05 From Digital to AI Transformation: Navigating the Complexities of Enterprise Innovation

Hello, friends. I hope you had a great Monday. I was just going to wrap up but thought I should sneak in an article before I am done for the day. I have always wondered why an AI transformation is unique and more challenging than a digital transformation, and I thought I would quickly share.

Yes, it'll be a refreshing change from all the use case stories that I've been bombarding everyone with so far??. I have to say I hardly touched the surface when it comes to use cases, and we will come back to it later. For those who haven't read my previous article, here it is: 04 How the NFL Reduced Player Injuries by 25%: Enterprise AI Innovation Use Case

To subscribe, please click?here.

AI Transformation Challenge: A Journey Beyond Digital Transformation

From the point of view of AI transformation, it makes sense to ask this question - Why is an AI transformation effort more challenging than a digital transformation initiative? We are just getting over (or maybe I should say "recovering from") the effects of digital transformation. However, yet another wave of transformation around AI possibilities is already looming at us. I feel it's an excellent question to ask because awareness of all these challenges will help us be prepared for this AI transformation journey.

Roadblocks to Successful AI Transformation: Overcoming Skills, Data and Culture Complexity

We can broadly categorize these challenges in terms of overall complexity, data challenges, culture, related change management issues, and regulatory compliance requirements.

Above and beyond the challenges that digital transformation has, AI transformation brings in some additional complexity, one of which is bringing all the skill sets together.

We need to bring in the data scientists, the machine learning engineers, and the cloud engineers to work together to stand up this technology for AI in the cloud. At the same time, we have to engage the domain experts on the business side, who have a good understanding of the business but not so much of what AI can do for them.

When we combine the complexity of setting up the entire end-to-end machine learning platform in the cloud with the data complexities, we can only imagine how challenging it will be. So, in short, bringing the people, the data, the technology, and the processes together to enable an AI use case can be very challenging.

Data Challenges: Building a Robust Data Infrastructure and Governance Framework

Another unique aspect of AI is the requirement to have data in vast quantities and of very high quality. It is essential because the more data we have, the more accurate the models will be in production. So from an enterprise perspective, this means that the enterprise should have a data management pipeline that allows data collection, ingestion, storage, management, and consumption in the AI models. What this implies is that the company needs not only a robust data infrastructure pipeline but also good data governance practices. Making sure that the data flows all the way from collection to consumption constantly without any failure and with good quality is a challenging task.

Overcoming Resistance in AI Transformation: Building Trust

The next one to tackle in an AI transformation effort is the classic problem of change management. AI will directly impact how an employee has been doing their job because it will change the way business processes have been done before. Some tasks may be automated, some tasks may be eliminated, and others may be created. Since this means it impacts the way an employee does their job, enabling this change and building trust when interacting with this new system will be challenging.

Ensuring Ethical AI: Overcoming Regulatory Compliance Challenges

Last but not least is the challenge of complying with the regulatory requirements. AI raises serious concerns around ethical issues, fairness, and bias when models predict outcomes. If the input data has a bias, then naturally, the outcome will also be biased. There are also concerns about data privacy, and these issues have a direct impact on the brand image of a company if they are not implemented properly.

Given this huge risk, the companies have to put in motion a lot of precautionary measures to ensure the models are monitored in production and continuously trained to avoid model drift and ensure model accuracy.?More on this topic later, as this is a huge concern right now!

I hope you liked this post. Please follow me on LinkedIn, subscribe to my Enterprise AI newsletter, and click the "like" button below. And share this post within your network and ask them to subscribe to this newsletter so more people can benefit.

Here's to AI transformation, thanks!

Disclaimer: All opinions are my own. I am speaking for myself only and do not reflect the views of my employer.

#accenture?#ai??#AIadoption??#aiapplications?#aichatbots?#aidevelopment??#aidriven?#aiengineer??#aiethics?#aiforall??#aiforbusiness??#aiforgood??#aiforhealth??#AIimplementation??#aiinbusiness??#AIResearch??#AIstrategy?#AItransformation??#AIusecases??#alteryx?#Analytics??#ArtificialIntelligence?#artificialintelligenceai??#artificialintelligencenow??#artificialintelligencetechnology??#Automation?#aws?#azureai??#azurecloud?#azuredatabricks?#azuredataengineer?#azuredevops?#BigData?#business?#BusinessIntelligence?#CognitiveComputing??#cognizant??#data??#DataAnalytics?#databricks??#DataCulture??#DataDriven??#DataEngineering??#DataGovernance??#Dataiku??#DataInsider??#DataInsight??#DataLiteracy?#DataManagement?#DataMindset??#dataprocessing??#datarobot??#DataScience?#DataSolutions??#DataStrategy?#DataTransformations??#DataVisualization?#DeepLearning?#deloitte?#deloitteconsulting?#deloittedigital?#deloitteindia?#deloitteinsightsmagazine?#deloitteusi?#digital?#DigitalTransformation?#enterpriseai??#enterprisearchitect??#enterprisearchitecture?#futureofAI?#AI??#FutureOfWork??#gcp??#gcparchitect?#gcpcloud?#hcl?#hcltech?#humansvsAI??#ibmwatson??#impactonsociety??#infosys??#Innovation?#IntelligentAutomation?#knime??#MachineLearning?#machinelearningalgorithms??#machinelearningengineer??#machinelearningmodels?#machinevision?#mckinsey??#ml?#mlengineer?#mlmodels?#mlops?#NeuralNetworks??#rapidminer?#RoboticProcessAutomation?#Robotics??#sagemaker?#sap?#sapabap?#sapbasis?#sapcommunity?#sapconsultant??#saperp?#sapfiori?#saphana?#sapjobs??#talend??#transformation??#trifacta?#verizon??#verizonwireless?#watson