This might feel like a quant post, but it’s more than macroeconomics. The data field is already feeling the ripples of economic forces. ZIRP (Zero % Interest Rate Policy) has ended, which means that the era’s hiring, pricing strategies, budgets, and leadership philosophies are also at an end. The story begins with macro factors, but it’s really a growth story about how badly businesses need their data teams to take on a new role.
This is part 1 of what AI-first means for small and medium-sized enterprises (SMEs). For them, part 1 is just getting momentum to begin doing more than reporting and dashboards. In part 2, I will discuss working within SMEs' constraints and advancing through the maturity model. Here, I will explain how we can take advantage of the current economy to overcome inertia.
Do SMEs Need Data And AI Strategy?
Even at SMEs, data teams need someone who can develop and adapt a data and AI strategy. The connection to strategic goals and value creation supports leveraging the technology for growth. Whatever you do, don’t let a consultant own that function. What’s effective for Fortune 500 and Global 2000 companies doesn’t work for SMEs.
Data and AI strategy is critical for the data team and business’s success, so if someone else owns it, they own your future, too. I’ve talked to several business leaders who spent millions on data and AI strategy consultants but got fleeced. There are good consultants, but business leaders cannot distinguish between capable and con artists. The data and AI strategy consulting field must mature before they become reliable partners for SMEs.
***End Rant***
Hire someone with experience. I’m happy to make referrals and introductions to people I have trained. If a data team member is interested in the AI strategy or product management career path, upskill and mentor them into the role. Whatever route you choose, ensure the data team helps define how the business monetizes data and AI and owns its value creation.
Data and AI strategy at SMEs must be lightweight and pragmatic. It is rarely implemented all at once, and we typically start with no buy-in or C-level sponsorship to build it. Today, it’s easier to get buy-in than in the past by focusing on an emerging business need.
What’s Changed? Easy Growth Isn’t Coming Back.
Behind every threat are opportunities to help businesses navigate it. These quotes should be a wake-up call for data teams across industries, but knowing what we should wake up and do requires more context.
“The Committee does not expect it will be appropriate to reduce the target range until it has gained greater confidence that inflation is moving sustainably toward 2 percent.” - FOMC.
“it's unlikely that market interest rates will return to levels that prevailed before the Covid-19 pandemic” - Janet Yellen.
Business leaders have been waiting for a clear signal about US interest rate policy before making any changes to strategy. They needed to know if monetary policy would resume its role as a growth driver. The FOMC delivered the first dose of clarity they were looking for. Hotter-than-expected US inflation numbers likely set that policy in stone. Janet Yellen piled on as well.
The US Federal Reserve signaled that interest rates will remain higher for longer. As more voices and evidence added support, business leaders’ hopes for a return to easy money evaporated. Growth won’t come from monetary policy or other external tailwinds. The business must find internal sources of growth, and that’s the opportunity.
In this article, I will explain the implications for data teams, the opportunities being created, and how we can take advantage of them.
What’s The Data Team’s Opportunity?
Uncertainty is always a data use case, and there’s a lot of uncertainty about what happens next. I covered this last year, but it’s worth revisiting now.
What do higher interest rates mean for your customers?
How is persistent inflation changing customer spending habits?
What do interest rates and inflation mean for the business’s input costs?
How will those changes impact competitors and the emergence of new competitors?
Any data you can provide to reveal opportunities, options, and threats is valuable for strategy and scenario planning. Insights into downstream changes are equally helpful. Write up a list of business questions the data team can answer and planning processes you can support. Circulate that document so everyone knows how data can help reduce uncertainty.
Training senior leaders and external teams to think of the data team when they face uncertainty changes their perception. Data teams should be seen as partners, but the rest of the business doesn’t know what we can do. Explaining our capabilities with easy-to-understand problem categories helps teach the data team’s value. Managing uncertainty isn’t our only opportunity.
“Higher for longer” policy is designed to slow growth, and it’s working. Businesses need a new input for growth or a lever to pull that will deliver a competitive advantage and allow them to grow by taking market share. Most CEOs are putting out an all-hands call for help, and we can create another perception change.
Data, analytics, and machine learning can be the lever. CEOs will listen to your data and AI product ideas, even if they haven’t in the past. They don’t see a path for anything else to create similar growth. Senior leaders are more willing to change after the magnitude of the threats and opportunities become apparent, and that’s happening as we speak.
Satya Nadella said he’s seeing flat or negative growth in developed economies when adjusted for inflation. Economies need a new input to restart their growth engines, and Nadella believes AI is that input. His and other technology leaders’ voices have convinced CEOs.
The data team’s opportunity is significant, but we must move quickly and pragmatically or risk losing momentum. The knee-jerk reaction to a downturn is cost-cutting to maintain profits while revenue shrinks. If data teams can get in front of the next layoff cycle, there’s a window to make the case for growth.
Telling The Right AI Story By Focusing On Opportunities
AI hype has overcome common sense and created unrealistic expectations for returns. CEOs who told a tall AI tale last year or the year before must execute this year. They must quantify AI returns and the company’s growth trajectory on a quarter-to-quarter basis. Investors are unforgiving when they don’t do both.
These are front-of-mind themes for CEOs because they are getting a daily barrage of examples. Adobe is the most recent. Businesses are in the “Find Out” stage of their AI stories. Any company with exposure to AI and a plausible story saw a boost from it. Investors are parsing this earning season to separate companies that are getting the most significant lift from companies being disrupted the most.
Palantir, Oracle, and UiPath are examples of companies that have benefited from delivering results. Greats like Salesforce, Intel, and even Apple are on investors’ watchlists for potential disruption. Dozens of other companies have suffered double-digit losses after AI impacts failed to materialize.
What started in tech has spread across industries. Investors reward best-in-class solutions and take money away from companies that can’t quantify returns.
Investors and the company’s Board of Directors ask CEOs, “What’s your AI Strategy, and when will we begin to see top- and bottom-line impacts?” CEOs turn to their executive teams and ask, “What’s our AI Strategy? How much will it improve productivity, and what kind of cost-cutting is possible? What are our revenue opportunities?”
Executive teams are leaning on their data teams. “What’s our AI Strategy? What internal productivity initiatives are on the roadmap? When are you shipping our first AI product to customers?” It’s a telephone game, and seeing the chain helps data teams meet the primary driver by supporting the CEO.
Having answers is critical to changing senior leaders’ perceptions. Data teams will either position themselves as the primary growth driver for the next five years or be a target for headcount reductions. Today, most senior leaders view the data team as a cost center, and delivering growth is the only way to change that perception.
What Does It Take To Succeed?
Data teams must support new activities. I’ll start with high-level categories and expand on each one.
Inform opportunity discovery and use case selection.
Connect the business’s AI story with execution through initiatives and continuous delivery.
Catch up with peers and accelerate returns by optimizing delivery, consolidating tools, and avoiding pitfalls.
Estimate impact upfront and track value creation quarterly (improve the business’s KPI maturity).
These don’t define a traditional data scientist, engineer, or analyst’s skillset. However, talent is no longer measured in technical knowledge. It’s measured in tangible business returns.
The new data unicorn turns technology into top- and bottom-line impacts. Everyone else is being cycled out or commoditized. Consider initiatives in a new framework that connects solutions with opportunities and business goals.
Maintain the link between your work and customer or internal user needs. Connect those needs to the solution you’re building and its impact on C-level goals or KPIs. Always estimate ROI upfront and track value creation after delivery.
Only use data, analytics, and machine learning when no other technology can support a use case. Avoid initiatives with incremental value. Consider the data team a finite resource, so opportunity discovery, initiative prioritization, and continuous delivery optimize its value.
Teach external business units and teams at every opportunity. Most don’t know how to articulate their needs, and they believe that Generative AI is the answer to every question. Showcase companies that have delivered chatbots that promptly flopped. H&R Block and Intuit have both struggled recently.
Provide examples of other companies using data, analytics, and machine learning. Discuss the types of benefits and returns those companies are realizing. Present the use case as a story or case study rather than a technical solution overview.
If you encounter inertia, add more cautionary tales. Discuss companies that are falling behind. Google is a good example, but there are others from retail, finance, manufacturing, healthcare, and other traditional industries.
Once you get the green light, deliver as frequently as possible, ideally every 6-8 weeks. Continuous delivery creates feedback loops that keep solutions close to needs and value. The longer the span between deliveries, the more solutions drift away from both. Iterative delivery generates near-term returns.
Each success extends the data team’s track record of success. We build coalitions with people who evangelize how the data team’s work impacted them. Coalitions build trust with senior leaders and external teams, which is how we get buy-in for more complex initiatives with higher value.
Part 1 is the jumping-off point. It’s how data teams go from 0 support, sponsorship, and buy-in to the business seeing the data team as a leaver to reduce uncertainty and create growth. Next, it’s time to advance on the road to AI-first.
Clarion call for the current AI age. Powerfully argued and written.
Hi Vin, I am a lead AI PM at health insurance company and working on calculating impact upfront and prioritising different initiatives. However, calculating impact upfront sometimes could be challenging as there is so much uncertainty/complexity in the business process that is going to consume it. It makes it difficult to track ROI as well. Sometimes, it is not possible to collect metadata related to a baseline process. Doing impact calculations takes time and you have to balance it with not losing the project to external vendor or competing internal team or stakeholder interest. I was able to connect the dots (model output to business KPIs) for some of the projects and it makes decision making super easy for everyone. So totally get the value but it could be challenging for some data products.