Enterprise AI in Practice and How Leading Firms Move from Strategy to Production

This article is sponsored by HTEC and was written, edited, and printed in alignment with our Emerj sponsored content guidelines. Learn extra about our thought management and content material creation providers on our Emerj Media Services page.

Operating fashions, workflows, and software program growth processes have been by no means designed for AI — ensuing in stalled initiatives, misplaced momentum, and an incapacity to ship measurable enterprise worth.

Research from RAND found that 84 p.c of enterprise leaders consider AI will considerably impression their group — but solely 14 p.c report being totally prepared to combine it, and greater than 80 p.c of AI initiatives fail at twice the speed of comparable non-AI expertise initiatives. Stanford HAI’s 2025 AI Index confirmed that whereas organizational use of generative AI greater than doubled in a single 12 months, most firms that report any monetary impression from AI estimate these advantages at low ranges. The result’s stalled initiatives, misplaced momentum, and an incapacity to ship measurable enterprise worth.

Emerj featured three leaders from HTEC on the AI in Business Podcast — Lawrence Whittle, Chief Strategy Officer; Ronny Fehling, Chief AI Transformation Officer; and Tim Sears, Chief AI Officer — in a sequence inspecting how enterprises can transfer AI from remoted wins to repeatable, enterprise‑seen impression.

This article examines 4 insights that make clear the circumstances underneath which enterprise AI produces actual, repeatable enterprise outcomes fairly than remoted pockets of progress:

• End‑to‑finish workflows as the actual unit of AI worth: AI solely produces measurable ROI when groups construct linked workflow sequences as an alternative of remoted pilots, enabling momentum, repeatability, and enterprise‑seen impression.
• AI constructed contained in the dwell workflow: The first AI slice should run in the actual system of report — slim sufficient to end in six to twelve weeks and necessary sufficient that customers really feel ache if it disappears — as a result of solely in‑workflow usefulness creates the pull and operational proof required to scale.
• Scaling AI from solo customers to groups: Apply AI to shared workflows — the steps groups depend on collectively — so supply accelerates throughout the entire group as an alternative of manufacturing scattered particular person enhancements.
• Enterprise AI succeeds when the work adjustments, not simply the instruments: The actual constraint isn’t mannequin functionality — it’s how groups select, construct, sequence, and ship the work that AI touches.

End‑to‑End Workflows because the Real Unit of AI Value

Episode 1:  What Enterprise AI Looks Like When It’s Real – with Lawrence Whittle of HTEC Group

Guest: Lawrence Whittle, Chief Strategy Officer at HTEC Group.

Expertise: Digital Transformation, AI Strategy, Data & Analytics, Go-to-Market Strategy

Brief Recognition: Lawrence Whittle is a seasoned expertise government with management expertise throughout AI, information platforms, and enterprise software program organizations. He at the moment serves as Chief Strategy Officer at HTEC, following government roles together with President and Chief Commercial Officer at Verana Health, the place he helped scale a real-world information platform targeted on healthcare insights, and CEO of Parsable, a linked employee platform acquired in 2024. Previously, he served as Chief Revenue Officer at Persado, an AI firm targeted on language intelligence, the place he helped drive progress by way of enterprise adoption throughout monetary providers, healthcare, telecommunications, and retail. Lawrence has additionally been concerned as an investor and advisor to expertise firms, with expertise spanning a number of IPOs and M&A transactions.

Lawrence Whittle makes a transparent distinction amongst customers, use circumstances, and finish‑to‑finish workflows, explaining why solely the third produces measurable ROI. His perspective is grounded in what he has seen throughout world shoppers: pilots succeed technically however fail commercially as a result of they by no means have interaction the complete enterprise sequence in which worth is created and measured.

He explains that organizations spent 2024–2025 validating ideas fairly than validating worth. Pilots ticked technically, however they have been scoped round slim use circumstances that couldn’t display enterprise impression. Leaders might see exercise, however they couldn’t see return. Whittle argues that enterprises unlock momentum when AI is deployed throughout the precise workflow sequence — the chain of steps the place value, velocity, and conversion metrics dwell.

He frames the distinction between experimentation and enterprise‑degree worth creation:

“There’s a giant distinction between a person, a use case, and an finish‑to‑finish use case. A person is simply a person experimenting with instruments, and a use case is a small slice of a enterprise course of. Neither of these creates measurable enterprise impression. You solely see actual ROI when AI spans the complete workflow — the sequence of steps that allows you to say, ‘I spent X and bought Y again,’ whether or not that return reveals up as increased conversions, decrease prices, or quicker velocity throughout a number of groups.”

— Lawrence Whittle, Chief Strategy Officer at HTEC Group

Workflow sequencing — not instrument choice, not pilot quantity — is the actual unit of worth. Evaluating AI throughout the complete workflow sequence makes the return seen, as a result of worth accumulates throughout steps fairly than inside remoted duties. When AI is deployed at that degree, even small initiatives present clear trigger‑and‑impact, creating the momentum that accelerates adoption throughout the enterprise.

AI Built Inside the Live workflow

Episode 2:  Fixing the Pilot‑to‑Production Gap in Enterprise AI – with Ronny Fehling of HTEC

Guest: Ronny Fehling, Chief AI Transformation Officer at HTEC

Expertise: Generative AI Strategy, AI Transformation, Enterprise AI Implementation, Digital Strategy

Brief Recognition: Ronny Fehling is a expertise and AI transformation chief with greater than 20 years of expertise spanning software program engineering, digital transformation, and enterprise AI. He at the moment serves as Chief AI Transformation Officer at HTEC, the place he focuses on scaling production-grade AI capabilities throughout working fashions, engineering groups, and shopper options. Previously, Ronny was Partner and Vice President of Generative Artificial Intelligence at BCG X, the place he labored with Fortune 500 organizations on AI technique and implementation, constructed AI options delivering measurable enterprise worth, and led world groups of AI scientists, engineers, and strategists. He additionally based and scaled Spend AI, a patented AI answer that delivered important cost-optimization worth for enterprise shoppers. Ronny holds a Master’s diploma in Computer Science from the University of Freiburg with a specialization in Artificial Intelligence and accomplished research in Computer Science, Mathematics, Robotics, and associated fields at MIT.

​

AI scales when the primary slice runs inside the actual workflow, the place actual customers, actual techniques, and actual constraints drive it to show its worth.

Ronny emphasizes that almost all pilots fail as a result of they’re constructed subsequent to actuality fairly than inside it, which signifies that the second manufacturing begins, every thing the pilot prevented turns into the work.

He defines the primary manufacturing slice as a intentionally slim, bounded step inside an actual workflow — sufficiently small to end in six to twelve weeks, however actual sufficient that customers really feel the impression instantly.

When that slice is constructed outdoors actuality, all of the work that was intentionally unnoticed hits without delay: the techniques it has to combine with, the validation and certification it should go, the governance and monitoring it should fulfill, and the way in which actual customers truly behave. What appeared like momentum turns into slowdown — not as a result of the pilot was flawed, however as a result of it by no means touched the operational actuality it was meant to survive.

Ronny’s manufacturing instance reveals the other sample. His crew constructed a really small system for blue‑collar operators dealing with non‑high quality occasions in a regulated setting — a workflow that routinely brought about delays and blame. The slice sat evenly built-in into their present system, required no new studying, and eliminated a supply of day by day friction.

In this case, operators beforehand dealt with non‑high quality occasions by way of guide reporting and escalation steps, which slowed decision and created ambiguity about accountability. The AI system inserted immediately into that workflow diminished the time required to determine and resolve points, eliminating redundant steps with out requiring customers to change techniques or retrain.

“Their work simply bought simpler,” and that usefulness created the pull enterprises wrestle to manufacture: actual customers who would really feel ache if the system have been taken away.

Ronny attracts a pointy line between experimentation and enterprise‑degree worth:

“Adoption is a symptom of a helpful first slice, not one thing you engineer individually. The work you do has to take away the ache from the folks doing it. If it doesn’t, no change program will prevent.’

— Ronny Fehling, Chief AI Transformation Officer at HTEC

From his perspective, a primary slice is prepared to scale when it meets 4 circumstances:

• Removes actual ache — Operators really feel the distinction. If the slice doesn’t remove friction, “no change program will prevent.”
• Lives in the system of report — It runs contained in the precise workflow, not in a parallel setting constructed for experimentation.
• Matters sufficient that somebody cares — It touches a step that reveals up in the P&L or in operational accountability.
• Is painful to take away — Users generate pull as a result of the slice makes their work meaningfully simpler.

When the primary slice runs contained in the dwell workflow — with actual customers, actual information, and actual constraints — it produces the one sign Ronny trusts: proof that the system can survive contained in the setting that should finally carry it.

Evaluate whether or not an AI initiative is prepared for scaling by making use of a easy set of manufacturing standards:

• It runs inside a system of report, not a take a look at setting.
• It removes a clearly outlined operational ache level.
• It is delivered inside a 6–12 week timeframe
• Its removing would create speedy workflow regression

When these circumstances are met, AI strikes from theoretical worth to operational proof, creating the interior pull required for broader deployment.

​

Scaling AI from Solo Users to Teams

Episode 4:  How AI Is Reshaping the Way Enterprises Build Software – With Tim Sears of HTEC

​Guest: Tim Sears, Chief AI Officer at HTEC

Expertise: Artificial Intelligence, Machine Learning, AI Strategy, Data Science & Engineering

Brief Recognition: Tim Sears is an AI and expertise chief with a background spanning machine studying, information science, finance, and entrepreneurship. He at the moment serves as Chief AI Officer at HTEC, the place he focuses on serving to organizations apply AI strategically and operationally. Previously, Tim led Software Applications at Groq, the place he directed engineering groups constructing software program and instruments round Groq’s AI inference expertise, contributing to developments in AI workload efficiency and scalability. Before Groq, he constructed and managed Target’s Data Science & Engineering group, making use of information and AI capabilities to enhance enterprise outcomes at scale. Tim has additionally suggested organizations, together with Bain & Company’s Advanced Analytics Group, and holds a Ph.D. in Computer Science and Machine Learning from The Australian National University, with analysis targeted on machine studying mannequin constructions.

AI delivers significant enterprise impression when it accelerates the way in which groups work collectively, not simply when people experiment on their very own. Tim is specific that right this moment’s productiveness positive aspects are uneven as a result of they depend upon who’s personally enthusiastic about AI, who has discovered the instruments, and who’s prepared to experiment. That creates scattered particular person enhancements — useful, however not transformative.

The shift he describes is that AI should turn into a catalyst for teamwork. In software program engineering, meaning transferring from remoted chats and private boosts to shared workflows the place the entire crew can iterate constantly.

Tim argues that the actual breakthrough comes when AI allows non‑technical crew members to put their fingerprints on a mission, when groups share context and artifacts by way of AI, and when iteration occurs each time the AI could make a significant change — not solely throughout scheduled sprints.

From this angle, scaling AI means designing for crew‑degree velocity:

• shared workflows
• shared context
• shared iteration
• and shared positive aspects.

When AI is utilized to the steps groups depend on collectively, supply accelerates throughout the entire group as an alternative of manufacturing scattered particular person enhancements.​

Enterprise AI Succeeds When the Work Changes, Not Just the Tools

Lawrence highlights that many enterprises nonetheless run AI inside working patterns designed for a slower, pre‑AI setting. Long necessities cycles, intensive planning phases, and inflexible workflows have been constructed for a context the place engineering time was scarce, and iteration was costly. Those inherited constructions now restrict how rapidly organizations can convert AI functionality into measurable enterprise worth.

From his perspective, the constraint isn’t the expertise itself however how work is organized. Accelerating present processes isn’t adequate—organizations should cut back the space between idea and execution and shift from planning-heavy fashions towards steady manufacturing and studying.

Ronny factors to a associated constraint on the organizational degree. He observes that enterprises typically try to outline governance frameworks, information platforms, and working fashions earlier than something has been confirmed in manufacturing. In observe, this sequencing introduces early friction, slowing iteration and limiting the power to validate AI techniques underneath real-world circumstances.

From his expertise, this leads to recurring failure patterns:

• Premature governance earlier than manufacturing proof exists.
• Platform and information investments made with out validated use.
• Mature software program practices utilized to early-stage AI work.
• Operating fashions designed earlier than any actual utilization sign.

His emphasis is on sequencing: worth should be demonstrated in a bounded, production-level context first, with construction forming round what works fairly than being designed in advance.

Tim expands this sample into how groups function and the place productiveness positive aspects truly happen. While people can use AI instruments to enhance their very own output, these positive aspects stay uneven till workflows are shared and coordinated on the crew degree:

“Today, we see a variety of consolation and ability in phrases of utilizing AI instruments and becoming in new methods of working. It’s having an amazing impression on people. But software program growth, enterprise in common, is a crew sport. AI helps sure gamers do efficiency‑enhancing issues, however the teamwork facet is what’s going to decide actual impression. When we determine methods for groups to share context and iterate along with AI, that’s once we’re going to see dramatic productiveness positive aspects. Until then, these positive aspects will stay uneven and tied to particular person adoption.”

— Tim Sears, Chief AI Officer at HTEC

This distinction reinforces a constant sample throughout all three leaders: AI doesn’t ship significant enterprise impression when layered onto present workflows unchanged. Value emerges when workflows, sequencing, and crew coordination adapt to how AI truly operates in observe—enabling steady iteration, shared context, and quicker motion from idea to manufacturing.

Rather than counting on giant, upfront ROI justification, groups shift analysis towards cumulative proof. Multiple smaller deployments—every enhancing effectivity, throughput, or workflow friction—create clearer alerts of worth and enable organizations to construct momentum whereas figuring out the place AI produces the strongest return.