video was written and produced to “Fix the Agentic AI design flaws” from the more than 300+ companies that were produced into video news stories available on ChannelPartner.TV.
IMPORTANTLY, ALL companies missed a number of key concepts needed for high-performance, risk-less and user-centric AI systems.
Bottom-line – Fix these issues before you buy-build or do AI:
1 – Consider starting with new “greenfield” problems, rather than trying to fix a legacy problem that can’t be fixed, or very difficult to be improved upon. Or as one person said “If you automate a bad process, you just get bad results faster.” Or if you must, start not with CEO problems but problems everyone has and if it works you get a huge fan base to build on.
2 – Capacity – beware of assigning AI to teams who are already over capacity, get “fresh” new people to help.
3 – Creep – user use cases result in scope creep beyond expected plan.
4 – Build a Cost “business case” analysis of everything and anything. Before you can figure the ROI, you need to know the TCO. From AI infrastructure and all its components to everyone and anyone who plans, programs, evaluates and uses, capture all the costs known and guesses for the unknown and expect it to be a lot more than you planned for.
And to expedite the ROI analysis, get someone from Finance to be on the team.
5 – Capture Institutional Knowledge – colleagues who know systems and processes better than anyone else, so before they leave or retire ensure “human intelligence” data is captured, including all levels in the organization.
6 – Cleanse – data gathering is a continuous cleansing process, along with remembering “garbage in = garbage out”, as data parameters continuously change, with ever-evolving data fields, data taxonomies, ontologies along with meta tagging, and as cross organization data points are integrated.
7 – Condense – raw, even structured data, needs to be organized or condensed into human usable processes already in use. Develop a methodology or algorithm, that is understandable and practical by the team. This includes short and long-term memory management.
8 – Collaborate – is where problems and processes are identified, and collaborated across the company, to determine usable, measurable, and actionable outcomes and financial metrics. This also is the phase to design “user use cases”, not just “use cases” to personalize agents to specific users most in “need” of the solution.
9 – Communicate – then develop a pilot app led by those “all-in”, then test for as long as it takes, continuously try to “break it” by trying any, and all possible “wild card” user interactions, including simulations.
10 – Continuous control – including benchmarks and version control, to reverse and repeat previous steps. Release to small and then add users carefully, and stop immediately with any problem. Have a crisis team on alert before public launch, including a user support group and even gamification.
11 – Contain and Kill – couple any hallucination or public crisis with a “kill switch”, to move back to previous version, identify and document problems, review results and release carefully.
12 – Cross check testing against existing and beforehand with new corporate applications. Then repeat often as seemingly all applications update constantly without warning or guidance.
13 – Cybersecurity – check with and against existing cybersecurity solutions and add cybersecurity to the list for new AI solutions.
14 – Comply – get third-party compliance certification, for industry and governmental laws and regulations, along with a new internal AI auditing team.
15 – Concealment and sabotage – A new study found that 44% of GenZ admitted to “sabotaging” AI.
16 – Build Content – create, maintain and update training for everyone at every level in the organization, along with integration into existing training such as IT and even change management.
17 – Checking and testing for intelligence. While there are no A I Q standards yet and few AI testing services so far, independent AI Q testing services will be a vital requirement for ongoing AI maintenance and validation.
18 – Catastrophe – when a major LLM “accidentally” releases source code to the public, one must also ask what else they lost – maybe your data or IP-intellectual property.
19 – Create Again – means creating a new team to develop a new version, as this process has changed users and organizational structure and being ready for an ever-evolving ever-faster approaches such as B2A–business-to-agent and agent orchestration and more.
20 – Candor – above all be transparent, honest and truthful as you will need everyone to make sure this really works.
Bottom line – AI also changes the way humans interact with other humans which changes the way humans work.
If you want to fix these issues:
email cross@gocross.com to get help today.
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More references available.
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