January 28, 2025
In the digital world,
Fire-Aim-Ready is not a winning strategy for GenAI
Never have so many spent so much and seen so little in return
In my monthly blog from last February, I highlighted the gold rush mentality of companies spending on GenAI because of a fear of missing out (FOMO) on this new wave of disruptive digital technology. Three years later, the results have shown that over 85% of those investments have yielded little or no return:
The September 2024 survey by IDC documented that:
- 70% of CIOs reported a 90% failure rate for their customer-built GenAI app projects
- 66% reported a 90% failure rate with vendor-led proof-of-concepts
The 2024 Board of Directors Survey from Gartner found that 80% of non-executive directors say their current board practices and structures are inadequate to effectively oversee GenAI.
- A Deloitte survey showed that 45% of board members said that GenAI has not made it onto their agendas at all
The 2024 Enterprise AI Readiness Radar report from Infosys found that only 2% of companies were fully prepared to implement GenAI at scale.
The Global AI Assessment 2024 report found that only 4% of the 1000 plus executives it surveyed would qualify as leaders in GenAI and Data Analytics.
Like most transformative digital technologies, to adopt and deploy GenAI successfully requires a clear understanding of what end result you want it to achieve. It also requires a realistic assessment of what resources and budgets you need to achieve those outcomes.
Developing a Ready Aim Fire Strategy
Too many GenAI projects are just about chasing the new bright shiny object with the belief that it will be the solution to all your productivity and operating challenges. As many companies are now discovering, there is a great deal of preparation required before they are ready to embark on a successful AI project. Here are some of the common mistakes that have resulted in the 90% failure rate:
- Lack of sufficient quantity of data – AI needs a large amount of good, easily accessible data in order to be trained to learn from it
- Lack of sufficient quality of data – As the old adage goes “garbage in, garbage out”
- Underestimating the time and cost of the data component of GenAI projects
- Lack of planning for a GenAI lifecycle model – AI is not a set it and forget it technology
The other reality is that AI is not IT. As such, the skills and knowledge required to successfully acquire or develop and run GenAI programs do not exist in most IT teams. Data engineers and data scientists have very specialized skills and talents that are not easily transferable which prioritizes hiring new AI talent rather than upskilling existing personnel.
Lastly, AI governance is the responsibility of the entire organization, including AI practitioners, line of business users, and C-Suite decision makers – not just the IT shop. This requires all these stakeholders to have sufficient GenAI literacy in order to avoid the mistakes above and get the desired business value from their GenAI investments.
Here are some questions to get you started:
- What do we need to be fully prepared to successfully adopt and deploy GenAI at scale?
- What is the business problem we are trying to solve or the business opportunity we are trying to seize?
- What must GenAI deliver to achieve that outcome?
- Is the quality and quantity of our data sufficient to train GenAI?
- What resources and budgets do we need to successfully adopt and scale GenAI?
- Do we fully understand the costs to move GenAI pilots into production?
- Do we have the necessary skills and capabilities to effectively manage GenAI initiatives?
- What metrics will we use to measure the desired ROI?
- Do we have an enterprise-wide GenAI & Data governance process in place?
A good place to start: Create and launch a GenAI Literacy & Awareness program
Several successful early GenAI adopter companies I’ve talked with started out by creating and implementing a comprehensive and detailed AI Awareness Program to educate their organization about what it is and what they must be prepared to do to get real business value from it. These programs include:
- Inviting GenAI experts into their company for presentations and discussions
- Building a GenAI archive of articles and use cases
- Visits to companies that have successfully deployed GenAI
- Hackethons to generate ideas and processes on how to get business value from GenAI
- Employee business value
- Customer business value
- Meeting with well-established GenAI vendors to evaluate alternative development and deployment approaches
A strategic framework to maximize the business value and ROI from GenAI
The reason so many AI projects have missed their desired outcomes is because they weren’t in service to solving a specific problem and taking advantage of a specific opportunity. They were mostly about deploying a new technology before anyone really understood what it can do and what kind of organizational and operational preparation you have to have in place to do it. Simply put, employees did not see a compelling reason to adopt and utilize GenAI.
In my early GenAI work with clients, we’ve used the 4 Zones Model framework above to identify multiple business value creation needs and opportunities across different operating and functional units. In each case, we identified a specific business value creation priority and then rigorously explored how successfully adopting GenAI could enable or enhance its outcome. As part of that work, we directly tied employee needs, priorities and benefits into the agreed upon implementation program. Here is an initial breakdown of these examples by zone:
Productivity Zone Business Value Creation:
- Employee Productivity
- Reduce time on low value work
- Increase time on high value work
- Automate Routine Tasks
- Call center interactions
- Supply chain optimization
- Cybersecurity
- Faster breach detection
- Better vulnerability monitoring
Performance Zone Business Value Creation
- Personalized Customer Experiences
- Individualized customer offers
- Efficient order fulfillment
- Increased Data Utilization
- Real time insights
- Predictive analytics
- Accurate Lead Generation
- Better demand forecasting
- Better product fit analysis
Incubation Zone Business Value Creation
- Automated Product Development & Testing
- Fail fast learn fast
- Increased time to value
- Customized Written, Audio & Visual Content
- Better content customer match
- Higher product & service differentiation
- Performing Probabilistic Thinking
- Comparative software solution analysis
- Scenario planning tool
Transformation Zone Business Value Creation
- M&A Analysis
- Faster and more accurate due diligence
- Better culture fit analysis
- New Business Assessment
- Operating model compatibility
- Organizational compatibility
- Digital Transformation Evaluation
- Zone offense differentiation potential
- Zone defense neutralization potential
The good news is that the work I’ve done with early adopter CIOs and their senior leadership teams utilizing this approach has resulted in them being able to be fully prepared to successfully adopt and utilize GenAI.
If this is a journey you want to undertake, I’d welcome the opportunity to discuss how we can put this approach to work for you and your leadership team.
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