AI independence is a journey that should not be taken lightly, but knowing that it is necessary to continue creating the competitive advantage or general competency for any organization, it has become an essential part of most organization’s roadmaps for the next several years.
It is important to realize that the success or failure of implementing AI depends on who is steering the ship. One must identify the necessary infrastructure, architecture, platform, and other resources and partners that can help an AI initiative be successful. We have just like many data warehouse and digital transformation initiatives over the last 20 years fail because of poor leadership, or companies only going half in on the objective.
While we realize without great leadership or vision of what the end result of implementing a newer, not even cutting edge, but more modern initiative it is difficult to see what lies over the horizon. That is where you need a good partner and strategized initiative to go by in order to measure success.
A people-oriented, holistic, forward-looking strategy will prove to yield great rewards when all the necessary people, technology, purpose are brought together.
The design, development, testing, iterative development, test case fulfillment, deployment, and continual maintenance are understandings that AICG has and continually brings to our customers on any initiative. AI (and technically Machine Learning) are no different here. Generative AI may seem like a new found plaything for your standard consumer, but to a business in is an uncut gem. It is a stone whose value appears to be in its shimmer and perceived value, but the value truly is rendered when a rare set of experts can envision the beauty within, admiring the potential angles, and acute structures and brilliance that will result in their interpretation of that potential, and with the right tools produce something that is brilliant and valuable in perceived and actual value to the one that possess it.
Don’t be mistaken, many resources are trying to convert from analysis to AI engineers, but this learning on the job can be costly to your organization. This is why the recommendation should be to bring in experts in the area who have not only a proven record for success in technology and data integration and development but also proven tools or deployed solutions that speak to your uncut gem vision where alignment is apparent for reaching your goal with the assistance of team that has working knowledge of the solution sought.
The partner will also help you to build a team of data and IT specialists. With an internal group at some point assisting with your Generative AI initiatives with the help of your partner expertise you can continue to leverage your partnership and grow you team in parallel. Data is at the core of AI and generative AI. Not just standard tabular data but data of all kinds. What we used to refer to as unstructured data when compare to structured tabular data. This includes documents such as PDFs and MS Word but also CSV and MS Excel files. It even extends to video, audio, geographic mapping data. In order to understand how this data is integrated and transformed you must work with resources that have a vast array of experience with many disparate data types who can interpret and process data, create algorithms and models to solve these complex, multimodal and multivariable problems for you. We know that top talent is difficult to attract, but in general it is difficult to find. Most resources specialize due to focus, laziness, lack of opportunity, or general sanity consideration. This means for the latest and greatest technology such as Generative AI, finding resources that understand or even more so experts, is difficult if not seemingly impossible without planning to have the resources learn on the job, fail your first few project phases, or deliver mediocre results which might even be worse because there’s few things worse to the analytics of a business than operating and making decisions on incorrect information or data you’ve believed to be truth.
And, yes, this is Big Data, but the importance is in knowing when it is your Big Data it is important to you and your company advantage, and that it could potentially be even more meaningful when integrated with other data (Big or Small) that you may attain from elsewhere such as third party weather data, or industry specific data germane to your business. Ultimately concepts such as Data Lakehouse architecture give you the ability to share your data (to trusted parties or otherwise), for profit or otherwise.
Don’t be confused in that AI and Generative AI can automate everything or that it eliminates the need for good development, data science, and data engineering efforts by great resources. Expert knowledge is still required and learning how to use AI tools may seem to be child’s play but there is still time consumed setting up the foundation on which your AI solution will be based. This setup, configuration, deployment takes time away from existing resource projects and initiatives. We see many resources confused at the moderate ramp up time to do something “cool” with AI but the long tail of fine-tuning, training, and tailoring the AI to your business is still as great as any other initiative’s tasks. We emphasize that good leaders should not be confused by existing team’s foray into AI showing something that glimmers because all that glitters is not gold, even when it comes to AI. AI is a paradigm shift. Companies should realize the potential early. They should work with partners to educate themselves through proofs of concept (POCs) and proofs of value (POVs) and measure the gap between what the know and have today to what they will need 12-36 months ahead.
The opportunity costs of not investing in AI are high. The paradigm shift is great enough that the team you engage on your AI initiative should start early, and give you confidence that the solution can grow with the business and propagate throughout the organization without resistance. Initially team structures will remain the same, some rightfully will want to be part of the latest and greatest AI trend initiative in the organization, but just like every other technological advancement brought into the organization, people will realize that they still have a job to do and that AI will just be a part of that job.
