AI is at the heart of digital disruption; By end of 2019, 40% of DX initiatives will use AI services and AI will be the technology that will propel DX. By 2021, 75% of commercial applications will use AI; By 2022, 75% of IT Ops will be enabled by AI; by 2024, By 2024, AI-enabled interfaces will replace 30% of today’s screen-based applications; By 2024, 7% rise in AI-based automation will drive new wave of business processes.
Here are 13 ways you can succeed with AI in your organization:
- AI will help improve businesses today and tomorrow: increase competitiveness, improve customer engagements, accelerate innovation, improve margins and employee productivity.
- Integrate AI with your corporate strategy.
- Data is and will be the basis for sustainable competitive advantage.
- Multiple key elements need to come together for AI success: Data, Talent Mix, Domain Knowledge, Key decisions, External Partnerships and Scalable Infrastructure.
- AI is hard. Persistence and perseverance will win.
- Most of the folks are focused on ML and linear or logistic regression is the common data science method in use.
- Always start with a business goal; explore and fine tune your AI initiative to meet that goal.
- Strive for a top-down endorsement of your AI strategy.
- Don’t be caught waiting for perfection or analysis/paralysis, for example, don’t get stuck on perfect data quality prior to experimenting. Learn from the experiments and strive for ongoing improvements.
Similarly, don’t be stuck waiting on “Information Architecture” or all the governance to be in place prior to starting to experiment or achieving success with a use case or two. - Focus on low hanging fruit as a start – areas where you never had a solution in place. For e.g. look for exploiting the power of AI for extracting insights from unstructured content (text captured in your CRM systems), link it with ERP system and enable Accounts Receivable to resolve stuck payments.
- Treasure your data; explore ways to compound the value of your data by pairing it with external sources of data.
- Build a center of excellence to ensure data scientists, data engineers, data architects and all the relevant stewards of data in the value chain are aligned with the same business goal and metrics. Some organizations may have a central data science team while some may have AI talent dispersed through the LOB teams with a few core specialists.
- Ensure ongoing enterprise data governance practices performed jointly by IT as well those in business and compliance functions.
So, how do you become AI Determined?
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