Editorial
The Gist
- Failure factors. AI projects fail due to unmet expectations, lack of data pipelines and disruptive results.
- Strategy definition. Define clear AI strategy, prepare data foundation and manage change for AI success.
- Adoption success. Successful AI adoption requires aligning technology with business goals and processes.
Every week, I meet with successful executives ensnared in the same seductive trap: shiny object syndrome, hindering their AI strategy.
Seduced by the lure of “new” and therefore “better,” these well-intentioned leaders are ready to open their wallets and invest in the next big thing on the market — convinced by the promise that new tech immediately solves existing challenges.
Right now nothing is shining brighter than artificial intelligence (AI). Businesses across industries continue to invest heavily in the promise of AI-driven efficiency, productivity and insights.
In fact: in 2020, large organizations were already spending on average $134 million a year on AI.
I get it — AI may not be new to the market, but recent advancements in no-code interfaces and copilots, like ChatGPT and Bard (now Gemini), are making AI more accessible and user-friendly than ever before.
As tech giants like Meta and Google release “open” AI models to enable developers to build their own software using AI, we’ll see increased competition and options, and with that, levels of value, risk, security and cost.
AI will continue to burn brightly, promising to make the impossible, possible, for those willing to invest — and they will: global spending on AI is expected to exceed $301 billion by 2026.
At the same time, AI is primed to burn any business leader buying into that promise without putting in the hard work.
You know, the nonsexy due diligence of defining a clear AI strategy and building the right foundation for AI to drive meaningful, long-term value for your business.
What Causes AI Projects to Fail?
First and foremost — many AI projects are successful. I’m in no way doubting the benefits and advantages AI and other exciting technologies bring to the market.
But, they don’t all succeed. The IDC reported in 2022 that just under one-third of AI projects failed.
The top three reasons for failure? The AI technology didn’t perform as expected or promised; the business lacked the data pipelines for diverse data sources, or the results were too disruptive to current business processes.
Commonly, many AI projects fail for the same reason digital transformation projects fail: Blinded by the promises and possibilities of the new technology, the business fails to adequately:
- Align on the right strategy and goals of the technology.
- Prepare a solid foundation for the implementation.
- Define processes and communication plans for change management within their organization to ensure adoption.
Let’s look at each of these key areas and the things to do today to solidify your AI strategy success in the future.
Define Your AI Strategy to Find the Right Solution
No technology or AI strategy will reliably and repeatedly solve your business challenges just by existing — even the coolest, newest, shiniest toys on the market.
This is important to note — it’s not the tech, ultimately. Those vendors aren’t deceiving businesses with what their tools accomplish.
It’s simply that your business, processes and culture are not one-size-fits-all, and, therefore, no out-of-the-box tool makes holistic, measurable change, if it’s not properly integrated into your business.
You must carefully match and prioritize capabilities, features and tactics to fill gaps, overcome hurdles and reveal opportunities to advance your unique business needs.
You need to define your organizations’ goals and strategy for building, implementing and using AI or any new technology to truly realize full value.
Sample questions to define your strategy and choose the right solution might be:
- What am I trying to achieve (duh!)?
- What do I know/what information do I need?
- Do I have or need an implementation partner?
- What’s the timeframe I need this stood up by?
- What team members should be involved in the decision process?
- Have we exhausted all existing systems? Are we replacing them?
- Should I build, buy, or outsource the solution?
- Who will own this solution ongoing and be accountable for its success?
- What key performance indicators (KPIs) will we use to measure success?
- Will an out-of-the-box solution cover more than 80% of my needs?
With these questions in-hand, you are better prepared to evaluate different AI technologies to achieve your goals, without distraction. But, you aren’t yet ready to open your wallet.
Related Article: 5 CX KPIs Companies Are Improving With AI
Prepare the Foundation for AI
If the walls of your home are crumbling, a new roof won’t get you anywhere.
AI is no different. Without the right foundation to fuel your AI strategy, very few tools or tactics will enable you to fully realize its value.
It’s garbage in, garbage out — at an unprecedented speed and scale. With AI’s computing power, you can make strategic decisions much faster, and with the wrong inputs, you can make catastrophically wrong decisions that much faster.
Let’s look at two key areas to set yourself up for AI strategy success long-term.
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1. Reliable and Integrated Data
I could write 20 articles on this topic alone. That garbage in? It’s your data. That garbage out? It’s your marketing, forecasting, digital experience and decision-making.
Data fuels effective AI. It’s the basis for generative AI’s content creation; the numbers feeding your business models; the basis of predictive recommendation engines and more.
If your data practice is messy and unreliable, then the AI output is messy and unreliable. Sure — it may get it right a percentage of the time, but I wouldn’t bet my business on it.
To prepare your company to continuously make better, more productive use out of AI, you need to first standardize your data practice.
At a minimum, get your house in order by:
- Cleaning and normalizing your data
- Connecting your data and tools (in a customer data platform)
- Building a data layer (to continue to capture, normalize, and action on data)
- Standardizing KPIs and reports for your AI strategy
- Testing. Learning. Optimizing — repeat.
Related Article: AI in Business: How Company Leaders Are Taking the Plunge
2. Brand and Content Rigor
AI is only as good as its data and training.
Many businesses are using third-party AI, but aren’t bothering to train it on their brand, voice and preferences. That’s all good if a human intervenes, but to scale your use of AI and extend it across your organization, you want it trained to embody your brand and best represent your business.
Set yourself up for success by clearly defining your brand and voice upfront. Document descriptive brand governance and editorial guidelines that specify everything from your voice to your brand identity, colors, visual assets, fonts, logo use, photography expectations, etc.
Document common customer journeys, FAQs, brand stories, customer service triggers, and more, to train the AI to understand how you want it to handle common prompts and inquiries.
The more you can define, document and train both the AI and your team, the higher the likelihood that you receive high-quality, trustworthy, and usable results.
Plus, this rigor empowers your teams to act as brand custodians and catch any mistakes AI creates along the way.
Related Article: AI in Business: Friend or Foe? Choose Wisely
Smooth Adoption Through Change Management
Your employees are likely using AI already in a million different ways. Your goal is to synchronize their use cases, so everyone is using the technology in a coordinated and controlled manner, to protect your data, privacy and brand.
Like any new technology implementation, standard processes and clear communication are a critical part of change management to maintain consistency and increase adoption.
Build data fluency within your teams, so they understand how AI works and why following data standards and processes is so important.
Encourage employees to experiment and become more efficient with AI, but set clear guardrails on how to protect your data, maintain work quality and deliver on business promises with the same level of attention expected from a human.
Related Article: How to Pick the Right Flavor of Generative AI
AI Success Starts Before Your First Prompt
I get it — it’s more fun to play with AI than to train it. It’s more invigorating to buy a tool to solve your biggest business challenge, than to admit that you must solve institutional challenges first.
The hard work of AI strategy may not be sexy, but rolling up your sleeves is frequently the make-or-break difference in whether your new technology investment succeeds or you’re shopping for the next one a year from now.
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