According to a recent report from the IBM Institute for Business Value, over 75% of enterprise executives believe they need to adopt generative AI quickly to keep up with competitors.
Yet as these same enterprises rush to adopt AI, many have hit the same frustrating barrier: their data isn’t ready.
Indeed, this common pitfall seems so close to ubiquitous that Snowflake CEO Sridhar Ramaswamy has woven it into Snowflake’s core messaging. The message? That there is no AI strategy without a data strategy.
Even so, the stakes are getting higher by the day. Leaders understand that the AI rat race isn’t going to halt while they scramble to shore up their data foundations.
Desperate to meet these challenges with the urgency they deserve, these enterprises have been forced to look to outside expertise in the form of dedicated data and AI consulting services.
Why Data Strategy Must Come First
Before enterprises can embed AI into workflows, they need to ensure their data meets four essential criteria. The laws of “garbage in, garbage out” mean data across the entire enterprise needs to be:
- Unified: Disparate systems, from legacy ERPs to modern SaaS apps, must be integrated into a single data fabric.
- Trustworthy: Data has to be governed, monitored, and explainable to meet regulatory requirements and build organizational confidence.
- Timely: Real-time data pipelines are essential for AI to deliver predictive or prescriptive insights.
- Accessible: Business users and technical teams alike need self-service access to data in ways that fit their roles.
Without these bare-bone data fundamentals, AI outputs become that much harder to trust or explain, resulting in expensive AI initiatives that stall or even fail outright before they ever get off the ground.
Why External Expertise Matters More Than Ever
Even when organizations understand the need for unified, trustworthy, timely, and accessible data, execution can become the next major stumbling block—especially when overburdened internal teams try to do it all themselves.
That’s where external data and AI consulting partners can create disproportionate value, providing enterprises with:
- Cross-industry experience: Lessons learned from other sectors accelerate innovation and help avoid costly missteps.
- Depth of technical expertise: From modern architectures like Snowflake to advanced AI tooling, external teams stay ahead of the curve in ways internal teams often can’t.
- Change management objectivity: Transforming data and AI isn’t just about technology. It’s also about culture. Outside experts can bridge silos, align stakeholders, and keep projects on track in ways that might be all but invisible to internal observers.
- Faster time to value: With proven frameworks and accelerators, partners help organizations move from idea to outcome in a fraction of the time.
By bringing in a dedicated data and AI consulting partner, enterprises can bypass the inertia of internal bottlenecks and focus on what matters most: delivering measurable business impact from AI investments.
From AI-Ready to AI-Powered
Once a solid data foundation is in place, AI-empowered consulting helps organizations unlock the true potential of all that data.
That means designing intelligent workflows that span customer experience, operations, compliance, and forecasting. It also means embedding explainable AI directly into business processes so teams can trust and act on recommendations with confidence.
Finally, it means building tailored solutions that balance cutting-edge models with urgent business needs, while never losing sight of governance and risk management.
In short, AI consulting ensures organizations don’t just “experiment” with AI. It makes sure they operationalize it in ways that are measurable, scalable, explainable, and secure.
Data and AI Consulting in Action
Hakkoda, an IBM company, was founded on the belief that great data engineering and AI enablement belong together. By designing modern data platforms on Snowflake and facilitating multicloud environments that unify fragmented ecosystems, we set enterprises up to implement AI-native workflows across a range of core functions, from compliance monitoring, and risk modeling, to demand forecasting and customer engagement.
We also embed best practices in governance, observability, and security into those same workflows, so enterprises can scale confidently and easily keep up with regulatory requirements.
With the combined strengths of Hakkoda and IBM, organizations gain not just technical horsepower but also the industry expertise needed to modernize complex data environments while accelerating AI adoption.
Walking the Tightrope Between Data Fundamentals and AI-powered Outcomes
With a whopping 72% of high-performing CEOs reporting that their competitive advantage depends on who has the most advanced generative AI, it’s not surprising that the rest of the market has taken this same sense of urgency to heart.
But in the rush to get out ahead of their competitors, executives run a very real risk of sabotaging AI initiatives by neglecting the bare essentials. Enterprises that treat data and AI as separate initiatives risk wasted investments as progress stalls or they fail to keep pace with emerging regulations.
Those that embrace an integrated data and AI consulting approach, together with a trusted data partner that can help them bridge the adoption gap, will be the ones to unlock faster insights, reduce risk, and open new revenue streams.
With the right strategic roadmap, data leaders don’t have to choose between building a trusted data foundation and delivering cutting-edge AI. They can do both, and they can do it all at the same time.
Ready to get started? Talk to one of our data and AI experts today.
