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Failed Expectations: Six Actions for a Better AI/ML Strategy

Unveil the secrets to crafting a successful AI/ML strategy with ‘Failed Expectations: Six Actions for a Better AI/ML Strategy.’ Learn from past setbacks and discover six actionable steps to optimize your approach.

Contrary to the ChatGPT boom, artificial intelligence (AI) and machine learning (ML) technologies have been around for quite some time but only recently gained momentum outside of the tech sectors. 

We’re seeing the topic of AI, in particular, everywhere these days: the news, social media, executive board rooms, and fiscal budgets. Leadership knows adopting new technologies is critical to staying relevant and competitive in the market today and five years from now, but this motive leads to a skewed perception or misuse of the technology itself. In fact, nearly a third of IT executives (32%) say their company leadership sees AI as a marketing tool for enhancing brand perception instead of technology that can drive business outcomes and product strategy. Yes, AI deployment is widespread, but 72% of those IT executives say their leaders don’t fully understand the technical capabilities available, so it makes sense another study found only a slim 12% of companies have a mature AI strategy in place. 

Beyond a general misunderstanding of the tech’s capabilities, the state of AI is facing a number of obstacles. Research reveals company expectations are too high, organizations lack the right tools to propel initiatives beyond the experimentation phase, business and product application falls on the shoulders of the wrong internal team, and ultimately, leadership isn’t prioritizing AI/ML with the urgency needed to be successful.

This creates an AI conundrum preventing many companies from achieving genuine business value through these technologies. Those failing to overcome this quandary and harness the full potential of maturing AI/ML technologies will be left in the dust. Here are six actions to change course for the better:

  1. Align Your Strategy 

Success starts with a goal; a plan is required to achieve the goal, and a strategy is needed to execute the plan. Your first action should be to assess your business goals, then tweak and align your strategy to include realistic expectations of product features and capable outcomes using AI and ML technologies from inception to execution. Education is key; rather than pursuing AI investments in the beginning, consider how understanding these technologies will better inform your strategy – and your spending – before jumping in.

  1. Shift Product Ownership

While the IT department is the primary influencer in adopting and investing in AI/ML, successful execution and implementation occur in product management. To go from internal experimentation to public use, technology ownership must shift to product managers. In applying these capabilities to product features, the management team can identify problems, solutions, and features that bring customers the most value in your products.  

  1. Capture User Feedback 

Once implemented in the product cycle, not only is product efficiency analyzed, but user experience plays a key role in pursuing investment growth in new technologies. Customer-validated feedback is an essential process to improve AI features and support continuous model accuracy. 

This feedback process also provides an opportunity to educate the customer on complex features, such as predictive model techniques, opening a dialogue for new discoveries and real-world applications. 

  1. Assess Monetary Value

This one may seem obvious, but surprisingly, it isn’t for most companies testing the waters of AI. Too often leaders think AI and ML features are essential to compete with the emerging startups and veteran adversaries in the market, which leads to an undercut of the monetary value and revenue gains getting left on the table. 

ML feature functionality offering never-before-seen capabilities and assets that change the product – not just improve it – shouldn’t be given away on a whim. Consider monetizing these features separately to set a new precedent. While assessing the worth of certain offerings is tricky enough, there are data monetization models providing inspiration and guidance readily available to get started. 

  1. Strengthen Data Skills

The toolbox democratizing the application of AI and ML technology seemingly grows every day, but successfully leveraging such tools is predicated on the data you own and whether you actually understand it. In several studies, there’s a clear gap where data analysis and data science skills are needed. While this isn’t new, there’s no indication this problem will go away.

“Data is king” – and it always will be – as it provides a better understanding of our world today and the possibilities of tomorrow. Strengthen these skills by allocating IT investments to data strategy and data management. This single action can be pursued in two ways: 1) partnering with a trusted IT services firm with a core competency in data science and 2) adopting a culture of data through accessible learning initiatives and resources for better data comprehension and application.

  1. Get Started Today

Artificial intelligence and similar technologies aren’t going anywhere anytime soon, so the last action is to get serious and get started today. Organizations should start investing in value-delivering product features now before it becomes impossible – or dangerously expensive – to catch up with competitors and meet market demands.

Now is the time to refine business strategies that will solve the AI conundrum, drive product innovation, and produce real results with better business outcomes. The organizations ignoring this strategic race to a digital future will be the ones with bigger problems than a little dirt in their mouths as they reach the finish line. 

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