The business world is overwhelmed with AI and generative AI discussion. The topic of “innovation” and AI adoption are pressing hard on Technology leaders. Both tech and business leaders on both sides cannot escape this subject.
But out of all the challenges tech leaders face in bringing AI efficiencies to their organization, one remains the greatest challenge of all.
It is what CIO, CTO and IT Directors fear the most when seeking budgetary approval for their new AI, Generative AI project.
However, there are additional challenges to adopting an AI initiative and seeing through to completion and putting into production.
- These projects are a huge investment in time, sometimes 6, 12, 18 months before going to production.
- Investment in talent resources. Do you have the in-house AI expertise to support the initiative from within the company? Even if you outsource expertise you need to have some internal AI, Data Science resource to manage and coordinate the project from your side, project manager.
- Not a quick fix! The impact on the business challenge your new AI initiative is supposed to address may not be realized immediately and realistic expectations must be set in advance.
These challenges and the media screaming how great AI is but nobody knows how to effectively apply its benefits are real. There is one key challenge above all…
Here is one challenge that is by far the greatest challenge.
It is the moment when the CEO, Board of Directors or Owner asks… “where is the ROI on my investment?”
Being able to answer this question correctly and ultimately show the ROI with tangible results in a clearly defined solution is a career changer for both tech and business executives alike. Here’s how you can address this make or break question:
1 | Completely understanding the Business Challenge being addressed is critical. For example, is the current regional/global pricing model too complicated? Are your 56 warehouses being managed manually using human schedulers and xcel creating mistakes. How are you currently managing the multitude of data being generated in your manufacturing operation to address yield and turnaround? Deep dive with an AI expert and fully understand what is happening in order to propose a solution.
There must be a clear understanding of what the business challenge is and how improving it will impact the business. Experienced AI expertise can help with this. Without asking the right questions, how can you measure and determine what a desired outcome is?
2 | Communicating Value when presenting initial AI projects to address business challenges. This value must be presented as a clearly defined outcome with measurable impact on a specific business area.
3 | Finally, the way you present this value is using a data driven report based upon agreed upon success criteria. This must be fact based with clearly understood ROI. People, processes and technologies should all be identified in this comprehensive report. Experienced Business AI expertise (either internal or outsourced) is required to generate a report like this.
In conclusion, whether you are a large, data mature company with an in-house Data Science team doing the work yourself or a smaller company with less mature data collection and processes, limited AI expertise, the challenge of presenting new AI projects to ownership remains the same.
