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Technology Strategy

Get the flexibility and value you need from technology

Why technology strategy matters.

the revenue growth when leaders double down on investments in technology and innovation

of CIO/CTOs are primarily focusing investment on revenue growth as opposed to cutting costs

of CIOs are focusing on an overall business transformation, versus a single function, in 2024

of enterprise transformation projects fail to meet expectations

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Start with technology, then reinvent

Use everything technology offers to build a better business.

What you need to do

Get out of tech debt and into tech value.

Curb your tech debt and focus your tech spending on the activities that will power your business growth. Give your management team a shared understanding of how tech can deliver more value.

Design an enterprise architecture that allows your business to soar

Make the most of digital core technologies and techniques to improve business continuity and reduce your risk. Reinvent every aspect of your IT using generative AI.

Build an operating model that’s as innovative as your team

Respond to changing market demands by being nimbler. Design and implement an intelligent operating model built for business agility, resiliency and growth.

Have a clear vision for your tech transformation

Prioritize what you want to achieve, set the success criteria, and establish a transformation office to deliver it.

What you’ll achieve

Cloud innovation

A tech strategy everyone supports

Create a coalition for change with a plan that also fires up your business, tech and finance teams.


Total transparency on where your tech spend is going

With a clear view, you can decide where to reduce, redistribute and expand your tech investments.

Cloud infrastructure

A vision for your future architecture

Get the outcomes your business needs while continuously transforming your organization at scale.

Cloud technology

An operating model that is your competitive edge

Get an intelligent operating model that moves at the same pace as your customers.

Cloud transformation

A transformation that meets expectations

Track and communicate the value of your enterprise transformation and get insights to help you make better decisions.

What’s trending in technology strategy

journey technology solutions

This is a singular moment for CIOs: here is how they can take advantage to unlock true business value across their enterprise.

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How do you simplify a complex enterprise transformation; Accenture’s Jason Sain recommends starting with a clear vision and value creation story.

journey technology solutions

By focusing on new opportunities provided by cloud, data and AI, CSPs can accelerate their legacy technology transformation to resolve tech debt and position themselves for new product and service growth. 

journey technology solutions

CIOs can bring the greatest value to transformation. Accenture’s Greg Douglass explains how CIOs address new challenges in today’s business.

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If Agile is challenging, meet multi-speed; a model combining the best of both worlds to gain agility.

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Accenture’s Kit Friend explains what’s at risk when CIOs mistake a hybrid solution for Agile, and why multi-speed isn’t always the answer.

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Five imperatives the C-suite must address to reinvent in the age of generative AI.

Greg Douglass from Accenture advises how to boost a tech-savvy board as embracing digital transformation requires tech expertise across enterprises.

Accelerate your journey

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Assess your business, talent, and IT maturity to understand your strengths and gaps. Unlock opportunities over a data-driven path to hastened growth and value.

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Orchestrate large-scale business transformations from start to finish, focusing on vision, value, speed, talent and technology.

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Our leaders

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Koenraad Schelfaut

Lead – Technology Strategy & Advisory

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Keith Boone

Lead – Technology Strategy & Advisory, North America

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Lead – Technology Strategy & Advisory, EMEA

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Lead – Technology Strategy & Advisory, Growth Markets

More From Forbes

Synopsys announces new ai-driven solutions.

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Synopsys CEO Sassine Ghazi takes the stage for the first time as CEO at SNUG, joined by Nvidia CEO Jensen Huang to discuss the companies’ collaboration and much more.

I never knew how close Synopsys and Nvidia were! Apparently, according to Jensen, Synopsys delivered boxes and boxes of manuals (yes, they used to be printed in massive tomes, if you recall!) to Nvidia’s corporate headquarters (just a one-room office) when the GPU company was just a baby. The startup would go under if the next chip came back with errors and required another tape-out, and they needed Synopsys simulation and design tools to make that remotely possible. The rest, as they say, is history.

Silicon Complexity: The journey to a trillion transistors and One Angstrom

Jensen and Sassine opened the conference talking about the challenges facing chip designers, from chip complexity to chiplets to the intersection of systems design and silicon design. The slide below summarizes what they have accomplished together, producing a 10-15 fold improvement across the chip design workflow and now moving into generative AI and systems software.

The Nvidia / Synopsys collaboration has produced massive gains in productivity and performance

Synopsys has already significantly improved design capture with DSO.AI, its first AI-enhanced design platform, and has broadened its use of reinforcement learning into validation, test, and analog space designs. These AI-driven tools have been used in hundreds of customer tape-outs and have yielded more than 10% boost in performance, power, area (PPA), up to 10X faster turn-around time, double-digit improvements in verification coverage, 4X faster test with the same coverage, and 4x faster analog circuit optimization when compared to optimization without the use of AI. They have also added AI to their data analytics platforms including Design.da, Silicon.da, and Fab.da to produce better PPA and higher manufacturing yields.

Synopsys has also built, a generative AI solution that helps more junior members of a chip design team get answers to questions about the myriad of AI tools and cloud service offerings. Note that Synopsys does not call something “generative AI”, unless it is built using a large language model. The .ai family, for example, is built using reinforcement learning, an older technique that remains the state-of-the-art when it comes to solving a game-theory problem like place and route.

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Synopsys has embedded AI across it design / verification / test portfolio

Now the company is applying AI to 3D chip design. 3D design brings in all sorts of thermal design challenges, and the new Platform Architect - Multi-Die and can boost productivity by as much as 10-fold, and helps resolve thermal challenges with a native thermal analysis solver in just hours, not days.

Synopsys announced new optimization and thermal analysis tools for Chiplet-based design

Sassine also introduced a new piece of hardware, the ZeBu EP2 emulation and prototyping platform that can emulate chips up to 5.6B gates in size, previously unheard-of scale. The new ZeBu emulator is for software bring-up, software/hardware validation, and power/performance analysis. Nobody designs a chip today with emulating the logic and layout; the designs are too complex. It is now rare to hear of a chip that required more that one or two tape-outs, a feat unheard of a decade ago.

Synopsys new ZeBu EP2 solution for hardware-assisted verification and prototyping

Staying in the Hardware Assisted Verification (HAV) space, Synopsys also launched the HAPS®-100 12 system, Synopsys’ highest capacity and density FPGA-based prototyping system, with a mix of fixed and flexible interconnects, particularly useful for prototyping big designs that require many FPGAs, such as multi-die systems and large SoCs. Available now, this new prototyping system shares a common hardware platform with Synopsys ZeBu EP2.

Hyperconvergence: the next EDA frontier

Finally, Sassine touched on the concept of hyperconvergence. The idea is that there remains a lot of optimization improvement that lies at the intersection of the various layers of the EDA stack. By applying AI to this level of abstraction and models, these hidden PPA opportunities can be uncovered and addressed. AI at this level combines in a tightly coupled system. AI can search the larger space across these layers and free the “trapped” PPA for further optimization. While too early to quantify the benefits of hyperconvergence AI, Synopsys has multiple customer already testing the concept and models, and we hope to see some results soon.


Synopsys believes they are a journey that will increasingly automate rote engineering work and improve the performance, power, and cost of designing and fabricating the complex chips. Talented, experience design engineers will be able to focus on the work only they can do, which increasingly operating at the architectural design and software levels. Perhaps 20% of “simple” chips, or chips being redesigned for a new manufacturing process node, could be designed entirely by AI under the supervision of a few engineers. And software platforms like Nvidia NIMs for inference processing will continue to increase the optionality designers seek.

Its a great time to be a chip designer!

Disclosures : This article expresses the author's opinions and

should not be taken as advice to purchase from or invest in the companies mentioned. Cambrian AI Research is fortunate to have many, if not most, semiconductor firms as our clients, including Blaize, BrainChip, Cadence Design, Cerebras, D-Matrix, Eliyan, Esperanto, GML, Groq, IBM, Intel, Mythic, NVIDIA, Qualcomm Technologies, Si-Five,, Synopsys, Ventana Microsystems, Tenstorrent and scores of investment clients. We have no investment positions in any of the companies mentioned in this article and do not plan to initiate any in the near future. For more information, please visit our website at .

Karl Freund

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