You don't need an AI strategy
Joey Jablonski
Product | AI | Strategy | Data | Governance | Transformation | Architecture
I know, it goes against everything we read from consultants, executives, advisors and product companies. But they miss what it means to work inside an organization that makes investments based on outcomes & impact and not the shiny factor of new features.
Now, don't mistake not needing an AI strategy with not needing AI. There are many interesting and exciting tools coming to market to improve efficiency of marketing content creation, improve an employee's ability to write a succinct email or to analyze customer support requests and prioritize them based on customer impact. But none of those wins will come from an AI strategy, they will come from the respective strategies of your marketing, sales growth, customer zero and customer support teams.
Simply put, an AI Strategy that stands alone from our corporate objectives & parallel to our existing inventory of technology roadmaps, strategies, vendor evaluations and R&D activities has no chance of realizing the potential value AI brings. Adoption of AI capabilities must come as part of your core initiatives including;
Migrating to SaaS business systems to reduce operational cost and increase velocity.
Decreasing tech debt to lower operational overhead and accelerate development of new capabilities.
Simplify integrations to increase data accuracy and process automation coverage, reducing manual exceptions that are expensive to handle.
Reducing duplicative technologies to minimize integrations and steps for data movement.
Rather than developing an AI Strategy, think about how your organization can;
Empower your Product Managers with knowledge, investment capacity and air-cover to implement AI as part of larger initiatives that build new capabilities for enterprise business users.
Educate Solution Architects on the capabilities available in the market and the long term business needs so they can identify areas where AI can be injected into already funding programs with defined OKRs. Ensure they have time for research, event attendance, vendor conversations and community forums.
Train Business Analysts to leverage tools on the market, experiment with them and begin to build automation where they find quick wins.
Focus your energies on building competency in every product manager, business analyst, functional lead, solution architect and leader across your organization. Enable them to determine the best places to apply AI in the context of their business function and the value chain they represent.
Quick recap; you don't need an AI strategy. You need a corporate strategy for key business functions that factors in modern technology, industry trends, company differentiation, weighted against risk and timelines measured against positive impacts of automation, scale & recommendations. You need a tolerance for risk backed by knowledgeable folks about capabilities that can be applied to business objectives. You need people that are educated in the available technologies and their tradeoffs to ensure that AI is part of the linking & enablement between corporate strategy and execution.
Thank you https://richferguson.com/product/a-i-magic-app/ for the great graphic. #notanendorsement
It’s intriguing to hear about your transition and the insights gained along the way. The shift from consulting to an internal role can indeed provide a fresh lens on the complexities of operational change. Your emphasis on skills enablement over the traditional AI strategy is particularly compelling—investing in people can create a more sustainable integration of technology. How have you seen this focus on skills impact engagement and innovation within your organization? Engaging employees in this manner can fundamentally change how new technologies are embraced and utilized. It would be valuable to explore what specific strategies have worked for you in fostering this environment of organic adoption.
Business transformation | IT operations | Engineering technology solutions
8moVery well put together... It starts with General sense, common sense and progresses with value output, Intelligence, and then AI, Gen AI
Investor and Trader | Senior Business / Technology Executive, Advisor | Digital Transformation and AI | Innovation | Human-Centricity, Sustainability and Resiliency | Industry 5.0 | Board Member
9moJoey Jablonski, congratulations on the new role for you as the opportunity and the mission of the organization seem to position you in an exciting place. It is great to hear your thoughts on AI Strategy and options on approach to develop it. There are many options and alternatives to approaching as long as it is focused on: Business Enablement, works into the Enterprise business and information technology strategy, defines and addresses security, compliance and risks. I look forward to hearing more as you progress through the journey and adventure ahead.
Technology innovator helping companies to deploy AI with ROI 🚀
9moIsn't supporting distributed and applied AI knowledge the AI strategy? Essentially, everyone should be using some form of AI tools. AI has tipped the balance promoting subject matter expertise above technical skills. Use the data gained from the immediate incremental successes to justify the large AI projects.
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9moCongratulations on the new role Joey Jablonski.. They are lucky to have you guiding their AI Strategy. Thank you for sharing your thoughts. Always helpful. Enterprise Data, in many respects, is unprepared for AI given data accuracy and silos. Errors require expensive manual exceptions. If they are not resolved then AI’s impact is limited. Automation facilitates huge improvements to business use of Data. As a next post, can you comment on how you are bringing trust, automation and reliability to Enterprise Data?